GP-HD: Using Genetic Programming to Generate Dynamical Systems Models for Health Care
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Abstract
The huge wealth of data in the health domain can be exploited to create models that predict development of health states over time. Temporal learning algorithms are well suited to learn relationships between health states and make predictions about their future developments. However, these algorithms: (1) either focus on learning one generic model for all patients, providing general insights but often with limited predictive performance, or (2) learn individualized models from which it is hard to derive generic concepts. In this paper, we present a middle ground, namely parameterized dynamical systems models that are generated from data using a Genetic Programming (GP) framework. A fitness function suitable for the health domain is exploited. An evaluation of the approach in the mental health domain shows that performance of the model generated by the GP is on par with a dynamical systems model developed based on domain knowledge, significantly outperforms a generic Long Term Short Term Memory (LSTM) model and in some cases also outperforms an individualized LSTM model.
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Published In
October 2019
507 pages
ISBN:9781450369343
DOI:10.1145/3350546
Copyright © 2019 ACM.
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Published: 14 October 2019
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WI '19
WI '19: IEEE/WIC/ACM International Conference on Web Intelligence
October 14 - 17, 2019
Thessaloniki, Greece
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Overall Acceptance Rate 118 of 178 submissions, 66%
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