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  • 1.
    Abiri, Najmeh
    et al.
    Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
    Linse, Björn
    Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
    Edén, Patrik
    Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
    Establishing strong imputation performance of a denoising autoencoder in a wide range of missing data problems2019Inngår i: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 65, s. 137-146Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Dealing with missing data in data analysis is inevitable. Although powerful imputation methods that address this problem exist, there is still much room for improvement. In this study, we examined single imputation based on deep autoencoders, motivated by the apparent success of deep learning to efficiently extract useful dataset features. We have developed a consistent framework for both training and imputation. Moreover, we benchmarked the results against state-of-the-art imputation methods on different data sizes and characteristics. The work was not limited to the one-type variable dataset; we also imputed missing data with multi-type variables, e.g., a combination of binary, categorical, and continuous attributes. To evaluate the imputation methods, we randomly corrupted the complete data, with varying degrees of corruption, and then compared the imputed and original values. In all experiments, the developed autoencoder obtained the smallest error for all ranges of initial data corruption. © 2019 Elsevier B.V.

  • 2.
    Alabdallah, Abdallah
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Jakubowski, Jakub
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Bobek, Szymon
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Nalepa, Grzegorz J.
    Understanding Survival Models through Counterfactual ExplanationsManuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    The development of black-box survival models has created a need for methods that explain their outputs, just as in the case of traditional machine learning methods. Survival models usually predict functions rather than point estimates. This special nature of their output makes it more difficult to explain their operation. We propose a method to generate plausible counterfactual explanations for survival models. The method supports two options that handle the special nature of survival models' output. One option relies on the Survival Scores, which are based on the area under the survival function, which is more suitable for proportional hazard models. The other one relies on Survival Patterns in the predictions of the survival model, which represent groups that are significantly different from the survival perspective. This guarantees an intuitive well-defined change from one risk group (Survival Pattern) to another and can handle more realistic cases where the proportional hazard assumption does not hold. The method uses a Particle Swarm Optimization algorithm to optimize a loss function to achieve four objectives: the desired change in the target, proximity to the explained example, likelihood, and the actionability of the counterfactual example. Two predictive maintenance datasets and one medical dataset are used to illustrate the results in different settings. The results show that our method produces plausible counterfactuals, which increase the understanding of black-box survival models.

  • 3.
    Alabdallah, Abdallah
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi. Lund University, Lund, Sweden.
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi. RISE Research Institutes of Sweden, Stockholm, Sweden.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi.
    The Concordance Index Decomposition: A Measure for a Deeper Understanding of Survival Prediction Models2024Inngår i: Artificial Intelligence in Medicine, ISSN 0933-3657, E-ISSN 1873-2860, Vol. 148, s. 1-10, artikkel-id 102781Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. This paper proposes a decomposition of the C-index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition enables a more fine-grained analysis of the strengths and weaknesses of survival prediction methods. The usefulness of this decomposition is demonstrated through benchmark comparisons against state-of-the-art and classical models, together with a new variational generative neural-network-based method (SurVED), which is also proposed in this paper. Performance is assessed using four publicly available datasets with varying levels of censoring. The analysis using the C-index decomposition and synthetic censoring shows that deep learning models utilize the observed events more effectively than other models, allowing them to keep a stable C-index in different censoring levels. In contrast, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events. © 2024 The Author(s)

  • 4.
    Alabdallah, Abdallah
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR). RISE Research Institutes of Sweden.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR). Lund University, Lund, Sweden.
    SurvSHAP: A Proxy-Based Algorithm for Explaining Survival Models with SHAP2022Inngår i: 2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA) / [ed] Joshua Zhexue Huang; Yi Pan; Barbara Hammer; Muhammad Khurram Khan; Xing Xie; Laizhong Cui; Yulin He, Piscataway, NJ: IEEE, 2022Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Survival Analysis models usually output functions (survival or hazard functions) rather than point predictions like regression and classification models. This makes the explanations of such models a challenging task, especially using the Shapley values. We propose SurvSHAP, a new model-agnostic algorithm to explain survival models that predict survival curves. The algorithm is based on discovering patterns in the predicted survival curves, the output of the survival model, that would identify significantly different survival behaviors, and utilizing a proxy model and SHAP method to explain these distinct survival behaviors. Experiments on synthetic and real datasets demonstrate that the SurvSHAP is able to capture the underlying factors of the survival patterns. Moreover, SurvSHAP results on the Cox Proportional Hazard model are compared with the weights of the model to show that we provide faithful overall explanations, with more fine-grained explanations of the sub-populations. We also illustrate the wrong model and explanations learned by a Cox model when applied to heterogeneous sub-populations. We show that a non-linear machine learning survival model with SurvSHAP can better model the data and provide better explanations than linear models.

  • 5.
    Alabdallah, Abdallah
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Fan, Yuantao
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Discovering Premature Replacements in Predictive Maintenance Time-to-Event Data2023Inngår i: Proceedings of the Asia Pacific Conference of the PHM Society 2023 / [ed] Takehisa Yairi; Samir Khan; Seiji Tsutsumi, New York: The Prognostics and Health Management Society , 2023, Vol. 4Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Time-To-Event (TTE) modeling using survival analysis in industrial settings faces the challenge of premature replacements of machine components, which leads to bias and errors in survival prediction. Typically, TTE survival data contains information about components and if they had failed or not up to a certain time. For failed components, the time is noted, and a failure is referred to as an event. A component that has not failed is denoted as censored. In industrial settings, in contrast to medical settings, there can be considerable uncertainty in an event; a component can be replaced before it fails to prevent operation stops or because maintenance staff believe that the component is faulty. This shows up as “no fault found” in warranty studies, where a significant proportion of replaced components may appear fault-free when tested or inspected after replacement.

    In this work, we propose an expectation-maximization-like method for discovering such premature replacements in survival data. The method is a two-phase iterative algorithm employing a genetic algorithm in the maximization phase to learn better event assignments on a validation set. The learned labels through iterations are accumulated and averaged to be used to initialize the following expectation phase. The assumption is that the more often the event is selected, the more likely it is to be an actual failure and not a “no fault found”.

    Experiments on synthesized and simulated data show that the proposed method can correctly detect a significant percentage of premature replacement cases.

