Articles
Predicting Mental Health Treatment Outcomes Using Advanced Machine Learning Models: Develops machine learning models to predict individual treatment responses for patients with mental health disorders, guiding personalized treatment selection and optimization
Published 24-05-2024
Keywords
- Machine Learning,
- Mental Health Disorders,
- Treatment Response Prediction,
- Personalized Medicine,
- Healthcare
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
[1]
D. F. Tabibi, “Predicting Mental Health Treatment Outcomes Using Advanced Machine Learning Models: Develops machine learning models to predict individual treatment responses for patients with mental health disorders, guiding personalized treatment selection and optimization”, Journal of Machine Learning for Healthcare Decision Support, vol. 4, no. 1, pp. 72–82, May 2024, Accessed: Jan. 22, 2025. [Online]. Available: https://medlines.uk/index.php/JMLHDS/article/view/17
Abstract
This research paper explores the application of machine learning (ML) models in predicting treatment responses for patients with mental health disorders. Mental health disorders present a significant challenge in healthcare due to their complex and heterogeneous nature, often requiring tailored treatment approaches. Traditional methods of treatment selection rely heavily on trial and error, leading to suboptimal outcomes and prolonged suffering for patients.
The use of ML offers a promising avenue for personalized medicine in mental health. By leveraging patient data such as demographic information, clinical history, genetic markers, and treatment outcomes, ML models can identify patterns and predict individual responses to different treatment options. This predictive capability has the potential to revolutionize mental health care by enabling clinicians to make informed decisions, improve treatment efficacy, and minimize adverse effects.
This paper reviews existing literature on ML models for predicting treatment responses in mental health disorders, discusses challenges and limitations, and proposes future directions for research and implementation. The development of accurate and reliable ML models for treatment response prediction has the potential to significantly impact patient outcomes and advance the field of mental health care.
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