Selected article for: "model performance and prediction model performance"
Author: Apostolova, Emilia; Karim, Fazle; Muscioni, Guido; Rana, Anubhav; Clyman, Jeffrey
Title: Self-supervision for health insurance claims data: a Covid-19 use case
  • Cord-id: fjqnjhtd
  • Document date: 2021_7_19
    • ID: fjqnjhtd
    Snippet: 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 performan
    Document: 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.

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