Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning

• URL: http://arxiv.org/abs/2104.01787v2
• Date: Tue, 6 Apr 2021 03:02:09 GMT
• Title: Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learning
• Authors: Jeong Min Lee and Milos Hauskrecht
• Abstract summary: Clinical event sequences consist of thousands of clinical events that represent records of patient care in time. One important challenge of learning a good predictive model of clinical sequences is patient-specific variability. We develop a new adaptive event sequence prediction framework that learns to adjust its prediction for individual patients through an online model update.
• Score: 11.574235466142833
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Clinical event sequences consist of thousands of clinical events that represent records of patient care in time. Developing accurate prediction models for such sequences is of a great importance for defining representations of a patient state and for improving patient care. One important challenge of learning a good predictive model of clinical sequences is patient-specific variability. Based on underlying clinical complications, each patient's sequence may consist of different sets of clinical events. However, population-based models learned from such sequences may not accurately predict patient-specific dynamics of event sequences. To address the problem, we develop a new adaptive event sequence prediction framework that learns to adjust its prediction for individual patients through an online model update.

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