  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.