    Fulltekst (pdf)
    fulltext
  • 6.
    Altarabichi, Mohammed Ghaith
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Alabdallah, Abdallah
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Pashami, Sepideh
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Improving Concordance Index in Regression-based Survival Analysis: Discovery of Loss Function for Neural Networks2024Manuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    In this work, we use an Evolutionary Algorithm (EA) to discover a novel Neural Network (NN) regression-based survival loss function with the aim of improving the C-index performance. Our contribution is threefold; firstly, we propose an evolutionary meta-learning algorithm SAGA$_{loss}$ for optimizing a neural-network regression-based loss function that maximizes the C-index; our algorithm consistently discovers specialized loss functions that outperform MSCE. Secondly, based on our analysis of the evolutionary search results, we highlight a non-intuitive insight that signifies the importance of the non-zero gradient for the censored cases part of the loss function, a property that is shown to be useful in improving concordance. Finally, based on this insight, we propose MSCE$_{Sp}$, a novel survival regression loss function that can be used off-the-shelf and generally performs better than the Mean Squared Error for censored cases. We performed extensive experiments on 19 benchmark datasets to validate our findings.

  • 7.
    Amirahmadi, Ali
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi. Department of Astronomy and Theoretical Physics, Lund, Sweden.
    Etminani, Kobra
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Deep learning prediction models based on EHR trajectories: A systematic review2023Inngår i: Journal of Biomedical Informatics, ISSN 1532-0464, E-ISSN 1532-0480, Vol. 144, artikkel-id 104430Artikkel, forskningsoversikt (Fagfellevurdert)
    Abstract [en]

    Background: : Electronic health records (EHRs) are generated at an ever-increasing rate. EHR trajectories, the temporal aspect of health records, facilitate predicting patients’ future health-related risks. It enables healthcare systems to increase the quality of care through early identification and primary prevention. Deep learning techniques have shown great capacity for analyzing complex data and have been successful for prediction tasks using complex EHR trajectories. This systematic review aims to analyze recent studies to identify challenges, knowledge gaps, and ongoing research directions. Methods: For this systematic review, we searched Scopus, PubMed, IEEE Xplore, and ACM databases from Jan 2016 to April 2022 using search terms centered around EHR, deep learning, and trajectories. Then the selected papers were analyzed according to publication characteristics, objectives, and their solutions regarding existing challenges, such as the model's capacity to deal with intricate data dependencies, data insufficiency, and explainability. Results: : After removing duplicates and out-of-scope papers, 63 papers were selected, which showed rapid growth in the number of research in recent years. Predicting all diseases in the next visit and the onset of cardiovascular diseases were the most common targets. Different contextual and non-contextual representation learning methods are employed to retrieve important information from the sequence of EHR trajectories. Recurrent neural networks and the time-aware attention mechanism for modeling long-term dependencies, self-attentions, convolutional neural networks, graphs for representing inner visit relations, and attention scores for explainability were frequently used among the reviewed publications. Conclusions: This systematic review demonstrated how recent breakthroughs in deep learning methods have facilitated the modeling of EHR trajectories. Research on improving the ability of graph neural networks, attention mechanisms, and cross-modal learning to analyze intricate dependencies among EHRs has shown good progress. There is a need to increase the number of publicly available EHR trajectory datasets to allow for easier comparison among different models. Also, very few developed models can handle all aspects of EHR trajectory data. © 2023 The Author(s)

  • 8.
    Amirahmadi, Ali
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi. Lund University, Lund, Sweden.
    Etminani, Kobra
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Melander, Olle
    Lund University, Lund, Sweden.
    Björk, Jonas
    Lund University, Lund, Sweden.
    A Masked Language Model for Multi-Source EHR Trajectories Contextual Representation Learning2023Inngår i: Caring is Sharing – Exploiting the Value in Data for Health and Innovation: Proceedings of MIE 2023 / [ed] Maria Hägglund; Madeleine Blusi; Stefano Bonacina; Lina Nilsson; Inge Cort Madsen; Sylvia Pelayo; Anne Moen; Arriel Benis; Lars Lindsköld; Parisis Gallos, Amsterdam: IOS Press, 2023, Vol. 302, s. 609-610Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Using electronic health records data and machine learning to guide future decisions needs to address challenges, including 1) long/short-term dependencies and 2) interactions between diseases and interventions. Bidirectional transformers have effectively addressed the first challenge. Here we tackled the latter challenge by masking one source (e.g., ICD10 codes) and training the transformer to predict it using other sources (e.g., ATC codes). © 2023 European Federation for Medical Informatics (EFMI) and IOS Press.

  • 9.
    Atabaki-Pasdar, Naeimeh
    et al.
    Department of Clinical Sciences, Lund University, Malmö, Sweden.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
    Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts2020Inngår i: PLoS Medicine, ISSN 1549-1277, E-ISSN 1549-1676, Vol. 17, nr 6, artikkel-id e1003149Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. Methods and findings We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (<5% or ≥5%) available for 1,514 participants. We applied LASSO (least absolute shrinkage and selection operator) to select features from the different layers of omics data and random forest analysis to develop the models. The prediction models included clinical and omics variables separately or in combination. A model including all omics and clinical variables yielded a cross-validated receiver operating characteristic area under the curve (ROCAUC) of 0.84 (95% CI 0.82, 0.86; p < 0.001), which compared with a ROCAUC of 0.82 (95% CI 0.81, 0.83; p < 0.001) for a model including 9 clinically accessible variables. The IMI DIRECT prediction models outperformed existing noninvasive NAFLD prediction tools. One limitation is that these analyses were performed in adults of European ancestry residing in northern Europe, and it is unknown how well these findings will translate to people of other ancestries and exposed to environmental risk factors that differ from those of the present cohort. Another key limitation of this study is that the prediction was done on a binary outcome of liver fat quantity (<5% or ≥5%) rather than a continuous one. Conclusions In this study, we developed several models with different combinations of clinical and omics data and identified biological features that appear to be associated with liver fat accumulation. In general, the clinical variables showed better prediction ability than the complex omics variables. However, the combination of omics and clinical variables yielded the highest accuracy. We have incorporated the developed clinical models into a web interface (see: https://www.predictliverfat.org/) and made it available to the community. © This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

