SOURCE-LINKED INTELLIGENCE

Mental Illness Detection and Clinical Assessment with Reliable Interpretability

CORDIS · observation · Publication date unknown

rs is crucial for helping clinicians and patients achieve accurate diagnoses and treatment. Research has long established that certain vocal features are linked to mental health disorders, allowing a machine learning model to learn how to classify different mental disorders using voice. Artificial intelligence (AI) has been providing excellent responses diagnosing mental diseases so far but, remarkably, they often operate as ""black boxes"" that do not allow for an understanding of how they make their decisions. This makes it mandatory to perform a thorough study on the system in order to achieve fairness, explainability and trustworthiness. This project aims to develop an open source AI based model, explainable and transparent, that can assess between many mental illnesses using voice recordings from a patient, contributing from the study, analysis and understanding of the problem, providing greater interpretability, security and extrapolation ability. The project will be divided in three stages, curation of dataset, the development of the system and exhaustive explainability and f

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recordType

award

status

SIGNED

region

EU

value

209914.56

unit

EUR