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Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population based registry study
Department of Clinical Sciences, Lund University, Lund, Sweden.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Halland Hospital, Region Halland, Halmstad, Sweden.ORCID iD: 0000-0001-5688-0156
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-3495-2961
Department of Emergency Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA & Harvard Medical School, Boston, Massachusetts, USA.
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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. Vol. 9, no 8, article id e028015
National Category
Social and Clinical Pharmacy
Identifiers
URN: urn:nbn:se:hh:diva-39307DOI: 10.1136/bmjopen-2018-028015PubMedID: 31401594OAI: oai:DiVA.org:hh-39307DiVA, id: diva2:1313022
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
In thesis
1. Predicting clinical outcomes via machine learning on electronic health records
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

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Ashfaq, AwaisPinheiro Sant'Anna, Anita

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