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Publications (7 of 7) Show all publications
Cooney, M., Ong, L., Pashami, S., Järpe, E. & Ashfaq, A. (2019). Avoiding improper treatment of dementia patients by care robots. In: : . Paper presented at The Dark Side of Human-Robot Interaction: Ethical Considerations and Community Guidelines for the Field of HRI. HRI Workshop, Daegu, South Korea, March 11, 2019.
Open this publication in new window or tab >>Avoiding improper treatment of dementia patients by care robots
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2019 (English)Conference paper, Published paper (Refereed)
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:hh:diva-39448 (URN)
Conference
The Dark Side of Human-Robot Interaction: Ethical Considerations and Community Guidelines for the Field of HRI. HRI Workshop, Daegu, South Korea, March 11, 2019
Available from: 2019-05-22 Created: 2019-05-22 Last updated: 2019-09-16
Ashfaq, A., Lönn, S., Nilsson, H., Eriksson, J., Kwatra, J., Yasin, Z., . . . Lingman, M. (2019). Data Profile: Regional Healthcare Information Platform in Halland, Sweden. International Journal of Epidemiology
Open this publication in new window or tab >>Data Profile: Regional Healthcare Information Platform in Halland, Sweden
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2019 (English)In: International Journal of Epidemiology, ISSN 0300-5771, E-ISSN 1464-3685Article in journal (Refereed) Submitted
Abstract [en]

Accurate and comprehensive healthcare data coupled with modern analytical tools can play a vital role in enabling care providers to make better-informed decisions, leading to effective and cost-efficient care delivery. This paper describes a novel strategic healthcare analysis and research platform that encapsulates 360-degree pseudo-anonymized data covering clinical, operational capacity and financial data on over 500,000 patients treated since 2009 across all care delivery units in the county of Halland, Sweden. The over-arching goal is to develop a comprehensive healthcare data infrastructure that captures complete care processes at individual, organizational and population levels. These longitudinal linked healthcare data are a valuable tool for research in a broad range of areas including health economy and process development using real world evidence.

Key messages

Structured and standardized variables have been linked from different regional healthcare sources into a research information platform including all healthcare visits in the county of Halland in Sweden, from 2009 to date.

Since 2015, the regional information platform integrates a cost component to each healthcare visit: thus being able to quantify patient level value, safety and cost efficiency across the continuum of care.

Place, publisher, year, edition, pages
Oxford: Oxford University Press, 2019
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
urn:nbn:se:hh:diva-39308 (URN)
Available from: 2019-05-02 Created: 2019-05-02 Last updated: 2019-05-03
Ashfaq, A. & Nowaczyk, S. (2019). Machine learning in healthcare - a system’s perspective. In: B. Aditya Prakash, Anil Vullikanti, Shweta Bansal, Adam Sadelik (Ed.), Proceedings of the ACM SIGKDD Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK): . Paper presented at 25th ACM SIGKDD Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK '19), Anchorage, Alaska, United States, August 5, 2019 (pp. 14-17). Arlington
Open this publication in new window or tab >>Machine learning in healthcare - a system’s perspective
2019 (English)In: Proceedings of the ACM SIGKDD Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK) / [ed] B. Aditya Prakash, Anil Vullikanti, Shweta Bansal, Adam Sadelik, Arlington, 2019, p. 14-17Conference paper, Published paper (Refereed)
Abstract [en]

A consequence of the fragmented and siloed healthcare landscape is that patient care (and data) is split along multitude of different facilities and computer systems and enabling interoperability between these systems is hard. The lack interoperability not only hinders continuity of care and burdens providers, but also hinders effective application of Machine Learning (ML) algorithms. Thus, most current ML algorithms, designed to understand patient care and facilitate clinical decision-support, are trained on limited datasets. This approach is analogous to the Newtonian paradigm of Reductionism in which a system is broken down into elementary components and a description of the whole is formed by understanding those components individually. A key limitation of the reductionist approach is that it ignores the component-component interactions and dynamics within the system which are often of prime significance in understanding the overall behaviour of complex adaptive systems (CAS). Healthcare is a CAS.

Though the application of ML on health data have shown incremental improvements for clinical decision support, ML has a much a broader potential to restructure care delivery as a whole and maximize care value. However, this ML potential remains largely untapped: primarily due to functional limitations of Electronic Health Records (EHR) and the inability to see the healthcare system as a whole. This viewpoint (i) articulates the healthcare as a complex system which has a biological and an organizational perspective, (ii) motivates with examples, the need of a system's approach when addressing healthcare challenges via ML and, (iii) emphasizes to unleash EHR functionality - while duly respecting all ethical and legal concerns - to reap full benefits of ML.

