Cluster Analysis of Acquired Immunodeficiency Syndrome Patients Mental
WANG Muyu1,2, WANG Ni1,2, HUANG Xiaojie3, CHEN Hui1,2
1. School of Biomedical Engineering, Capital Medical University, Beijing 100069, China;
2. Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Beijing 100069, China;
3. Center for Infectious Diseases, Beijing You’an Hospital, Capital Medical University, Beijing 100069, China
Abstract：Objective To comprehensively evaluate the mental health status of acquired immunodeficiency syndrome patients based
on the anxiety, depression and sleep disorder scales. Methods All item scores of a scale was regarded as a sequence, and the dynamic
time warping (DTW) method was used to calculate the similarity between patients for each scale. The similarity between patients
was obtained by averaging the similarities for all scales. The hierarchical clustering method was used for cluster analysis. Three
indicators, the Calinski Harabasz Index (CHI), Davies Bouldin Index (DBI) and silhouette coefficient (SC) were used to evaluate
the quality of the clusters and to determine the optimal number of clusters. The clusters were labelled according to the distribution
of patients with different mental conditions within each cluster. F1 score was calculated to evaluate the accuracy of the clusters. In
addition, cluster analysis was also performed using Euclidean distance as well as similarity based on a single scale to compare their
clustering quality, accuracy, and clinical interpretability. Results Patients clustering based on scale DTW similarity outperformed the
clustering based on Euclidean distance on all three clustering quality evaluation metrics, CHI, DBI and SC (166.24 vs. 72.68, 2.91 vs.
4.25, and 0.31 vs. 0.16). When clustering using DTW similarity as a distance measure, the F1 value for clustering using three scale
scores for anxiety, depression and sleep disorders (0.739) was higher than that for clustering using a single scale score (0.618, 0.695,
and 0.693). Conclusion Cluster analysis based on the sequence similarity of multiple scales has shown good performance in patient
clustering. It can be helpful in screening and assessing the mental health status of acquired immunodeficiency syndrome patients.
