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Pinheiro Sant'Anna, AnitaORCID iD iconorcid.org/0000-0002-3495-2961
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Publications (10 of 40) Show all publications
Calikus, E., Nowaczyk, S., Pinheiro Sant'Anna, A. & Dikmen, O. (2020). No Free Lunch But A Cheaper Supper: A General Framework for Streaming Anomaly Detection. Expert systems with applications
Open this publication in new window or tab >>No Free Lunch But A Cheaper Supper: A General Framework for Streaming Anomaly Detection
2020 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793Article in journal (Refereed) Submitted
Abstract [en]

In recent years, there has been increased research interest in detecting anomalies in temporal streaming data. A variety of algorithms have been developed in the data mining community, which can be divided into two categories (i.e., general and ad hoc). In most cases, general approaches assume the one-size-fits-all solution model where a single anomaly detector can detect all anomalies in any domain.  To date, there exists no single general method that has been shown to outperform the others across different anomaly types, use cases and datasets. On the other hand, ad hoc approaches that are designed for a specific application lack flexibility. Adapting an existing algorithm is not straightforward if the specific constraints or requirements for the existing task change. In this paper, we propose SAFARI, a general framework formulated by abstracting and unifying the fundamental tasks in streaming anomaly detection, which provides a flexible and extensible anomaly detection procedure. SAFARI helps to facilitate more elaborate algorithm comparisons by allowing us to isolate the effects of shared and unique characteristics of different algorithms on detection performance. Using SAFARI, we have implemented various anomaly detectors and identified a research gap that motivates us to propose a novel learning strategy in this work. We conducted an extensive evaluation study of 20 detectors that are composed using SAFARI and compared their performances using real-world benchmark datasets with different properties. The results indicate that there is no single superior detector that works well for every case, proving our hypothesis that "there is no free lunch" in the streaming anomaly detection world. Finally, we discuss the benefits and drawbacks of each method in-depth and draw a set of conclusions to guide future users of SAFARI.

Place, publisher, year, edition, pages
Oxford: Elsevier, 2020
Keywords
anomaly detection
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-41420 (URN)
Funder
Knowledge Foundation, 20160103
Available from: 2020-01-29 Created: 2020-01-29 Last updated: 2020-02-18
Calikus, E., Nowaczyk, S., Pinheiro Sant'Anna, A., Gadd, H. & Werner, S. (2019). A data-driven approach for discovering heat load patterns in district heating. Applied Energy, 252, Article ID 113409.
Open this publication in new window or tab >>A data-driven approach for discovering heat load patterns in district heating
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2019 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 252, article id 113409Article in journal (Refereed) Published
Abstract [en]

Understanding the heat usage of customers is crucial for effective district heating operations and management. Unfortunately, existing knowledge about customers and their heat load behaviors is quite scarce. Most previous studies are limited to small-scale analyses that are not representative enough to understand the behavior of the overall network. In this work, we propose a data-driven approach that enables large-scale automatic analysis of heat load patterns in district heating networks without requiring prior knowledge. Our method clusters the customer profiles into different groups, extracts their representative patterns, and detects unusual customers whose profiles deviate significantly from the rest of their group. Using our approach, we present the first large-scale, comprehensive analysis of the heat load patterns by conducting a case study on many buildings in six different customer categories connected to two district heating networks in the south of Sweden. The 1222 buildings had a total floor space of 3.4 million square meters and used 1540 TJ heat during 2016. The results show that the proposed method has a high potential to be deployed and used in practice to analyze and understand customers’ heat-use habits. © 2019 Calikus et al. Published by Elsevier Ltd.

Place, publisher, year, edition, pages
Oxford: Elsevier, 2019
Keywords
District heating, Energy efficiency, Heat load patterns, Clustering, Abnormal heat use
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-40907 (URN)10.1016/j.apenergy.2019.113409 (DOI)000497968000013 ()2-s2.0-85066961984 (Scopus ID)
Funder
Knowledge Foundation, 20160103
Available from: 2019-11-12 Created: 2019-11-12 Last updated: 2020-01-30Bibliographically approved
Mendoza-Palechor, F., Menezes, M. L., Pinheiro Sant'Anna, A., Ortiz-Barrios, M., Samara, A. & Galway, L. (2019). Affective recognition from EEG signals: an integrated data-mining approach. Journal of Ambient Intelligence and Humanized Computing, 10(10), 3955-3974
Open this publication in new window or tab >>Affective recognition from EEG signals: an integrated data-mining approach
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2019 (English)In: Journal of Ambient Intelligence and Humanized Computing, ISSN 1868-5137, E-ISSN 1868-5145, Vol. 10, no 10, p. 3955-3974Article in journal (Refereed) Published
Abstract [en]

