Learning the progression patterns of treatments using a probabilistic generative model.

Modeling a disease or the treatment of a patient has drawn much attention in recent years due to the vast amount of information that Electronic Health Records contain. This paper presents a probabilistic generative model of treatments that are described in terms of sequences of medical activities of variable length. The main objective is to identify distinct subtypes of treatments for a given disease, and discover their development and progression. To this end, the model considers that a sequence of actions has an associated hierarchical structure of latent variables that both classifies the sequences based on their evolution over time, and segments the sequences into different progression stages. The learning procedure of the model is performed with the Expectation–Maximization algorithm which considers the exponential number of configurations of the latent variables and is efficiently solved with a method based on dynamic programming. The evaluation of the model is twofold: first, we use synthetic data to demonstrate that the learning procedure allows the generative model underlying the data to be recovered; we then further assess the potential of our model to provide treatment classification and staging information in real-world data. Our model can be seen as a tool for classification, simulation, data augmentation and missing data imputation. [Display omitted] • We propose a novel generative model for treatment subtyping and disease staging. • The model is efficiently learned with the Expectation–Maximization algorithm. • Dynamic programming proposed method reduces the complexity of the generative model. • Experiments in synthetic data show that the model underlying the data is recovered. • Results for breast cancer were validated by physicians and medical guidelines. [ABSTRACT FROM AUTHOR]