  • 10.
    Björkelund, Anders
    et al.
    Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
    Ohlsson, Mattias
    Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
    Lundager Forberg, Jakob
    Department of Cardiology, Skåne University Hospital, Lund, Sweden.
    Mokhtari, Arash
    Department of Cardiology, Skåne University Hospital, Lund, Sweden; Department of Clinical Sciences at Lund, Lund University, Lund, Sweden.
    Olsson de Capretz, Pontus
    Department of Clinical Sciences at Lund, Lund University, Lund, Sweden; Department of Emergency Medicine, Skåne University Hospital, Lund, Sweden.
    Ekelund, Ulf
    Department of Clinical Sciences at Lund, Lund University, Lund, Sweden; Department of Emergency Medicine, Skåne University Hospital, Lund, Sweden.
    Björk, Jonas
    Division of Occupational and Environmental Medicine, Lund University, Lund, Sweden; Clinical Studies Sweden, Forum South, Skåne University Hospital, Lund, Sweden.
    Machine learning compared with rule‐in/rule‐out algorithms and logistic regression to predict acute myocardial infarction based on troponin T concentrations2021Inngår i: Journal of the American College of Emergency Physicians Open, E-ISSN 2688-1152, Vol. 2, nr 2, artikkel-id e12363Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Abstract

    ObjectiveComputerized decision-support tools may improve diagnosis of acute myocardial infarction (AMI) among patients presenting with chest pain at the emergency department (ED). The primary aim was to assess the predictive accuracy of machine learning algorithms based on paired high-sensitivity cardiac troponin T (hs-cTnT) concentrations with varying sampling times, age, and sex in order to rule in or out AMI.

    Methods

    In this register-based, cross-sectional diagnostic study conducted retrospectively based on 5695 chest pain patients at 2 hospitals in Sweden 2013–2014 we used 5-fold cross-validation 200 times in order to compare the performance of an artificial neural network (ANN) with European guideline-recommended 0/1- and 0/3-hour algorithms for hs-cTnT and with logistic regression without interaction terms. Primary outcome was the size of the intermediate risk group where AMI could not be ruled in or out, while holding the sensitivity (rule-out) and specificity (rule-in) constant across models.

    Results

    ANN and logistic regression had similar (95%) areas under the receiver operating characteristics curve. In patients (n = 4171) where the timing requirements (0/1 or 0/3 hour) for the sampling were met, using ANN led to a relative decrease of 9.2% (95% confidence interval 4.4% to 13.8%; from 24.5% to 22.2% of all tested patients) in the size of the intermediate group compared to the recommended algorithms. By contrast, using logistic regression did not substantially decrease the size of the intermediate group.

    Conclusion

    Machine learning algorithms allow for flexibility in sampling and have the potential to improve risk assessment among chest pain patients at the ED.

  • 11.
    David, Jennifer
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Söderberg, Bo
    Lund University, Lund, Sweden.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi. Lund University, Lund, Sweden.
    Deterministic annealing with Potts neurons for multi-robot routing2022Inngår i: Intelligent Service Robotics, ISSN 1861-2776, Vol. 15, nr 3, s. 321-334Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    A deterministic annealing (DA) method is presented for solving the multi-robot routing problem with min–max objective. This is an NP-hard problem belonging to the multi-robot task allocation set of problems where robots are assigned to a group of sequentially ordered tasks such that the cost of the slowest robot is minimized. The problem is first formulated in a matrix form where the optimal solution of the problem is the minimum-cost permutation matrix without any loops. The solution matrix is then found using the DA method is based on mean field theory applied to a Potts spin model which has been proven to yield near-optimal results for NP-hard problems. Our method is bench-marked against simulated annealing and a heuristic search method. The results show that the proposed method is promising for small-medium sized problems in terms of computation time and solution quality compared to the other two methods. © The Author(s) 2022

  • 12.
    Davidge, J.
    et al.
    Lund University, Lund, Sweden.
    Ashfaq, Awais
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Oedegaard, K.
    Novartis Norway, Oslo, Norway.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Costa-Scharplatz, M.
    Novartis Sweden AB, Täby, Sweden.
    Agvall, B.
    Region Halland, Halmstad, Sweden.
    Heart Failure Mortality in a Community-Based Population Using a Novel Algorithm for Extraction of Ejection Fraction2022Inngår i: Value in Health, ISSN 1098-3015, E-ISSN 1524-4733, Vol. 25, nr 1, Suppl., s. S165-S165Artikkel i tidsskrift (Fagfellevurdert)
  • 13.
    Davidge, Jason
    et al.
    Center for Primary Health Care Research, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden; Capio Vårdcentral Halmstad, Capio AB, Halmstad, Sweden.
    Ashfaq, Awais
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Ødegaard, Kristina Malene
    University of Oslo, Oslo, Norway; Novartis Norge AS, Oslo, Norway.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Costa-Scharplatz, Madlaina
    Novartis Sverige AB, Täby, Sweden.
    Agvall, Björn
    Department of Research and Development, Region Halland, Halmstad, Sweden.
    Clinical characteristics and mortality of patients with heart failure in Southern Sweden from 2013 to 2019: a population-based cohort study2022Inngår i: BMJ Open, E-ISSN 2044-6055, Vol. 12, nr 12, artikkel-id e064997Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    OBJECTIVES: To describe clinical characteristics and prognosis related to heart failure (HF) phenotypes in a community-based population by applying a novel algorithm to obtain ejection fractions (EF) from electronic medical records. DESIGN: Retrospective population-based cohort study. SETTING: Data were collected for all patients with HF in Southwest Sweden. The region consists of three acute care hospitals, 40 inpatient wards, 2 emergency departments, 30 outpatient specialty clinics and 48 primary healthcare. PARTICIPANTS: 8902 patients had an HF diagnosis based on the International Classification of Diseases, Tenth Revision during the study period. Patients <18 years as well as patients declining to participate were excluded resulting in a study population of 8775 patients. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome measure was distribution of HF phenotypes by echocardiography. The secondary outcome measures were 1 year all-cause mortality and HR for all-cause mortality using Cox regression models. RESULTS: Out of 8775 patients with HF, 5023 (57%) had a conclusive echocardiography distributed into HF with reduced EF (35%), HF with mildly reduced EF (27%) and HF with preserved EF (38%). A total of 43% of the cohort did not have a conclusive echocardiography, and therefore no defined phenotype (HF-NDP). One-year all-cause mortality was 42% within the HF-NDP group and 30% among those with a conclusive EF. The HR of all-cause mortality in the HF-NDP group was 1.27 (95% CI 1.17 to 1.37) when compared with the confirmed EF group. There was no significant difference in survival within the HF phenotypes. CONCLUSIONS: This population-based study showed a distribution of HF phenotypes that varies from those in selected HF registries, with fewer patients with HF with reduced EF and more patients with HF with preserved EF. Furthermore, 1-year all-cause mortality was significantly higher among patients with HF who had not undergone a conclusive echocardiography at diagnosis, highlighting the importance of correct diagnostic procedure to improve treatment strategies and outcomes. © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