Emotions play an important role in human communication, interaction, and decision making processes. Therefore, considerable efforts have been made towards the automatic identification of human emotions, in particular electroencephalogram (EEG) signals and Data Mining (DM) techniques have been then used to create models recognizing the affective states of users. However, most previous works have used clinical grade EEG systems with at least 32 electrodes. These systems are expensive and cumbersome, and therefore unsuitable for usage during normal daily activities. Smaller EEG headsets such as the Emotiv are now available and can be used during daily activities. This paper investigates the accuracy and applicability of previous affective recognition methods on data collected with an Emotiv headset while participants used a personal computer to fulfill several tasks. Several features were extracted from four channels only (AF3, AF4, F3 and F4 in accordance with the 10–20 system). Both Support Vector Machine and Naïve Bayes were used for emotion classification. Results demonstrate that such methods can be used to accurately detect emotions using a small EEG headset during a normal daily activity. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

Place, publisher, year, edition, pages
Heidelberg: Springer Berlin/Heidelberg, 2019
Keywords
Affective recognition, Statistical features, Affective computing, Electroencephalogram (EEG), Data Mining (DM)
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-40797 (URN)10.1007/s12652-018-1065-z (DOI)000487047400018 ()2-s2.0-85054326423 (Scopus ID)
Note

Funders: REMIND Project from the European Union's Horizon 2020 research and innovation programme (734355), European Cooperation in Science and Technology (COST) (COST-STSM-TD1405- 33385) & National Council for Scientific and Technological Development (CNPq).

Available from: 2019-10-31 Created: 2019-10-31 Last updated: 2019-12-05Bibliographically approved
Haglund, E., Pinheiro Sant'Anna, A., Andersson, M., Bremander, A. & Aili, K. (2019). Dynamic joint stability measured as gait symmetry in people with symptomatic knee osteoarthritis. Paper presented at Annual European Congress of Rheumatology (EULAR 2019), Madrid, Spain, June 12-15, 2019. Annals of the Rheumatic Diseases, 78(Suppl. 2)
Open this publication in new window or tab >>Dynamic joint stability measured as gait symmetry in people with symptomatic knee osteoarthritis
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2019 (English)In: Annals of the Rheumatic Diseases, ISSN 0003-4967, E-ISSN 1468-2060, Vol. 78, no Suppl. 2, p. -1458Article in journal, Meeting abstract (Refereed) Published
Abstract [en]

Background: Modern strategies for knee osteoarthritis (OA) treatment and prevention includes early detection and analyses about pain, gait and lower extremity muscle function including both strength and stability. The very first sign of knee OA is pain or perceived knee instability, often experienced during weight bearing activities e.g. walking. Increased muscle strength will provide dynamic joint stability, reduce pain, and disability. Specific measures of gait symmetry (GS) can be assessed objectively by using accelerometers, which potentially is a feasible method when evaluating early symptoms of symptomatic knee OA.

Objectives: The aim was to study if symptoms of early knee pain affected gait symmetry, and the association between lower extremity muscles function and gait symmetry in patients with symptomatic knee OA.

Methods: Thirty-five participants (mean age 52 SD 9 years, 66% women) with uni- or bilateral symptomatic knee OA, and without signs of an inflammatory rheumatic disease or knee trauma were included. Pain was assessed by a numeric rating scale (NRS, range 0-10 best to worse), tests of lower extremity muscle function with the maximum number of one leg rises. Dynamic stability was measured as GS by using wearable inertial sensors (PXNordic senseneering platform), during the 6 min walking test to obtain spatio-temporal gait parameters. GS was computed based on stride time (temporal symmetry, TS) and stride length (spatial symmetry, SS). Stride length was normalized by height. Kruskal-Wallis and Spearman’s correlation coefficient were used for analyses.