Place, publisher, year, edition, pages
Arlington: , 2019
Keywords
Machine learning, Healthcare complexity, System's thinking, Electronic health records
National Category
Other Medical Engineering
Identifiers
urn:nbn:se:hh:diva-40395 (URN)
Conference
25th ACM SIGKDD Workshop on Epidemiology meets Data Mining and Knowledge Discovery (epiDAMIK '19), Anchorage, Alaska, United States, August 5, 2019
Available from: 2019-08-14 Created: 2019-08-14 Last updated: 2019-08-14Bibliographically approved
Ashfaq, A. (2019). Predicting clinical outcomes via machine learning on electronic health records. (Licentiate dissertation). Halmstad: Halmstad University Press
Open this publication in new window or tab >>Predicting clinical outcomes via machine learning on electronic health records
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The rising complexity in healthcare, exacerbated by an ageing population, results in ineffective decision-making leading to detrimental effects on care quality and escalates care costs. Consequently, there is a need for smart decision support systems that can empower clinician's to make better informed care decisions. Decisions, which are not only based on general clinical knowledge and personal experience, but also rest on personalised and precise insights about future patient outcomes. A promising approach is to leverage the ongoing digitization of healthcare that generates unprecedented amounts of clinical data stored in Electronic Health Records (EHRs) and couple it with modern Machine Learning (ML) toolset for clinical decision support, and simultaneously, expand the evidence base of medicine. As promising as it sounds, assimilating complete clinical data that provides a rich perspective of the patient's health state comes with a multitude of data-science challenges that impede efficient learning of ML models. This thesis primarily focuses on learning comprehensive patient representations from EHRs. The key challenges of heterogeneity and temporality in EHR data are addressed using human-derived features appended to contextual embeddings of clinical concepts and Long-Short-Term-Memory networks, respectively. The developed models are empirically evaluated in the context of predicting adverse clinical outcomes such as mortality or hospital readmissions. We also present evidence that, surprisingly, different ML models primarily designed for non-EHR analysis (like language processing and time-series prediction) can be combined and adapted into a single framework to efficiently represent EHR data and predict patient outcomes.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2019
Series
Halmstad University Dissertations ; 58
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
urn:nbn:se:hh:diva-39309 (URN)978-91-88749-24-6 (ISBN)978-91-88749-25-3 (ISBN)
Presentation
2019-05-23, R4318, R Building, Halmstad University, Halmstad, Sweden, 13:00 (English)
Opponent
Supervisors
Available from: 2019-05-06 Created: 2019-05-02 Last updated: 2019-05-06Bibliographically approved
Ashfaq, A., Pinheiro Sant'Anna, A., Lingman, M. & Nowaczyk, S. (2019). Readmission prediction using deep learning on electronic health records. Journal of Biomedical Informatics, 97, Article ID 103256.
Open this publication in new window or tab >>Readmission prediction using deep learning on electronic health records
2019 (English)In: Journal of Biomedical Informatics, ISSN 1532-0464, E-ISSN 1532-0480, Vol. 97, article id 103256Article in journal (Refereed) Published
Abstract [en]

Unscheduled 30-day readmissions are a hallmark of Congestive Heart Failure (CHF) patients that pose significant health risks and escalate care cost. In order to reduce readmissions and curb the cost of care, it is important to initiate targeted intervention programs for patients at risk of readmission. This requires identifying high-risk patients at the time of discharge from hospital. Here, using real data from over 7,500 CHF patients hospitalized between 2012 and 2016 in Sweden, we built and tested a deep learning framework to predict 30-day unscheduled readmission. We present a cost-sensitive formulation of Long Short-Term Memory (LSTM) neural network using expert features and contextual embedding of clinical concepts. This study targets key elements of an Electronic Health Record (EHR) driven prediction model in a single framework: using both expert and machine derived features, incorporating sequential patterns and addressing the class imbalance problem. We show that the model with all key elements achieves a higher discrimination ability (AUC 0.77) compared to the rest. Additionally, we present a simple financial analysis to estimate annual savings if targeted interventions are offered to high risk patients. © 2019 The Authors

Place, publisher, year, edition, pages
Maryland Heights, MO: Academic Press, 2019
Keywords
Electronic health records, Readmission prediction, Long short-term memory networks, Contextual embeddings
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
urn:nbn:se:hh:diva-39297 (URN)10.1016/j.jbi.2019.103256 (DOI)31351136 (PubMedID)2-s2.0-85069858722 (Scopus ID)
Projects
HiCube - behovsmotiverad hälsoinnovation
Funder
European Regional Development Fund (ERDF)
Note

Funding: The authors thank the European Regional Development Fund (ERDF), Health Technology Center and CAISR at Halmstad University and Hallands Hospital for financing the research work under the project HiCube - behovsmotiverad hälsoinnovation.

