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Readmission prediction using deep learning on electronic health records
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Halland Hospital, Region Halland, 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
Halland Hospital, Region Halland, Sweden & Institute of Medicine, Dept. of Molecular and Clinical Medicine/Cardiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-7796-5201
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. Vol. 97, article id 103256
Keywords [en]
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: urn:nbn:se:hh:diva-39297DOI: 10.1016/j.jbi.2019.103256ISI: 000525699100005PubMedID: 31351136Scopus ID: 2-s2.0-85069858722OAI: oai:DiVA.org:hh-39297DiVA, id: diva2:1308084
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: 2022-02-15Bibliographically 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
2. Deep Evidential Doctor
Open this publication in new window or tab >>Deep Evidential Doctor
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Recent years have witnessed an unparalleled surge in deep neural networks (DNNs) research, surpassing traditional machine learning (ML) and statistical methods on benchmark datasets in computer vision, audio processing and natural language processing (NLP). Much of this success can be attributed to the availability of numerous open-source datasets, advanced computational resources and algorithms. These algorithms learn multiple levels of simple to complex abstractions (or representations) of data resulting in superior performances on downstream applications. This has led to an increasing interest in reaping the potential of DNNs in real-life safety-critical domains such as autonomous driving, security systems and healthcare. Each of them comes with their own set of complexities and requirements, thereby necessitating the development of new approaches to address domain-specific problems, even if building on common foundations.

In this thesis, we address data science related challenges involved in learning effective prediction models from structured electronic health records (EHRs). In particular, questions related to numerical representation of complex and heterogeneous clinical concepts, modelling the sequential structure of EHRs and quantifying prediction uncertainties are studied. From a clinical perspective, the question of predicting onset of adverse outcomes for individual patients is considered to enable early interventions, improve patient outcomes, curb unnecessary expenditures and expand clinical knowledge.

This is a compilation thesis including five articles. It begins by describing a healthcare information platform that encapsulates clinical, operational and financial data of patients across all public care delivery units in Halland, Sweden. Thus, the platform overcomes the technical and legislative data-related challenges inherent to the modern era's complex and fragmented healthcare sector. The thesis presents evidence that expert clinical features are powerful predictors of adverse patient outcomes. However, they are well complemented by clinical concept embeddings; gleaned via NLP inspired language models. In particular, a novel representation learning framework (KAFE: Knowledge And Frequency adapted Embeddings) has been proposed that leverages medical knowledge schema and adversarial principles to learn high quality embeddings of both frequent and rare clinical concepts. In the context of sequential EHR modelling, benchmark experiments on cost-sensitive recurrent nets have shown significant improvements compared to non-sequential networks. In particular, an attention based hierarchical recurrent net is proposed that represents individual patients as weighted sums of ordered visits, where visits are, in turn, represented as weighted sums of unordered clinical concepts. In the context of uncertainty quantification and building trust in models, the field of deep evidential learning has been extended. In particular for multi-label tasks, simple extensions to current neural network architecture are proposed, coupled with a novel loss criterion to infer prediction uncertainties without compromising on accuracy. Moreover, a qualitative assessment of the model behaviour has also been an important part of the research articles, to analyse the correlations learned by the model in relation to established clinical science.

Put together, we develop DEep Evidential Doctor (DEED). DEED is a generic predictive model that learns efficient representations of patients and clinical concepts from EHRs and quantifies its confidence in individual predictions. It is also equipped to infer unseen labels.

Overall, this thesis presents a few small steps towards solving the bigger goal of artificial intelligence (AI) in healthcare. The research has consistently shown impressive prediction performance for multiple adverse outcomes. However, we believe that there are numerous emerging challenges to be addressed in order to reap the full benefits of data and AI in healthcare. For future works, we aim to extend the DEED framework to incorporate wider data modalities such as clinical notes, signals and daily lifestyle information. We will also work to equip DEED with explainability features.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2022. p. 21
Series
Halmstad University Dissertations ; 88
National Category
Computer Sciences
Identifiers
urn:nbn:se:hh:diva-46347 (URN)978-91-88749-85-7 (ISBN)978-91-88749-86-4 (ISBN)
Public defence
2022-03-15, J102 (Wigforss), Visionen, Kristian IV:s väg 3, Halmstad, 13:00 (English)
Opponent
Supervisors
Available from: 2022-02-15 Created: 2022-02-15 Last updated: 2022-05-12Bibliographically approved

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Ashfaq, AwaisPinheiro Sant'Anna, AnitaNowaczyk, Sławomir

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