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Friday, August 28, 2015

Computer Diagnosis of Schizophrenia

Computers Can Predict Schizophrenia Based on How a Person Talks

A new study finds an algorithmic word analysis is flawless at determining whether a person will have a psychotic episode.

Adrienne LaFrance | August 26, 2015


...But in psychiatry, much attention is paid to such intricacies of thinking. For instance, disorganized thought, evidenced by disjointed patterns in speech, is considered a hallmark characteristic of schizophrenia. Several studies of at-risk youths have found that doctors are able to guess with impressive accuracy—the best predictive models hover around 79 percent—whether a person will develop psychosis based on tracking that person’s speech patterns in interviews.
A computer, it seems, can do better.

Could brain scans help predict schizophrenia? Source: http://www.cardiff.ac.uk/news/view/100837-advanced-mri-scans-could-help-predict-people-at-risk-of-schizophrenia

<more at http://www.theatlantic.com/technology/archive/2015/08/speech-analysis-schizophrenia-algorithm/402265/; related links: Sourceable.net/libraries-reclaiming-their/ (Could brain scans help predict schizophrenia? May 11, 2015) and https://www.inverse.com/article/5655-this-computer-can-detect-schizophrenia-by-listening-to-you-talk (This Computer Can Detect Schizophrenia by Listening to You Talk; A way to predict psychosis in at-risk young people. August 26, 2015); further: http://www.nature.com/articles/npjschz201530 (Automated analysis of free speech predicts psychosis onset in high-risk youths. Gillinder Bedi, Facundo Carrillo, Guillermo A Cecchi, Diego Fern├índez Slezak, Mariano Sigman, Nat├ília B. Mota, Sidarta Ribeiro, Daniel C. Javitt, Mauro Copelli and Cheryl M. Corcoran. doi:10.1038/npjschz.2015.30. [Abstract: Background/Objectives: Psychiatry lacks the objective clinical tests routinely used in other specializations. Novel computerized methods to characterize complex behaviors such as speech could be used to identify and predict psychiatric illness in individuals. AIMS: In this proof-of-principle study, our aim was to test automated speech analyses combined with Machine Learning to predict later psychosis onset in youths at clinical high-risk (CHR) for psychosis. Methods: Thirty-four CHR youths (11 females) had baseline interviews and were assessed quarterly for up to 2.5 years; five transitioned to psychosis. Using automated analysis, transcripts of interviews were evaluated for semantic and syntactic features predicting later psychosis onset. Speech features were fed into a convex hull classification algorithm with leave-one-subject-out cross-validation to assess their predictive value for psychosis outcome. The canonical correlation between the speech features and prodromal symptom ratings was computed. Results: Derived speech features included a Latent Semantic Analysis measure of semantic coherence and two syntactic markers of speech complexity: maximum phrase length and use of determiners (e.g., which). These speech features predicted later psychosis development with 100% accuracy, outperforming classification from clinical interviews. Speech features were significantly correlated with prodromal symptoms. Conclusions: Findings support the utility of automated speech analysis to measure subtle, clinically relevant mental state changes in emergent psychosis. Recent developments in computer science, including natural language processing, could provide the foundation for future development of objective clinical tests for psychiatry.]>

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