Sensing Psychosis: Toward Robust Computational Phenotypes in Severe Mental Illness

Explore a comprehensive lecture on computational psychiatry and its potential to revolutionize mental health care. Delve into the challenges of applying personalized medicine in psychiatry and learn how pervasive computing offers unprecedented opportunities to build dynamic models of mental illness. Discover how unobtrusive, quantitative behavioral phenotyping strategies can transform the understanding of causal relationships between illness fluctuations, contextual factors, and treatment interventions. Examine recent efforts to bridge complementary approaches through single-case experimental designs in individuals with severe mental illnesses such as bipolar disorder and schizophrenia. Gain insights into the methodology, including scales of data, simultaneous signals, motion sequencing, and state-space models, that contribute to developing robust computational phenotypes in severe mental illness.