Exploratory analysis of longitudinal data of patients with dementia through unsupervised techniques

Dementia is a set of mental diseases affecting millions of people worldwide. Similarly to all the other mental health issues, it is often difficult to forecast the trend of the disease for patients suffering from it. In this context, data of patients suffering from mental health are usually collected through questionnaires, psychological and cognitive tests, over several timepoints. This way, longitudinal data can help identify disease trajectories and allow medical doctors to forecast specific treatments. In this study, we analyze an open, unrestricted dataset of electronic health records (EHRs) of patients suffering from dementia, called OASIS-2, through several unsupervised machine learning methods (К-means, Hierarchical Clustering, Gaussian Mixture Model, and Spectral Clustering). This dataset contains demographic data and psychological test data collected over five independent visits, and having 142 patients at the first visit and ten features. Our goal is to identify patients’ clusters that stay stable over the first four visits (we discarded the data of the fifth visit because of its small size), and then to characterize these clusters by studying their variables. We also measure the performances of the clustering methods through conventional metrics for internal and external validation. Our preliminary results show that unsupervised techniques can identify significant clusters of patients with mental health issues in this dataset and that Hierarchical Clustering outperforms the other algorithms to this end.