Discovering the Causal Structure of the Hamilton Rating Scale for Depression Using Causal Discovery
Abstract
Major depressive disorder (MDD) causes many negative consequences including suicide and disability, and is one of the leading preventable causes of death in many countries. The Hamilton Rating Scale for Depression (HAM-D) used in this paper evaluates depression severity based on 17 symptoms of MDD (i.e., the HAM-D 17 , referred to below as the HAM-D). Studying the relationship amongst MDD symptoms not only provides insight into depressive symptoms but also helps identify subgroups of patients with depression. We employ causal discovery to discover the causal and correlational relationships among HAM-D symptoms. To the best of our knowledge, this is the first study to investigate HAM-D using causal discovery..
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Lu Wang
Assistant Professor of Biomedical Engineering
My research interests are developing and applying Machine Learning, Data Mining and Statistical methods (e.g., Multi-task Learning, Survival Analysis, Clustering, Risk Factor Analysis and Causal Discovery) on various data including gene expression, electronic health/medical records, and DNA sequencing reads for both cognitive disorders (e.g., delirium, Alzheimer’s disease, dementia, major depressive disorder) and chronic diseases (e.g., cancer, obesity, hypertension). Inspired by the human factors approach, she also designs and develops Human-Centered Artificial Intelligence tools for users to integrate, visualize, analyze, and interpret health data in order to improve the interoperability and accessibility of AI-assisted healthcare decision support.