  • 14.
    de Capretz, Pontus Olsson
    et al.
    Skåne University Hospital, Lund, Sweden; Lund University, Lund, Sweden.
    Björkelund, Anders
    Lund University, Lund, Sweden.
    Björk, Jonas
    Lund University, Lund, Sweden; Skåne University Hospital, Lund, Sweden.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi. Lund University, Lund, Sweden.
    Mokhtari, Arash
    Skåne University Hospital, Lund, Sweden; Lund University, Lund, Sweden.
    Nyström, Axel
    Lund University, Lund, Sweden.
    Ekelund, Ulf
    Skåne University Hospital, Lund, Sweden; Lund University, Lund, Sweden.
    Machine learning for early prediction of acute myocardial infarction or death in acute chest pain patients using electrocardiogram and blood tests at presentation2023Inngår i: BMC Medical Informatics and Decision Making, E-ISSN 1472-6947, Vol. 23, nr 1, s. 1-10, artikkel-id 25Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Aims: In the present study, we aimed to evaluate the performance of machine learning (ML) models for identification of acute myocardial infarction (AMI) or death within 30 days among emergency department (ED) chest pain patients. Methods and results: Using data from 9519 consecutive ED chest pain patients, we created ML models based on logistic regression or artificial neural networks. Model inputs included sex, age, ECG and the first blood tests at patient presentation: High sensitivity TnT (hs-cTnT), glucose, creatinine, and hemoglobin. For a safe rule-out, the models were adapted to achieve a sensitivity > 99% and a negative predictive value (NPV) > 99.5% for 30-day AMI/death. For rule-in, we set the models to achieve a specificity > 90% and a positive predictive value (PPV) of > 70%. The models were also compared with the 0 h arm of the European Society of Cardiology algorithm (ESC 0 h); An initial hs-cTnT < 5 ng/L for rule-out and ≥ 52 ng/L for rule-in. A convolutional neural network was the best model and identified 55% of the patients for rule-out and 5.3% for rule-in, while maintaining the required sensitivity, specificity, NPV and PPV levels. ESC 0 h failed to reach these performance levels. Discussion: An ML model based on age, sex, ECG and blood tests at ED arrival can identify six out of ten chest pain patients for safe early rule-out or rule-in with no need for serial blood tests. Future studies should attempt to improve these ML models further, e.g. by including additional input data. © 2023, The Author(s).

  • 15.
    Ekelund, Ulf
    et al.
    Department Of Clinical Sciences Lund, Lund, Sweden.
    Ohlsson, Bodil
    University Hospital, Lund, Sweden.
    Melander, Olle
    University Hospital, Lund, Sweden.
    Björk, Jonas
    Department Of Laboratory Medicine, Lund, Sweden; University Hospital, Lund, Sweden.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi. Lund University, Lund, Sweden.
    Forberg, Jakob Lundager
    Department Of Clinical Sciences Lund, Lund, Sweden.
    de Capretz, Pontus Olsson
    Department Of Clinical Sciences Lund, Lund, Sweden.
    Nyström, Axel
    Department Of Laboratory Medicine, Lund, Sweden; Lund University, Lund, Sweden.
    Björkelund, Anders
    Lund University, Lund, Sweden.
    The skåne emergency medicine (SEM) cohort2024Inngår i: Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, E-ISSN 1757-7241, Vol. 32, nr 1, s. 1-8Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: In the European Union alone, more than 100 million people present to the emergency department (ED) each year, and this has increased steadily year-on-year by 2–3%. Better patient management decisions have the potential to reduce ED crowding, the number of diagnostic tests, the use of inpatient beds, and healthcare costs. Methods: We have established the Skåne Emergency Medicine (SEM) cohort for developing clinical decision support systems (CDSS) based on artificial intelligence or machine learning as well as traditional statistical methods. The SEM cohort consists of 325 539 unselected unique patients with 630 275 visits from January 1st, 2017 to December 31st, 2018 at eight EDs in the region Skåne in southern Sweden. Data on sociodemographics, previous diseases and current medication are available for each ED patient visit, as well as their chief complaint, test results, disposition and the outcome in the form of subsequent diagnoses, treatments, healthcare costs and mortality within a follow-up period of at least 30 days, and up to 3 years. Discussion: The SEM cohort provides a platform for CDSS research, and we welcome collaboration. In addition, SEM’s large amount of real-world patient data with almost complete short-term follow-up will allow research in epidemiology, patient management, diagnostics, prognostics, ED crowding, resource allocation, and social medicine. © The Author(s) 2024.

  • 16.
    Galozy, Alexander
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi.
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi.
    A New Bandit Setting Balancing Information from State Evolution and Corrupted ContextManuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    We propose a new sequential decision-making setting, combining key aspects of two established online learning problems with bandit feedback. The optimal action to play at any given moment is contingent on an underlying changing state which is not directly observable by the agent. Each state is associated with a context distribution, possibly corrupted, allowing the agent to identify the state. Furthermore, states evolve in a Markovian fashion, providing useful information to estimate the current state via state history. In the proposed problem setting, we tackle the challenge of deciding on which of the two sources of information the agent should base its arm selection. We present an algorithm that uses a referee to dynamically combine the policies of a contextual bandit and a multi-armed bandit. We capture the time-correlation of states through iteratively learning the action-reward transition model, allowing for efficient exploration of actions. Our setting is motivated by adaptive mobile health (mHealth) interventions. Users transition through different, time-correlated, but only partially observable internal states, determining their current needs. The side information associated with each internal state might not always be reliable, and standard approaches solely rely on the context risk of incurring high regret. Similarly, some users might exhibit weaker correlations between subsequent states, leading to approaches that solely rely on state transitions risking the same. We analyze our setting and algorithm in terms of regret lower bound and upper bounds and evaluate our method on simulated medication adherence intervention data and several real-world data sets, showing improved empirical performance compared to several popular algorithms. 

