A Computing System for Complex Cases of Major Recurrent Depression Based on Latent Semantic Analysis: Relationship between Life Themes and Symptoms

Abstract

The paper presents a computing procedure with the goal of suggesting applicable solutions to improve complex cases of major recurrent depression. The focus is on identifying the patients’ illness patterns and on finding solutions for alleviating problematic symptoms. The illness patterns synthesize the outcomes of the relationship between the patients’ life themes and symptoms. The testing of the effectiveness of illness improvement solutions was conducted by evaluating and comparing the Beck scores of patients after each psychotherapy session. In addition to latent semantic analysis used to identify semantic relationships between life themes and symptoms, the research also employed the correlation method to find life themes/symptoms that are experienced undistortedly and associations between life themes that amplify latent symptoms. The computing system was applied to eleven patients with severe forms of depression and their progress was monitored for six months. The results obtained following the application of the computing system demonstrated its ability to describe personalized illness patterns and to significantly improve, through its suggestions, the illness of all patients. These findings recommend the use of the computing system in severe cases of major recurrent depression.

Keywords

• computing system
• cosine similarity
• dimensionality reduction
• illness pattern
• latent life theme
• latent semantic analysis