_:n8798c06f26af4e279b9770aef7c41418xb41
<http://schema.org/description>
"The huge wealth of data in the health domain can be exploited to create\nmodels that predict development of health states over time. Temporal learning\nalgorithms are well suited to learn relationships between health states and\nmake predictions about their future developments. However, these algorithms:\n(1) either focus on learning one generic model for all patients, providing\ngeneral insights but often with limited predictive performance, or (2) learn\nindividualized models from which it is hard to derive generic concepts. In this\npaper, we present a middle ground, namely parameterized dynamical systems\nmodels that are generated from data using a Genetic Programming (GP) framework.\nA fitness function suitable for the health domain is exploited. An evaluation\nof the approach in the mental health domain shows that performance of the model\ngenerated by the GP is on par with a dynamical systems model developed based on\ndomain knowledge, significantly outperforms a generic Long Term Short Term\nMemory (LSTM) model and in some cases also outperforms an individualized LSTM\nmodel."
<https://deepai.org/profile/mark-hoogendoorn>