  • 17.
    Galozy, Alexander
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Corrupted Contextual Bandits with Action Order ConstraintsManuskript (preprint) (Annet vitenskapelig)
    Abstract [en]

    We consider a variant of the novel contextual bandit problem with corrupted context, which we call the contextual bandit problem with corrupted context and action correlation, where actions exhibit a relationship structure that can be exploited to guide the exploration of viable next decisions. Our setting is primarily motivated by adaptive mobile health interventions and related applications, where users might transitions through different stages requiring more targeted action selection approaches. In such settings, keeping user engagement is paramount for the success of interventions and therefore it is vital to provide relevant recommendations in a timely manner. The context provided by users might not always be informative at every decision point and standard contextual approaches to action selection will incur high regret. We propose a meta-algorithm using a referee that dynamically combines the policies of a contextual bandit and multi-armed bandit, similar to previous work, as wells as a simple correlation mechanism that captures action to action transition probabilities allowing for more efficient exploration of time-correlated actions. We evaluate empirically the performance of said algorithm on a simulation where the sequence of best actions is determined by a hidden state that evolves in a Markovian manner. We show that the proposed meta-algorithm improves upon regret in situations where the performance of both policies varies such that one is strictly superior to the other for a given time period. To demonstrate that our setting has relevant practical applicability, we evaluate our method on several real world data sets, clearly showing better empirical performance compared to a set of simple algorithms.

  • 18.
    Galozy, Alexander
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Nowaczyk, Sławomir
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Pinheiro Sant'Anna, Anita
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Ohlsson, Mattias
    Lund University, Lund, Sweden.
    Lingman, Markus
    Halland Hospital, Region Halland, Sweden & Institute of Medicine, Department of Molecular and Clinical Medicine/Cardiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Pitfalls of medication adherence approximation through EHR and pharmacy records: Definitions, data and computation2020Inngår i: International Journal of Medical Informatics, ISSN 1386-5056, E-ISSN 1872-8243, Vol. 136, artikkel-id 104092Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background and purpose: Patients’ adherence to medication is a complex, multidimensional phenomenon. Dispensation data and electronic health records are used to approximate medication-taking through refill adherence. In-depth discussions on the adverse effects of data quality and computational differences are rare. The purpose of this article is to evaluate the impact of common pitfalls when computing medication adherence using electronic health records.

    Procedures: We point out common pitfalls associated with the data and operationalization of adherence measures. We provide operational definitions of refill adherence and conduct experiments to determine the effect of the pitfalls on adherence estimations. We performed statistical significance testing on the impact of common pitfalls using a baseline scenario as reference.

    Findings: Slight changes in definition can significantly skew refill adherence estimates. Pickup patterns cause significant disagreement between measures and the commonly used proportion of days covered. Common data related issues had a small but statistically significant (p < 0.05) impact on population-level and significant effect on individual cases.

    Conclusion: Data-related issues encountered in real-world administrative databases, which affect various operational definitions of refill adherence differently, can significantly skew refill adherence values, leading to false conclusions about adherence, particularly when estimating adherence for individuals. © 2020 The Authors. Published by Elsevier B.V. 

  • 19.
    Hall, Ola
    et al.
    Lund University, Lund, Sweden.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi. Lund University, Lund, Sweden.
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi.
    A review of explainable AI in the satellite data, deep machine learning, and human poverty domain2022Inngår i: Patterns, E-ISSN 2666-3899, Vol. 3, nr 10, artikkel-id 100600Artikkel, forskningsoversikt (Fagfellevurdert)
    Abstract [en]

    Recent advances in artificial intelligence and deep machine learning have created a step change in how to measure human development indicators, in particular asset-based poverty. The combination of satellite imagery and deep machine learning now has the capability to estimate some types of poverty at a level close to what is achieved with traditional household surveys. An increasingly important issue beyond static estimations is whether this technology can contribute to scientific discovery and, consequently, new knowledge in the poverty and welfare domain. A foundation for achieving scientific insights is domain knowledge, which in turn translates into explainability and scientific consistency. We perform an integrative literature review focusing on three core elements relevant in this context—transparency, interpretability, and explainability—and investigate how they relate to the poverty, machine learning, and satellite imagery nexus. Our inclusion criteria for papers are that they cover poverty/wealth prediction, using survey data as the basis for the ground truth poverty/wealth estimates, be applicable to both urban and rural settings, use satellite images as the basis for at least some of the inputs (features), and the method should include deep neural networks. Our review of 32 papers shows that the status of the three core elements of explainable machine learning (transparency, interpretability, and domain knowledge) is varied and does not completely fulfill the requirements set up for scientific insights and discoveries. We argue that explainability is essential to support wider dissemination and acceptance of this research in the development community and that explainability means more than just interpretability. (c) 2022 The Author(s). 

  • 20.
    Heyman, Ellen Tolestam
    et al.
    Department of Emergency Medicine, Halland Hospital, Region Halland, Varberg, Sweden; Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden.
    Ashfaq, Awais
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Halland Hospital, Region Halland, Halmstad, Sweden.
    Khoshnood, Ardavan
    Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden; Skåne University Hospital Lund, Lund, Sweden.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab). Department of Astronomy and Theoretical Physics, Division of Computational Biology and Biological Physics, Lund University, Lund, Sweden.
    Ekelund, Ulf
    Department of Clinical Sciences Lund, Faculty of Medicine, Lund University, Lund, Sweden; Skåne University Hospital Lund, Lund, Sweden.
    Holmqvist, Lina Dahlén
    Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Sahlgrenska University Hospitals, Gothenburg, Sweden; .
    Lingman, Markus
    Halland Hospital, Region Halland, Halmstad Sweden; Department of Molecular and Clinical Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Improving Machine Learning 30-Day Mortality Prediction by Discounting Surprising Deaths2021Inngår i: Journal of Emergency Medicine, ISSN 0736-4679, E-ISSN 1090-1280, Vol. 61, nr 6, s. 763-773Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Machine learning (ML) is an emerging tool for predicting need of end-of-life discussion and palliative care, by using mortality as a proxy. But deaths, unforeseen by emergency physicians at time of the emergency department (ED) visit, might have a weaker association with the ED visit.