Results: Reports of knee pain did not differ between gender (women 4.7, SD 2.4 vs. men 3.9, SD 2.4, p= 0.362), neither did one leg rises or gait symmetry. Participants who reported unilateral knee pain (left/right side n=9/13), had a shorter stride length on the painful side. The mean difference in stride length was 0.7% of the subject’s height (SD 1.3). Participants with unilateral pain also presented less SS gait than those who reported bilateral pain (p=0.005). The higher number of one-leg rises performed, the better TS was observed. We found a significant relationship between TS and one-leg rise for the right r s =-0.39, p=0.006, and left r s =-0.40, p=0.004 left side). No significant relationship was observed between SS and one-leg rises.

Conclusion: Our results is in line with earlier findings stating that knee pain affects GS negatively and that lower extremity muscle function is an important feature for symmetry and dynamic joint stability in patients with symptomatic knee OA. We also found that pain in one leg was related to impaired GS. Bilateral knee pain was however more symmetrical and will need healthy controls for comparison to better understand the negative impact of symptomatic knee OA.

Place, publisher, year, edition, pages
London, UK: BMJ Publishing Group Ltd, 2019
Keywords
Knee osteoarthritis, joint stability, objective measure
National Category
Physiotherapy
Identifiers
urn:nbn:se:hh:diva-40948 (URN)000472207104307 ()
Conference
Annual European Congress of Rheumatology (EULAR 2019), Madrid, Spain, June 12-15, 2019
Available from: 2019-11-14 Created: 2019-11-14 Last updated: 2019-12-09Bibliographically approved
Calikus, E., Fan, Y., Nowaczyk, S. & Pinheiro Sant'Anna, A. (2019). Interactive-cosmo: Consensus self-organized models for fault detection with expert feedback. In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019: . Paper presented at 1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, Australia; 15 February, 2019 (pp. 1-9). New York: Association for Computing Machinery (ACM)
Open this publication in new window or tab >>Interactive-cosmo: Consensus self-organized models for fault detection with expert feedback
2019 (English)In: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, New York: Association for Computing Machinery (ACM), 2019, p. 1-9Conference paper, Published paper (Refereed)
Abstract [en]

Diagnosing deviations and predicting faults is an important task, especially given recent advances related to Internet of Things. However, the majority of the efforts for diagnostics are still carried out by human experts in a time-consuming and expensive manner. One promising approach towards self-monitoring systems is based on the "wisdom of the crowd" idea, where malfunctioning equipments are detected by understanding the similarities and differences in the operation of several alike systems.

A fully autonomous fault detection, however, is not possible, since not all deviations or anomalies correspond to faulty behaviors; many can be explained by atypical usage or varying external conditions. In this work, we propose a method which gradually incorporates expert-provided feedback for more accurate self-monitoring. Our idea is to support model adaptation while allowing human feedback to persist over changes in data distribution, such as concept drift. © 2019 Association for Computing Machinery.

Place, publisher, year, edition, pages
New York: Association for Computing Machinery (ACM), 2019
Keywords
Anomaly Detection, Self-Monitoring, Active Learning, Human-in- the-loop
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-41365 (URN)10.1145/3304079.3310289 (DOI)2-s2.0-85069779014 (Scopus ID)978-1-4503-6296-2 (ISBN)
Conference
1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, Melbourne, Australia; 15 February, 2019
Available from: 2020-01-10 Created: 2020-01-10 Last updated: 2020-01-30Bibliographically 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
Mashad Nemati, H., Pinheiro Sant'Anna, A., Nowaczyk, S., Jürgensen, J. H. & Hilber, P. (2019). Reliability Evaluation of Power Cables Considering the Restoration Characteristic. International Journal of Electrical Power & Energy Systems, 105, 622-631
Open this publication in new window or tab >>Reliability Evaluation of Power Cables Considering the Restoration Characteristic
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2019 (English)In: International Journal of Electrical Power & Energy Systems, ISSN 0142-0615, E-ISSN 1879-3517, Vol. 105, p. 622-631Article in journal (Refereed) Published
Abstract [en]

In this paper Weibull parametric proportional hazard model (PHM) is used to estimate the failure rate of every individual cable based on its age and a set of explanatory factors. The required information for the proposed method is obtained by exploiting available historical cable inventory and failure data. This data-driven method does not require any additional measurements on the cables, and allows the cables to be ranked for maintenance prioritization and repair actions.

