| 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 models: (1) either focus on one generic model for all patients, providing general insights but often with limited predictive performance, or (2) are individualized models from which it is hard to derive generic concepts. In this presentation, I will present a middle ground, namely parameterized dynamical systems models that are generated from data using a genetic programming (GP) framework we introduce. A fitness function suitable for the health domain is defined. 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 LSTM model and in some cases also outperforms an individualized LSTM model. |