    Objectives: To develop an ML algorithm that predicts unsurprising deaths within 30 days after ED discharge.

    Methods: In this retrospective registry study, we included all ED attendances within the Swedish region of Halland in 2015 and 2016. All registered deaths within 30 days after ED discharge were classified as either “surprising” or “unsurprising” by an adjudicating committee with three senior specialists in emergency medicine. ML algorithms were developed for the death subclasses by using Logistic Regression(LR), Random Forest (RF), and Support Vector Machine (SVM).

    Results: Of all 30-day deaths (n = 148), 76% (n = 113) were not surprising to the adjudicating committee. The most common diseases were advanced stage cancer, multidisease/frailty, and dementia. By using LR, RF, and SVM, mean area under the receiver operating characteristic curve (ROC-AUC) of unsurprising deaths in the test set were 0.950 (SD 0.008), 0.944 (SD 0.007), and 0.949 (SD 0.007), respectively. For all mortality, the ROC-AUCs for LR, RF, and SVM were 0.924 (SD 0.012), 0.922 (SD 0.009), and 0.931 (SD 0.008). The difference in prediction performance between all and unsurprising death was statistically significant (P < .001) for all three models.

    Conclusion: In patients discharged to home from the ED, three-quarters of all 30-day deaths did not surprise an adjudicating committee with emergency medicine specialists. When only unsurprising deaths were included, ML mortality prediction improved significantly. © 2021 The Author(s). Published by Elsevier Inc.

  • 21.
    Hjärtström, Malin
    et al.
    Lund University, Lund, Sweden.
    Dihge, Looket
    Lund University, Lund, Sweden; Skåne University Hospital, Malmö, Sweden.
    Bendahl, Pär-Ola
    Lund University, Lund, Sweden.
    Skarping, Ida
    Lund University, Lund, Sweden; Skåne University Hospital, Malmö, Sweden.
    Ellbrant, Julia
    Lund University, Lund, Sweden; Skåne University Hospital, Malmö, Sweden.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi. Skåne University Hospital, Malmö, Sweden.
    Rydén, Lisa
    Lund University, Lund, Sweden; Skåne University Hospital, Malmö, Sweden.
    Noninvasive Staging of Lymph Node Status in Breast Cancer Using Machine Learning: External Validation and Further Model Development2023Inngår i: JMIR Cancer, E-ISSN 2369-1999, Vol. 9, artikkel-id e46474Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Most patients diagnosed with breast cancer present with a node-negative disease. Sentinel lymph node biopsy (SLNB) is routinely used for axillary staging, leaving patients with healthy axillary lymph nodes without therapeutic effects but at risk of morbidities from the intervention. Numerous studies have developed nodal status prediction models for noninvasive axillary staging using postoperative data or imaging features that are not part of the diagnostic workup. Lymphovascular invasion (LVI) is a top-ranked predictor of nodal metastasis; however, its preoperative assessment is challenging.Objective: This paper aimed to externally validate a multilayer perceptron (MLP) model for noninvasive lymph node staging (NILS) in a large population-based cohort (n=18,633) and develop a new MLP in the same cohort. Data were extracted from the Swedish National Quality Register for Breast Cancer (NKBC, 2014-2017), comprising only routinely and preoperatively available documented clinicopathological variables. A secondary aim was to develop and validate an LVI MLP for imputation of missing LVI status to increase the preoperative feasibility of the original NILS model. Methods: Three nonoverlapping cohorts were used for model development and validation. A total of 4 MLPs for nodal status and 1 LVI MLP were developed using 11 to 12 routinely available predictors. Three nodal status models were used to account for the different availabilities of LVI status in the cohorts and external validation in NKBC. The fourth nodal status model was developed for 80% (14,906/18,663) of NKBC cases and validated in the remaining 20% (3727/18,663). Three alternatives for imputation of LVI status were compared. The discriminatory capacity was evaluated using the validation area under the receiver operating characteristics curve (AUC) in 3 of the nodal status models. The clinical feasibility of the models was evaluated using calibration and decision curve analyses.Results: External validation of the original NILS model was performed in NKBC (AUC 0.699, 95% CI 0.690-0.708) with good calibration and the potential of sparing 16% of patients with node-negative disease from SLNB. The LVI model was externally validated (AUC 0.747, 95% CI 0.694-0.799) with good calibration but did not improve the discriminatory performance of the nodal status models. A new nodal status model was developed in NKBC without information on LVI (AUC 0.709, 95% CI: 0.688-0.729), with excellent calibration in the holdout internal validation cohort, resulting in the potential omission of 24% of patients from unnecessary SLNBs.Conclusions: The NILS model was externally validated in NKBC, where the imputation of LVI status did not improve the model's discriminatory performance. A new nodal status model demonstrated the feasibility of using register data comprising only the variables available in the preoperative setting for NILS using machine learning. Future steps include ongoing preoperative validation of the NILS model and extending the model with, for example, mammography images. © Malin Hjärtström, Looket Dihge, Pär-Ola Bendahl, Ida Skarping, Julia Ellbrant, Mattias Ohlsson, Lisa Rydén.

  • 22.
    Linse, Björn
    et al.
    Lund University, Lund, Sweden.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi. Lund University, Lund, Sweden.
    Stehlik, Joseph
    University Of Utah School Of Medicine, Salt Lake City, United States; Ishlt Transplant Registry, Dallas, United States.
    Lund, Lars H.
    Karolinska Institutet, Stockholm, Sweden; Karolinska University Hospital, Stockholm, Sweden.
    Andersson, Bodil
    Lund University, Lund, Sweden; University Hospital, Lund, Sweden.
    Nilsson, Johan
    Department Of Translational Medicine, Malmo, Sweden; University Hospital, Lund, Sweden.
    A machine learning model for prediction of 30-day primary graft failure after heart transplantation2023Inngår i: Heliyon, E-ISSN 2405-8440, Vol. 9, nr 3, s. 1-10, artikkel-id e14282Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Background: Primary graft failure (PGF) remains the most common cause of short-term mortality after heart transplantation. The main objective was to develop and validate a risk model for prediction of short-term mortality due to PGF after heart transplantation using the ISHLT Heart Transplant Registry. Methods: We developed a non-linear artificial neural networks (ANN) model to evaluate the association between recipient-donor variables and post-transplant PGF. Patients in the ISHLT registry were randomly divided into derivation and an independent internal validation cohort. The primary endpoint was PGF defined as death within 30 days due to Graft failure or Cardiovascular causes or retransplant within 30 days for causes other than rejection. Results: Among 64,964 adult recipients transplanted between 1994 and 2013, mean age was 51 years and 22% were female. The incidence of PGF up to 30 days was 3.7%. The ANN model selected 33 of 77 risk variables as relevant for PGF prediction. The C-index in the test cohort was 0.70 (95% CI: 0.68-0.71). The risk variables which most influenced the PGF were underlying HF diagnosis, ischemia time and sex, while renal function had a lower influence. Conclusion: An ANN model to predict primary graft dysfunction was derived and independently validated. The good discrimination of the ANN model likely results from its flexibility to model potentially non-linear relationships and interactions. Whether this model with improved discrimination can assist in clinical decisions at the time of transplant should be tested. © 2023 The Authors

