Self-supervision for health insurance claims data: a Covid-19 use case

Emilia Apostolova, Fazle Karim, Guido Muscioni, Anubhav Rana, Jeffrey Clyman

Abstract: In this work, we modify and apply self-supervision techniques to the domain of medical health insurance claims. We model patients' healthcare claims history analogous to free-text narratives, and introduce pre-trained `prior knowledge', later utilized for patient outcome predictions on a challenging task: predicting Covid-19 hospitalization, given a patient's pre-Covid-19 insurance claims history. Results suggest that pre-training on insurance claims not only produces better prediction performance, but, more importantly, improves the model's `clinical trustworthiness' and model stability/reliability.