Furthermore, the results of reliability analysis of power cables are compared when the cables are considered as repairable or non-repairable components. The paper demonstrates that the methods which estimate the time-to-the-first failure (for non-repairable components) lead to incorrect conclusions about reliability of repairable power cables.

The proposed method is used to evaluate the failure rate of each individual Paper Insulated Lead Cover (PILC) underground cables in a distribution grid in the south of Sweden. © 2018 Elsevier Ltd

Place, publisher, year, edition, pages
London: Elsevier, 2019
Keywords
Power cable, historical data, reliability, proportional hazard model, preventive maintenance.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:hh:diva-35470 (URN)10.1016/j.ijepes.2018.08.047 (DOI)000449447200055 ()2-s2.0-85053080255 (Scopus ID)
Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2019-03-19Bibliographically approved
Galozy, A., Nowaczyk, S. & Pinheiro Sant'Anna, A. (2019). Towards Understanding ICU Treatments Using Patient Health Trajectories. In: Lect. Notes Comput. Sci.: . Paper presented at 26 June 2019 through 29 June 2019 (pp. 67-81). Springer
Open this publication in new window or tab >>Towards Understanding ICU Treatments Using Patient Health Trajectories
2019 (English)In: Lect. Notes Comput. Sci., Springer, 2019, p. 67-81Conference paper, Published paper (Refereed)
Abstract [en]

Overtreatment or mistreatment of patients is a phenomenon commonly encountered in health care and especially in the Intensive Care Unit (ICU) resulting in increased morbidity and mortality. We explore the MIMIC-III intensive care unit database and conduct experiments on an interpretable feature space based on the fusion of severity subscores, commonly used to predict mortality in an ICU setting. Clustering of medication and procedure context vectors based on a semantic representation has been performed to find common and individual treatment patterns. Two-day patient health state trajectories of a cohort of congestive heart failure patients are clustered and correlated with the treatment and evaluated based on an increase or reduction of probability of mortality on the second day of stay. Experimental results show differences in treatments and outcomes and the potential for using patient health state trajectories as a starting point for further evaluation of medical treatments and interventions. © Springer Nature Switzerland AG 2019.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Clustering, Electronic Health Records, Health trajectory, Intensive care treatments, Knowledge representation, Medical computing, Medical information systems, Patient treatment, Semantics, Trajectories, Congestive heart failures, Context vector, Electronic health record, Intensive care, Medical treatment, Patient health, Semantic representation, Intensive care units
National Category
Health Care Service and Management, Health Policy and Services and Health Economy
Identifiers
urn:nbn:se:hh:diva-41538 (URN)10.1007/978-3-030-37446-4_6 (DOI)2-s2.0-85078449305 (Scopus ID)9783030374457 (ISBN)
Conference
26 June 2019 through 29 June 2019
Available from: 2020-02-04 Created: 2020-02-04 Last updated: 2020-02-04Bibliographically 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)000502537200142 ()31401594 (PubMedID)2-s2.0-85070687111 (Scopus ID)
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: 2020-01-31Bibliographically approved
Menezes, M. L., Pinheiro Sant'Anna, A., Pavel, M., Jimison, H. & Alonso-Fernandez, F. (2018). Affective Ambient Intelligence: from Domotics to Ambient Intelligence. In: A2IC 2018: Artificial Intelligence International Conference: Book of Abstract. Paper presented at Artificial Intelligence International Conference, A2IC 2018, November 21-23, 2018, Barcelona, Spain (pp. 25-25).
Open this publication in new window or tab >>Affective Ambient Intelligence: from Domotics to Ambient Intelligence
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2018 (English)In: A2IC 2018: Artificial Intelligence International Conference: Book of Abstract, 2018, p. 25-25Conference paper, Oral presentation with published abstract (Refereed)
National Category
Signal Processing
Identifiers
urn:nbn:se:hh:diva-38503 (URN)
Conference
Artificial Intelligence International Conference, A2IC 2018, November 21-23, 2018, Barcelona, Spain
Available from: 2018-12-06 Created: 2018-12-06 Last updated: 2018-12-06Bibliographically approved
Projects
Multi-modal emotion recognition from bio-signals [2015-04074_Vinnova]; Halmstad University
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-3495-2961

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