Available from: 2019-04-30 Created: 2019-04-30 Last updated: 2019-09-10Bibliographically approved
Blom, M. C., Ashfaq, A., Pinheiro Sant'Anna, A., Anderson, P. D. & Lingman, M. (2019). Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population based registry study. BMJ Open, 9(8), Article ID e028015.
Open this publication in new window or tab >>Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population based registry study
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2019 (English)In: BMJ Open, ISSN 2044-6055, E-ISSN 2044-6055, Vol. 9, no 8, article id e028015Article in journal (Refereed) Published
Abstract [en]

Background: Aggressive treatment at end-of-life (EOL) can be traumatic to patients and may not add clinical benefit. Absent an accurate prognosis of death, individual level biases may prevent timely discussions about the scope of EOL care and patients are at risk of being subject to care against their desire. The aim of this work is to develop predictive algorithms for identifying patients at EOL, with clinically meaningful discriminatory power.

Methods: Retrospective, population-based study of patients utilizing emergency departments (EDs) in Sweden, Europe. Electronic health records (EHRs) were used to train supervised learning algorithms to predict all-cause mortality within 30 days following ED discharge. Algorithm performance was validated out of sample on EHRs from a separate hospital, to which the algorithms were previously unexposed.

Results: Of 65,776 visits in the development set, 136 (0.21%) experienced the outcome. The algorithm with highest discrimination attained ROC-AUC 0.945 (95% CI 0.933 - 0.956), with sensitivity 0.869 (95% CI 0.802, 0.931) and specificity 0.858 (0.855, 0.860) on the validation set.

Conclusions: Multiple algorithms displayed excellent discrimination and outperformed available indexes for short-term mortality prediction. The practical utility of the algorithms increases as the required data were captured electronically and did not require de novo data collection.

Trial registration number: Not applicable.

Place, publisher, year, edition, pages
London: BMJ Publishing Group Ltd, 2019
National Category
Social and Clinical Pharmacy
Identifiers
urn:nbn:se:hh:diva-39307 (URN)10.1136/bmjopen-2018-028015 (DOI)31401594 (PubMedID)
Note

Funding: This work was partly funded by Region Halland, Sweden.The initial stage of MCBs involvement in the work was funded by a grant for post-doctoral research from the Tegger Foundation.

Available from: 2019-05-02 Created: 2019-05-02 Last updated: 2019-08-15Bibliographically approved
Ashfaq, A. & Adler, J. (2017). A modified fuzzy C means algorithm for shading correction in craniofacial CBCT images. In: Almir Badnjevic (Ed.), CMBEBIH 2017: Proceedings of the International Conference on Medical and Biological Engineering 2017. Paper presented at International Conference on Medical and Biological Engineering, CMBEBIH 2017, Sarajevo, Bosnia and Herzegovina, 16 - 18 March 2017 (pp. 531-538). Singapore: Springer, 62
Open this publication in new window or tab >>A modified fuzzy C means algorithm for shading correction in craniofacial CBCT images
2017 (English)In: CMBEBIH 2017: Proceedings of the International Conference on Medical and Biological Engineering 2017 / [ed] Almir Badnjevic, Singapore: Springer, 2017, Vol. 62, p. 531-538Conference paper, Published paper (Refereed)
Abstract [en]

CBCT images suffer from acute shading artifacts primarily due to scatter. Numerous image-domain correction algorithms have been proposed in the literature that use patient-specific planning CT images to estimate shading contributions in CBCT images. However, in the context of radiosurgery applications such as gamma knife, planning images are often acquired through MRI which impedes the use of polynomial fitting approaches for shading correction. We present a new shading correction approach that is independent of planning CT images. Our algorithm is based on the assumption that true CBCT images follow a uniform volumetric intensity distribution per material, and scatter perturbs this uniform texture by contributing cupping and shading artifacts in the image domain. The framework is a combination of fuzzy C-means coupled with a neighborhood regularization term and Otsu’s method. Experimental results on artificially simulated craniofacial CBCT images are provided to demonstrate the effectiveness of our algorithm. Spatial non-uniformity is reduced from 16% to 7% in soft tissue and from 44% to 8% in bone regions. With shading-correction, thresholding based segmentation accuracy for bone pixels is improved from 85% to 91% when compared to thresholding without shading-correction. The proposed algorithm is thus practical and qualifies as a plug and play extension into any CBCT reconstruction software for shading correction. © Springer Nature Singapore Pte Ltd. 2017.

Place, publisher, year, edition, pages
Singapore: Springer, 2017
Series
IFMBE Proceedings, ISSN 1680-0737
Keywords
Cone beam CT, Shading correction, Fuzzy C means
National Category
Medical Image Processing
Identifiers
urn:nbn:se:hh:diva-36107 (URN)10.1007/978-981-10-4166-2_81 (DOI)2-s2.0-85016072022 (Scopus ID)978-981-10-4166-2 (ISBN)978-981-10-4165-5 (ISBN)
Conference
International Conference on Medical and Biological Engineering, CMBEBIH 2017, Sarajevo, Bosnia and Herzegovina, 16 - 18 March 2017
Available from: 2018-01-17 Created: 2018-01-17 Last updated: 2018-03-23Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-5688-0156

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