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

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1911.05346.pdf (2.596Mb)
Date
2019
Publisher city
Paris
Collection title
Cahier de recherche CEREMADE, Université Paris Dauphine-PSL
Link to item file
https://arxiv.org/abs/1911.05346
Dewey
Traitement du signal
Sujet
relapse; ZiMM
URI
https://basepub.dauphine.fr/handle/123456789/20689
Collections
  • CEREMADE : Publications
Metadata
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Author
Kabeshova, Anastasiia
89626 Centre de Mathématiques Appliquées - Ecole Polytechnique [CMAP]
Yu, Yiyang
542130 Laboratoire de Probabilités, Statistiques et Modélisations [LPSM (UMR_8001)]
Lukacs, B.
Bacry, Emmanuel
60 CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Gaïffas, Stéphane
542130 Laboratoire de Probabilités, Statistiques et Modélisations [LPSM (UMR_8001)]
Type
Document de travail / Working paper
Item number of pages
26
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.

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