Machine Learning Approach in Predicting Treatment Response in Emotionally Unstable Personality Disorder
DOI:
https://doi.org/10.70454/IJMRE.2024.40304Keywords:
Emotionally Unstable Personality Disorder, Machine Learning, Personality Disorder, Treatment Response Prediction, Data-Driven Decision Making, PsychotherapyAbstract
Emotionally Unstable Personality Disorder (EUPD) presents unique challenges due to its complex and heterogeneous nature, often leading to varied treatment responses. This exploratory study aims to develop and assess the feasibility of a machine learning model for predicting treatment response in individuals diagnosed with EUPD. The research was conducted by a clinical psychologist with expertise in machine learning and treatment outcome analysis. Retrospective clinical data from 15 individuals diagnosed with EUPD were analyzed using advanced machine-learning techniques. Demographic information, clinical assessment, and treatment history served as predictors of treatment response. Supervised learning algorithms, including random forest and neural networks, were employed to identify patterns and relationships within the data. Cross-validation and bootstrapping techniques enhanced the models’ performance and generalizability.
The sample consisted of 15 individuals with EUPD who had received treatment and had comprehensive records available. Ethical guidance was strictly followed, with informed consent obtained and participant privacy protected through rigorous anonymization procedures. The data was stored to maintain confidentiality. Treatment response was assessed using outcome measures such as symptom improvement, remission rates, and quality of life evaluations. The machine learning model aimed to identify predictors of treatment success and provide insights into the complex dynamics influencing treatment outcomes. The results indicated the development of a promising predictive model with preliminary accuracy. The model showed potential in predicting treatment response, offering initial guidance for clinicians in obtaining treatment planning and enhancing patient well-being. While the sample size was limited, the exploratory study contributes to the growing precision of the mental health care field. It underscores the feasibility of utilizing machine learning to personalize interventions for individuals with EUPD.
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Copyright (c) 2024 Padma C Shaji, Anviti Gupta (Author)
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