Accurate and Efficient Data-Driven Psychiatric Assessment Using Machine Learning

BackgroundAccurate assessment of mental disorders and learning disabilities is essential for timely intervention. Machine learning and feature selection techniques have demonstrated potential in improving the accuracy and efficiency of mental health assessments. However, limited research has explored the use of large transdiagnostic datasets containing a vast number of items (exceeding 1000), as well as the application of these techniques in developing quick, question-based learning disability assessments. The goals of this study are to apply machine learning and feature selection techniques to a large transdiagnostic dataset featuring a high number of input items, and to create a tool for the streamlined creation of efficient and effective assessment using existing datasets.MethodsThis study leverages the Healthy Brain Network (HBN) dataset to develop a tool for creation of efficient and effective machine learning-based assessment of mental disorders and learning disabilities. Feature selection algorithms were applied to identify parsimonious item subsets. Modular architecture ensures straightforward application to other datasets. ResultsMachine learning models trained on the HBN data exhibited improved performance over existing assessments. Using only non-proprietary assessments did not significantly impact model performance. DiscussionThis study demonstrates the feasibility of using existing large-scale datasets for creating accurate and efficient assessments for mental disorders and learning disabilities. The performance values of the machine learning models provide estimates of the performance of the new assessments in a population similar to HBN. The trained models can be used in a new population after validation and acquiring consent of the authors of the original assessments. The modular architecture of the developed tool ensures seamless application to diverse clinical and research contexts.