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2021 | OriginalPaper | Chapter

Neural Clinical Event Sequence Prediction Through Personalized Online Adaptive Learning

Authors : Jeong Min Lee, Milos Hauskrecht

Published in: Artificial Intelligence in Medicine

Publisher: Springer International Publishing

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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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previous chapter Detecting Mild Cognitive Impairment Using Smooth Pursuit and a Modified Corsi Task
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Metadata
Title
Neural Clinical Event Sequence Prediction Through Personalized Online Adaptive Learning
Authors
Jeong Min Lee
Milos Hauskrecht
Copyright Year
2021
Book
Artificial Intelligence in Medicine

Print ISBN: 978-3-030-77210-9

Electronic ISBN: 978-3-030-77211-6

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-77211-6_20

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