title: Automated analysis of free speech predicts psychosis onset in high-risk youths.
abstract: OBJECTIVE
  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.
  
  
  OBJECTIVE
  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.
authors.affiliation: Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA; Division on Substance Abuse, New York State Psychiatric Institute, New York, NY, USA.
authors.forename: Gillinder
authors.initials: G
authors.lastname: Bedi
authors.affiliation: Department of computer Science, School of Sciences, Universidad de Buenos Aires , Buenos Aires, Argentina.
authors.forename: Facundo
authors.initials: F
authors.lastname: Carrillo
authors.affiliation: Computational Biology Center-Neuroscience, IBM T.J. Watson Research Center , Yorktown Heights, NY, USA.
authors.forename: Guillermo A
authors.initials: GA
authors.lastname: Cecchi
authors.affiliation: Department of computer Science, School of Sciences, Universidad de Buenos Aires , Buenos Aires, Argentina.
authors.forename: Diego Fernández
authors.initials: DF
authors.lastname: Slezak
authors.affiliation: Department of Physics, School of Sciences, Universidad de Buenos Aires , Buenos Aires, Argentina.
authors.forename: Mariano
authors.initials: M
authors.lastname: Sigman
authors.affiliation: Brain Institute, Federal University of Rio Grande do Norte , Natal, Brazil.
authors.forename: Natália B
authors.initials: NB
authors.lastname: Mota
authors.affiliation: Brain Institute, Federal University of Rio Grande do Norte , Natal, Brazil.
authors.forename: Sidarta
authors.initials: S
authors.lastname: Ribeiro
authors.affiliation: Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA; Division of Experimental Therapeutics, New York State Psychiatric Institute, New York, NY, USA.
authors.forename: Daniel C
authors.initials: DC
authors.lastname: Javitt
authors.affiliation: Department of Physics, Federal University of Pernambuco , Recife, Brazil.
authors.forename: Mauro
authors.initials: M
authors.lastname: Copelli
authors.affiliation: Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, NY, USA; Division of Experimental Therapeutics, New York State Psychiatric Institute, New York, NY, USA.
authors.forename: Cheryl M
authors.initials: CM
authors.lastname: Corcoran