skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records

Scientific reports, 2016-05, Vol.6 (1), p.26094-26094, Article 26094 [Peer Reviewed Journal]

Copyright Nature Publishing Group May 2016 ;Copyright © 2016, Macmillan Publishers Limited 2016 Macmillan Publishers Limited ;ISSN: 2045-2322 ;EISSN: 2045-2322 ;DOI: 10.1038/srep26094 ;PMID: 27185194

Full text available

Citations Cited by
  • Title:
    Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records
  • Author: Miotto, Riccardo ; Li, Li ; Kidd, Brian A ; Dudley, Joel T
  • Subjects: Automatic Data Processing ; Biostatistics ; Data processing ; Decision making ; Diabetes mellitus ; Electronic Health Records ; Electronic medical records ; Humans ; Learning algorithms ; Machine Learning ; Mental disorders ; Patients ; Prognosis ; Schizophrenia
  • Is Part Of: Scientific reports, 2016-05, Vol.6 (1), p.26094-26094, Article 26094
  • Description: Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name "deep patient". We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.
  • Publisher: England: Nature Publishing Group
  • Language: English
  • Identifier: ISSN: 2045-2322
    EISSN: 2045-2322
    DOI: 10.1038/srep26094
    PMID: 27185194
  • Source: Open Access: PubMed Central
    AUTh Library subscriptions: ProQuest Central
    MEDLINE
    DOAJ Directory of Open Access Journals
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait