ZiMM: a deep learning model for long term adverse events with non clinical claims data
This paper considers the problem of modeling long term adverse events following prostatic surgery performed on patients with urination problems, using the French national health insurance database (SNIIRAM) which is a non clinical claims database built around healthcare reimbursements of more than 65 million people. This makes the problem particularly challenging compared to what could be done using clinical hospital data, albeit a much smaller sample, while we exploit here the claims of almost all French citizens diagnosed with prostatic problems (with between 1.5 and 5 years of history) We introduce a new model, called ZiMM (Zero inflated Mixture of Multinomial distributions) to capture such long term adverse events, and we build a deep learning architecture on top of it to deal with the complex, highly heterogeneous and sparse patterns observable in such a large claims database. This architecture combines several ingredients: embedding layers for drugs, medical procedures, and diagnosis codes; embeddings aggregation through a self attention mechanism; recurrent layers to encode the health pathways of patients before their surgery and a final decoder layer which outputs the ZiMM's parameters.