hh.sePublikasjoner
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Predicting clinical outcomes via machine learning on electronic health records
Högskolan i Halmstad, Akademin för informationsteknologi, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR Centrum för tillämpade intelligenta system (IS-lab).ORCID-id: 0000-0001-5688-0156
2019 (engelsk)Licentiatavhandling, med artikler (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Halmstad: Halmstad University Press, 2019.
Serie
Halmstad University Dissertations ; 58
HSV kategori
Identifikatorer
URN: urn:nbn:se:hh:diva-39309ISBN: 978-91-88749-24-6 (tryckt)ISBN: 978-91-88749-25-3 (digital)OAI: oai:DiVA.org:hh-39309DiVA, id: diva2:1313100
Presentation
2019-05-23, R4318, R Building, Halmstad University, Halmstad, Sweden, 13:00 (engelsk)
Opponent
Veileder
Tilgjengelig fra: 2019-05-06 Laget: 2019-05-02 Sist oppdatert: 2019-05-06bibliografisk kontrollert
Delarbeid
1. Data Profile: Regional Healthcare Information Platform in Halland, Sweden
Åpne denne publikasjonen i ny fane eller vindu >>Data Profile: Regional Healthcare Information Platform in Halland, Sweden
Vise andre…
2019 (engelsk)Inngår i: International Journal of Epidemiology, ISSN 0300-5771, E-ISSN 1464-3685Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Oxford: Oxford University Press, 2019
HSV kategori
Identifikatorer
urn:nbn:se:hh:diva-39308 (URN)
Tilgjengelig fra: 2019-05-02 Laget: 2019-05-02 Sist oppdatert: 2019-05-03
2. Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population based registry study
Åpne denne publikasjonen i ny fane eller vindu >>Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population based registry study
Vise andre…
2019 (engelsk)Inngår i: BMJ Open, ISSN 2044-6055, E-ISSN 2044-6055, Vol. 9, nr 8, artikkel-id e028015Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
London: BMJ Publishing Group Ltd, 2019
HSV kategori
Identifikatorer
urn:nbn:se:hh:diva-39307 (URN)10.1136/bmjopen-2018-028015 (DOI)31401594 (PubMedID)
Merknad

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.

Tilgjengelig fra: 2019-05-02 Laget: 2019-05-02 Sist oppdatert: 2019-08-15bibliografisk kontrollert
3. Readmission prediction using deep learning on electronic health records
Åpne denne publikasjonen i ny fane eller vindu >>Readmission prediction using deep learning on electronic health records
2019 (engelsk)Inngår i: Journal of Biomedical Informatics, ISSN 1532-0464, E-ISSN 1532-0480, Vol. 97, artikkel-id 103256Artikkel i tidsskrift (Fagfellevurdert) 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

sted, utgiver, år, opplag, sider
Maryland Heights, MO: Academic Press, 2019
Emneord
Electronic health records, Readmission prediction, Long short-term memory networks, Contextual embeddings
HSV kategori
Identifikatorer
urn:nbn:se:hh:diva-39297 (URN)10.1016/j.jbi.2019.103256 (DOI)31351136 (PubMedID)2-s2.0-85069858722 (Scopus ID)
Prosjekter
HiCube - behovsmotiverad hälsoinnovation
Forskningsfinansiär
European Regional Development Fund (ERDF)
Merknad

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.

Tilgjengelig fra: 2019-04-30 Laget: 2019-04-30 Sist oppdatert: 2019-09-10bibliografisk kontrollert

Open Access i DiVA

Lic(974 kB)278 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 974 kBChecksum SHA-512
c20ed2af1d393ffee505a74d69da1c2509c14c1d23d69b31d5c46704750dcee537eaba61e3901a5f4b64d96932bb2472d16db438c900483144e47b9403d6ad1c
Type fulltextMimetype application/pdf

Personposter BETA

Ashfaq, Awais

Søk i DiVA

Av forfatter/redaktør
Ashfaq, Awais
Av organisasjonen

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 278 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

isbn
urn-nbn

Altmetric

isbn
urn-nbn
Totalt: 789 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf