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ZiMM : a deep learning model for long term adverse events with non-clinical claims data

Kabeshova, Anastasiia; Yu, Yiyang; Lukacs, Bertrand; Bacry, Emmanuel; Gaïffas, Stéphane (2020), ZiMM : a deep learning model for long term adverse events with non-clinical claims data, Journal of biomedical informatics, 110. 10.1016/j.jbi.2020.103531

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Type
Article accepté pour publication ou publié
Date
2020
Journal name
Journal of biomedical informatics
Volume
110
Publication identifier
10.1016/j.jbi.2020.103531
Metadata
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Author(s)
Kabeshova, Anastasiia
Centre de Mathématiques Appliquées - Ecole Polytechnique [CMAP]
Yu, Yiyang
Laboratoire de Probabilités, Statistique et Modélisation [LPSM (UMR_8001)]
Lukacs, Bertrand
Bacry, Emmanuel cc
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Gaïffas, Stéphane
Centre de Mathématiques Appliquées - Ecole Polytechnique [CMAP]
Laboratoire de Probabilités, Statistique et Modélisation [LPSM (UMR_8001)]
Abstract (EN)
This paper considers the problems of modeling and predicting a long-term and "blurry" relapse that occurs after a medical act, such as a surgery. We do not consider a short-term complication related to the act itself, but a long-term relapse that clinicians cannot explain easily, since it depends on unknown sets or sequences of past events that occurred before the act. The relapse is observed only indirectly, in a "blurry" fashion, through longitudinal prescriptions of drugs over a long period of time after the medical act. We introduce a new model, called ZiMM (Zero-inflated Mixture of Multinomial distributions) in order to capture long-term and blurry relapses. On top of it, we build an end-to-end deep-learning architecture called ZiMM Encoder-Decoder (ZiMM ED) that can learn from the complex, irregular, highly heterogeneous and sparse patterns of health events that are observed through a claims-only database. ZiMM ED is applied on a "non-clinical" claims database, that contains only timestamped reimbursement codes for drug purchases, medical procedures and hospital diagnoses, the only available clinical feature being the age of the patient. This setting is more challenging than a setting where bedside clinical signals are available. Indeed, we consider a dataset containing the claims of almost all French citizens who had surgery for prostatic problems, with a history between 1.5 and 5 years. We consider a long-term (18 months) relapse (urination problems still occur despite surgery), which is blurry since it is observed only through the reimbursement of a specific set of drugs for urination problems. Our experiments show that ZiMM ED improves several baselines, including non-deep learning and deep-learning approaches, and that it allows working on such a dataset with minimal preprocessing work.
Subjects / Keywords
Deep learning; Mixture models; Zero-inflation; Electronic health records; Claims datasets; EHR data

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