The latest People Who Read People podcast episode is an interview with Dr. Neguine Rezaii, a psychiatrist and psychology researcher, about her team’s 2019 research using machine learning finding speech patterns in young adults that were predictive of later psychosis and schizophrenia diagnosis. The two language patterns found in the subjects’ speech were 1) a low semantic density (i.e., low meaning), and 2) speech related to sound or voices. Here’s a good article about this work: Machine learning approach predicts emergence of psychosis.
Links to this episode:
Topics discussed include:
- How exactly they determined “low semantic density” (low meaning in speech content)
- How the algorithm found, on its own, indicators related to sound-related speech content
- The future of using machine learning and automatic diagnosis tools in psychology and therapy
- Theories that might help explain these findings
Content mentioned in podcast, or related content:
- The whisper of schizophrenia: Machine learning finds ‘sound’ words predict psychosis
- NIH article: Language patterns may predict psychosis
- Great book on schizophrenia: Hidden Valley Road, about a family that had 6 boys end up diagnosed with schizophrenia
- Work by Elaine Walker (part of Rezaii’s team for this research) finding behavior in children in home videos linked to later psychosis
- Information about how sensory gating issues may be related to schizophrenia
- Vice article about how people born blind don’t get schizophrenia
One reply on “Can you predict schizophrenia by analyzing language?, with Dr. Neguine Rezaii”
[…] is a reshare of a 2020 talk I did with psychology researcher Neguine Rezaii. We talk about her research using machine learning to find patterns in language used by teenagers […]