{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deepcare-a-deep-dynamic-memory-model-for","title":"DeepCare: A Deep Dynamic Memory Model for Predictive Medicine","arxiv_id":"1602.00357","date":"2016-02-01","proceeding":null,"authors":["Trang Pham","Truyen Tran","Dinh Phung","Svetha Venkatesh"],"abstract":"Personalized predictive medicine necessitates the modeling of patient illness\nand care processes, which inherently have long-term temporal dependencies.\nHealthcare observations, recorded in electronic medical records, are episodic\nand irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural\nnetwork that reads medical records, stores previous illness history, infers\ncurrent illness states and predicts future medical outcomes. At the data level,\nDeepCare represents care episodes as vectors in space, models patient health\nstate trajectories through explicit memory of historical records. Built on Long\nShort-Term Memory (LSTM), DeepCare introduces time parameterizations to handle\nirregular timed events by moderating the forgetting and consolidation of memory\ncells. DeepCare also incorporates medical interventions that change the course\nof illness and shape future medical risk. Moving up to the health state level,\nhistorical and present health states are then aggregated through multiscale\ntemporal pooling, before passing through a neural network that estimates future\noutcomes. We demonstrate the efficacy of DeepCare for disease progression\nmodeling, intervention recommendation, and future risk prediction. On two\nimportant cohorts with heavy social and economic burden -- diabetes and mental\nhealth -- the results show improved modeling and risk prediction accuracy.","url_abs":"http://arxiv.org/abs/1602.00357v2","url_pdf":"http://arxiv.org/pdf/1602.00357v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deepcare-a-deep-dynamic-memory-model-for","repo_url":"https://github.com/trangptm/DeepCare","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1602.00357","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}