  • 23.
    Najmeh, Abiri
    et al.
    Department of Astronomy and Theoretical Physics, Lund University, Lund, Sweden.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Variational auto-encoders with Student’s t-prior2019Inngår i: ESANN 2019 Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning: Bruges – 24-26 April 2019, Bruges: ESANN , 2019, s. 415-420Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We propose a new structure for the variational auto-encoders (VAEs) prior, with the weakly informative multivariate Student’s t-distribution. In the proposed model all distribution parameters are trained, thereby allowing for a more robust approximation of the underlying data distribution. We used Fashion-MNIST data in two experiments to compare the proposed VAEs with the standard Gaussian priors. Both experiments showed a better reconstruction of the images with VAEs using Student’s t-prior distribution. © 2019 ESANN (i6doc.com). All rights reserved.

  • 24.
    Nyström, Axel
    et al.
    Lund University, Lund, Sweden.
    Olsson de Capretz, Pontus
    Skåne University Hospital, Lund, Sweden; Lund University, Lund, Sweden.
    Björkelund, Anders
    Lund University, Lund, Sweden.
    Lundager Forberg, Jakob
    Lund University, Lund, Sweden; Helsingborg Hospital, Helsingborg, Sweden.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi. Lund University, Lund, Sweden.
    Björk, Jonas
    Lund University, Lund, Sweden; Skåne University Hospital, Lund, Sweden.
    Ekelund, Ulf
    Skåne University Hospital, Lund, Sweden; Lund University, Lund, Sweden.
    Prior electrocardiograms not useful for machine learning predictions of major adverse cardiac events in emergency department chest pain patients2024Inngår i: Journal of Electrocardiology, ISSN 0022-0736, E-ISSN 1532-8430, Vol. 82, s. 42-51Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    At the emergency department (ED), it is important to quickly and accurately determine which patients are likely to have a major adverse cardiac event (MACE). Machine learning (ML) models can be used to aid physicians in detecting MACE, and improving the performance of such models is an active area of research. In this study, we sought to determine if ML models can be improved by including a prior electrocardiogram (ECG) from each patient. To that end, we trained several models to predict MACE within 30 days, both with and without prior ECGs, using data collected from 19,499 consecutive patients with chest pain, from five EDs in southern Sweden, between the years 2017 and 2018. Our results indicate no improvement in AUC from prior ECGs. This was consistent across models, both with and without additional clinical input variables, for different patient subgroups, and for different subsets of the outcome. While contradicting current best practices for manual ECG analysis, the results are positive in the sense that ML models with fewer inputs are more easily and widely applicable in practice. © 2023 The Authors

  • 25.
    Ohlsson, Mattias
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Hellmark, Thomas
    Skånes universitetssjukhus, Lund, Sweden.
    Bengtsson, Anders A.
    Rheumatology, Department of Clinical Sciences, Lund, Lund University, Lund, SE-221 00, Sweden; Department of Rheumatology, Skåne University Hospital, Lund and Malmö, SE-214 28, Sweden.
    Theander, Elke
    Rheumatology, Department of Clinical Sciences, Malmö, Lund University, Malmö, SE-221 00, Sweden.
    Turesson, Carl
    Department of Rheumatology, Skåne University Hospital, Lund and Malmö, SE-214 28, Sweden; Rheumatology, Department of Clinical Sciences, Malmö, Lund University, Malmö, SE-221 00, Sweden.
    Klint, Cecilia
    Immunovia AB, Lund, Sweden.
    Wingren, Christer
    Lunds universitet, Lund, Sweden.
    Ekstrand, Anna Isinger
    Lunds universitet, Lund, Sweden.
    Proteomic Data Analysis for Differential Profiling of the Autoimmune Diseases SLE, RA, SS, and ANCA-Associated Vasculitis2021Inngår i: Journal of Proteome Research, ISSN 1535-3893, E-ISSN 1535-3907, Vol. 20, nr 2, s. 1252-1260Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Early and correct diagnosis of inflammatory rheumatic diseases (IRD) poses a clinical challenge due to the multifaceted nature of symptoms, which also may change over time. The aim of this study was to perform protein expression profiling of four systemic IRDs, systemic lupus erythematosus (SLE), ANCA-associated systemic vasculitis (SV), rheumatoid arthritis (RA), and Sjögren's syndrome (SS), and healthy controls to identify candidate biomarker signatures for differential classification. A total of 316 serum samples collected from patients with SLE, RA, SS, or SV and from healthy controls were analyzed using 394-plex recombinant antibody microarrays. Differential protein expression profiling was examined using Wilcoxon signed rank test, and condensed biomarker panels were identified using advanced bioinformatics and state-of-the art classification algorithms to pinpoint signatures reflecting each disease (raw data set available at https://figshare.com/s/3bd3848a28ef6e7ae9a9.). In this study, we were able to classify the included individual IRDs with high accuracy, as demonstrated by the ROC area under the curve (ROC AUC) values ranging between 0.96 and 0.80. In addition, the groups of IRDs could be separated from healthy controls at an ROC AUC value of 0.94. Disease-specific candidate biomarker signatures and general autoimmune signature were identified, including several deregulated analytes. This study supports the rationale of using multiplexed affinity-based technologies to reflect the biological complexity of autoimmune diseases. A multiplexed approach for decoding multifactorial complex diseases, such as autoimmune diseases, will play a significant role for future diagnostic purposes, essential to prevent severe organ- and tissue-related damage. © 2020 American Chemical Society.

