Predicting healthcare trajectories from medical records: a deep learning approach
Open accessAbstract
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.
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In a cohort of diabetic and mental health patients, DeepCare was demonstrated to predict disease progression, optimal interventions, and likelihood for readmission.
“Studying data from a cohort of diabetic and mental health patients it was demonstrated that DeepCare could predict the progression of disease, optimal interventions, and assessing the likelihood for readmission [37].”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [37]
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- Authors
- Pham, T., Tran, T., Phung, D., & Venkatesh, S.
- Affiliations
- Deakin University (AU)
- Corresponding author
- Pham, Trang
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2017-05
- Volume
- 69
- Pages
- 218-229
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W2607113351
- Cited by
- 447
- References
- 53
- Citations by year
- 2026: 16 · 2025: 37 · 2024: 40 · 2023: 57 · 2022: 76 · 2021: 68 · 2020: 81 · 2019: 36 · 2018: 32 · 2017: 4
- FWCI
- 24.95
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- Telstra-Deakin Centre of Excellence in Big Data and Machine Learning
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- electronic health records, long short-term memory, disease progression modeling, intervention recommendations, diabetes mellitus, mental health
- MeSH terms
- Delivery of Health Care, Health Status, Humans, Neural Networks, Computer, Disease Progression, Electronic Health Records
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