First, we focus on ontology.
we will use an expert-type artificial intelligence approach to organize psychotherapeutic actions as structured knowledge that can be understood by both computers and humans. We also aim to organize the vocabulary and build a search system. (PI: Takuichi Nishimura, Ph.D.).
Then there is words and sound.
will use words (natural language) that are spoken and written in psychotherapy (PI: Yoshitake Takebayashi, Ph.D.).
will use speech uttered in psychotherapy (PI: Masaya Ito, Ph.D.).
Units A02 and A03 will apply artificial intelligence techniques such as machine learning to the analysis of language and speech data, respectively, to discriminate the mental states of people undergoing psychotherapy and to predict the therapeutic effects of psychotherapy.
Lastly, a network.
we will attempt to elucidate in detail what symptoms are changed by what interventions and through what interactions in the course of psychotherapy by using mathematical statistics for the network theory (PI: Jun Kashihara, Ph.D.).
Comprehensively, this research area aims to leverage multi-modal, big, and precise data in psychotherapy, apply AI technologies to it, and achieve high definition in mental health care: “Ultra-High-Definition Mental Health Care”.
We expect that if we could successfully identify mental states and predict treatment trajectories using this approach, more data modalities such as facial expression, gestures, and bio-physiological data can be taken advantage of. Furthermore, we expected that the technology developed through this research can be applied to other areas that involve communications such as education, welfare, and service industry and benefit beyond the mental health field.