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. Attached Files Name Description MIMEType Size Downloads Title Predicting healthcare trajectories from medical records: a deep learning approach. Author(s) Pham, Trang Tran, Truyen orcid.org/0000-0001-6531-8907 Phung, Dinh orcid.org/0000-0002-9977-8247 Venkatesh, Svetha orcid.org/0000-0001-8675-6631 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 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Centre for Pattern Recognition and Data Analytics (PRADA) Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 26 Apr 2017, 10:01:33 EST Thu, 27 Apr 2017, 05:00:19 EST Thu, 27 Apr 2017, 07:49:03 EST Sat, 13 May 2017, 02:33:38 EST Fri, 19 May 2017, 13:03:59 EST Sat, 20 May 2017, 06:05:35 EST Fri, 02 Jun 2017, 15:33:18 EST Fri, 02 Jun 2017, 15:36:46 EST Sat, 07 Oct 2017, 01:30:09 EST Thu, 21 Feb 2019, 08:13:21 EST Filtered Full Citation counts:   Cited 35 times in TR Web of Science Cited 44 times in Scopus Search Google Scholar Access Statistics: 520 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Fri, 19 May 2017, 13:03:59 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.