  • 26.
    Polymeri, E.
    et al.
    Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Sadik, M.
    Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.
    Kaboteh, R.
    Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.
    Borrelli, P.
    Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.
    Enqvist, O.
    Department of Electrical Engineering, Region Västra Götaland, Chalmers University of Technology, Gothenburg, Sweden.
    Ulén, J.
    Eigenvision AB, Malmö, Sweden.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).
    Trägårdh, E.
    Department of Translational Medicine, Institute of Clinical Sciences, Lund University, Malmö, Sweden.
    Poulsen, M. H.
    Department of Urology, Odense University Hospital, Odense, Denmark.
    Simonsen, J. A.
    Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark.
    Hoilund-Carlsen, P. F.
    Department of Nuclear Medicine, Odense University Hospital, Odense, Denmark.
    Johnsson, ÅA.
    Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Edenbrandt, L.
    Department of Clinical Physiology, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg, Sweden.
    Deep learning-based quantification of PET/CT prostate gland uptake: association with overall survival2020Inngår i: Clinical Physiology and Functional Imaging, ISSN 1475-0961, E-ISSN 1475-097X, Vol. 40, nr 2, s. 106-113Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Aim: To validate a deep-learning (DL) algorithm for automated quantification of prostate cancer on positron emission tomography/computed tomography (PET/CT) and explore the potential of PET/CT measurements as prognostic biomarkers. Material and methods: Training of the DL-algorithm regarding prostate volume was performed on manually segmented CT images in 100 patients. Validation of the DL-algorithm was carried out in 45 patients with biopsy-proven hormone-naïve prostate cancer. The automated measurements of prostate volume were compared with manual measurements made independently by two observers. PET/CT measurements of tumour burden based on volume and SUV of abnormal voxels were calculated automatically. Voxels in the co-registered 18F-choline PET images above a standardized uptake value (SUV) of 2·65, and corresponding to the prostate as defined by the automated segmentation in the CT images, were defined as abnormal. Validation of abnormal voxels was performed by manual segmentation of radiotracer uptake. Agreement between algorithm and observers regarding prostate volume was analysed by Sørensen-Dice index (SDI). Associations between automatically based PET/CT biomarkers and age, prostate-specific antigen (PSA), Gleason score as well as overall survival were evaluated by a univariate Cox regression model. Results: The SDI between the automated and the manual volume segmentations was 0·78 and 0·79, respectively. Automated PET/CT measures reflecting total lesion uptake and the relation between volume of abnormal voxels and total prostate volume were significantly associated with overall survival (P = 0·02), whereas age, PSA, and Gleason score were not. Conclusion: Automated PET/CT biomarkers showed good agreement to manual measurements and were significantly associated with overall survival. © 2019 The Authors. Clinical Physiology and Functional Imaging published by John Wiley & Sons Ltd on behalf of Scandinavian Society of Clinical Physiology and Nuclear Medicine

  • 27.
    Pomares-Millan, Hugo
    et al.
    Department Of Clinical Sciences Malmö, Malmö, Sweden.
    Poveda, Alaitz
    Department Of Clinical Sciences Malmö, Malmö, Sweden.
    Atabaki-Pasdar, Naemieh
    Department Of Clinical Sciences Malmö, Malmö, Sweden.
    Johansson, Ingegerd
    Umeå University, Umea, Sweden.
    Björk, Jonas
    Lund University, Lund, Sweden; University Hospital, Lund, Sweden.
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi. Department Of Astronomy And Theoretical Physics, Lund, Sweden.
    Giordano, Giuseppe N.
    Department Of Clinical Sciences Malmö, Malmö, Sweden.
    Franks, Paul W.
    Department Of Clinical Sciences Malmö, Malmö, Sweden; Umeå University, Umeå, Sweden; Harvard School Of Public Health, Boston, United States.
    Predicting Sensitivity to Adverse Lifestyle Risk Factors for Cardiometabolic Morbidity and Mortality2022Inngår i: Nutrients, E-ISSN 2072-6643, Vol. 14, nr 15, artikkel-id 3171Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    People appear to vary in their susceptibility to lifestyle risk factors for cardiometabolic disease; determining a priori who is most sensitive may help optimize the timing, design, and delivery of preventative interventions. We aimed to ascertain a person’s degree of resilience or sensitivity to adverse lifestyle exposures and determine whether these classifications help predict cardiometabolic disease later in life; we pooled data from two population-based Swedish prospective cohort studies (n = 53,507), and we contrasted an individual’s cardiometabolic biomarker profile with the profile predicted for them given their lifestyle exposure characteristics using a quantile random forest approach. People who were classed as ‘sensitive’ to hypertension- and dyslipidemia-related lifestyle exposures were at higher risk of developing cardiovascular disease (CVD, hazards ratio 1.6 (95% CI: 1.3, 1.91)), compared with the general population. No differences were observed for type 2 diabetes (T2D) risk. Here, we report a novel approach to identify individuals who are especially sensitive to adverse lifestyle exposures and who are at higher risk of subsequent cardiovascular events. Early preventive interventions may be needed in this subgroup. © 2022 by the authors.

  • 28.
    Sarmadi, Hamid
    et al.
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Rögnvaldsson, Thorsteinn
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Carlsson, Nils Roger
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Ohlsson, Mattias
    Högskolan i Halmstad, Akademin för informationsteknologi, Centrum för forskning om tillämpade intelligenta system (CAISR).
    Wahab, Ibrahim
    Lund University, Lund, Sweden.
    Hall, Ola
    Lund University, Lund, Sweden.
    Towards Explaining Satellite Based Poverty Predictions with Convolutional Neural Networks2023Inngår i: 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), IEEE, 2023Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Deep convolutional neural networks (CNNs) have been shown to predict poverty and development indicators from satellite images with surprising accuracy. This paper presents a first attempt at analyzing the CNNs responses in detail and explaining the basis for the predictions. The CNN model, while trained on relatively low resolution day- and night-time satellite images, is able to outperform human subjects who look at high-resolution images in ranking the Wealth Index categories. Multiple explainability experiments performed on the model indicate the importance of the sizes of the objects, pixel colors in the image, and provide a visualization of the importance of different structures in input images. A visualization is also provided of type images that maximize the network prediction of Wealth Index, which provides clues on what the CNN prediction is based on.

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