Transforming Mental Health Care through Clinical Decision Support System Surface Technology

Mental health diseases rank high in terms of global disease burden, and many patients are not diagnosed and treated adequately since only about one third receive proper treatment. This research has developed a high accuracy CDSS that utilizes deep learning algorithms, Natural Language Processing, and probabilistic graphical models. Due to the integration of patient-specific data along with clinical history and genetic factors and application of latest clinical guideline updates to it, the effectiveness is very high in terms of diagnosis and treatment related suggestions for every patient. First measurements suggest diagnostic accuracy to be above 90%, a treatment delay reduction of 35% and a significantly better comorbidity management. This work has the vision to fill the existing gaps in the mental health care by providing accurate, measurable, and available solutions that augment the efficacy of the mental health services in the world and enable clinicians to make quicker and more accurate decisions.