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Predicting healthcare trajectories from medical records: a deep learning approach.

Pham, Trang, Tran, Truyen, Phung, Dinh and Venkatesh, Svetha 2017, Predicting healthcare trajectories from medical records: a deep learning approach., Journal of biomedical informatics, vol. 69, pp. 218-229, doi: 10.1016/j.jbi.2017.04.001.

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Title Predicting healthcare trajectories from medical records: a deep learning approach.
Author(s) Pham, Trang
Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, Svetha
Journal name Journal of biomedical informatics
Volume number 69
Start page 218
End page 229
Total pages 12
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2017-05
ISSN 1532-0480
Keyword(s) Electronic medical records
Healthcare processes
Irregular timing
Long-Short Term Memory
Predictive medicine
Summary Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, stored in electronic medical records are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors and models patient health state trajectories by the memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces methods to handle irregularly timed events by moderating the forgetting and consolidation of memory. DeepCare also explicitly models medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden - diabetes and mental health - the results show improved prediction accuracy.
Language eng
DOI 10.1016/j.jbi.2017.04.001
Field of Research 080109 Pattern Recognition and Data Mining
080702 Health Informatics
Socio Economic Objective 0 Not Applicable
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30094319

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