Accurate and efficient data-driven psychiatric assessment using machine learning

Background Accurate assessment of mental disorders and learning disabilities is essential for timely intervention. Machine learning and feature selection techniques have potential in improving the accuracy and efficiency of mental health assessments. However, limited research has explored the use of large transdiagnostic datasets, as well as the application of these techniques in developing quick, question-based 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 assessment items, and to create a tool for the streamlined creation of efficient and effective assessment using existing datasets. Methods This study uses 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. Results Machine learning models trained on the HBN data exhibited improved performance over existing assessments. Using only non-proprietary assessments did not significantly impact model performance. Discussion This study demonstrates the feasibility of using existing large-scale datasets for creating accurate and efficient assessments for mental disorders and learning disabilities. The performance of the machine learning models provide estimates of the performance of the new assessments in a population similar to HBN. The modular architecture of the developed tool ensures seamless application to diverse clinical and research contexts.