AI is making significant strides in mental health care. This chapter explores the potential of machine learning, a subset of AI, to transform mental health care by delivering more accurate predictions (e.g. suicide, relapse, etc.) and revealing hidden patterns (e.g. similarities between lived experiences). It begins with a personal narrative highlighting mental health professionals’ challenges in delivering personalised care and demonstrates how machine learning can help overcome these hurdles. The chapter delves into the two main types of machine learning used in mental health research: supervised learning and unsupervised learning. In the section on supervised learning, the chapter provides practical examples of how it can be utilised to predict outcomes such as treatment efficacy, risk of relapse, suicide risk, and medication response. It discusses techniques like feature importance, particularly SHAP values, which help interpret the factors that most significantly influence these predictions. These examples illustrate how practitioners can employ supervised learning to make informed decisions about patient care, while researchers can use it to identify the underlying predictors of mental health issues. The discussion then shifts to unsupervised learning, which focuses on identifying patterns and grouping similar data without predefined outcomes. This approach is valuable for discovering new symptom clusters, understanding treatment pathways, and uncovering hidden risk factors. Real-world applications, such as clustering similar patients or identifying common themes in therapy sessions, are presented to showcase the utility of unsupervised learning in the field of mental health. The chapter also emphasises the importance of high-quality data for developing effective machine learning models. It highlights various data sources, including clinical records, surveys, wearable devices, and public databases. Additionally, the text addresses the challenges of learning and implementing machine learning in mental health, stressing the need for specialised training tailored to mental health professionals. To bridge this gap, the author provides several training resources, including online courses, a book, and workshops designed to equip mental health practitioners with the necessary skills to apply machine learning effectively. The chapter concludes by highlighting the significant potential of machine learning to enhance mental health care and improve patient outcomes, provided it is used with appropriate training, ethical considerations, and integration into existing systems.

How to Apply Machine Learning (AI) in Mental Health Care?

章节
发表日期：

2026年

查看全文

Chandril Ghosh

摘要

AI is making significant strides in mental health care. This chapter explores the potential of machine learning, a subset of AI, to transform mental health care by delivering more accurate predictions (e.g. suicide, relapse, etc.) and revealing hidden patterns (e.g. similarities between lived experiences). It begins with a personal narrative highlighting mental health professionals’ challenges in delivering personalised care and demonstrates how machine learning can help overcome these hurdles. The chapter delves into the two main types of machine learning used in mental health research: supervised learning and unsupervised learning. In the section on supervised learning, the chapter provides practical examples of how it can be utilised to predict outcomes such as treatment efficacy, risk of relapse, suicide risk, and medication response. It discusses techniques like feature importance, particularly SHAP values, which help interpret the factors that most significantly influence these predictions. These examples illustrate how practitioners can employ supervised learning to make informed decisions about patient care, while researchers can use it to identify the underlying predictors of mental health issues. The discussion then shifts to unsupervised learning, which focuses on identifying patterns and grouping similar data without predefined outcomes. This approach is valuable for discovering new symptom clusters, understanding treatment pathways, and uncovering hidden risk factors. Real-world applications, such as clustering similar patients or identifying common themes in therapy sessions, are presented to showcase the utility of unsupervised learning in the field of mental health. The chapter also emphasises the importance of high-quality data for developing effective machine learning models. It highlights various data sources, including clinical records, surveys, wearable devices, and public databases. Additionally, the text addresses the challenges of learning and implementing machine learning in mental health, stressing the need for specialised training tailored to mental health professionals. To bridge this gap, the author provides several training resources, including online courses, a book, and workshops designed to equip mental health practitioners with the necessary skills to apply machine learning effectively. The chapter concludes by highlighting the significant potential of machine learning to enhance mental health care and improve patient outcomes, provided it is used with appropriate training, ethical considerations, and integration into existing systems.