Prediction of psychosis across protocols and risk cohorts using automated language analysis
Language and speech are the primary source of data for psychiatrists to diagnose and treat mental disorders. In psychosis, the very structure of language can be disturbed, including semantic coherence (e.g., derailment and tangentiality) and syntactic complexity (e.g., concreteness). Subtle disturba...
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2018
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_17238617_v17_n1_p67_Corcoran http://hdl.handle.net/20.500.12110/paper_17238617_v17_n1_p67_Corcoran |
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paper:paper_17238617_v17_n1_p67_Corcoran2023-06-08T16:26:57Z Prediction of psychosis across protocols and risk cohorts using automated language analysis Automated language analysis high-risk youths machine learning prediction of psychosis semantic coherence syntactic complexity adolescent adult area under the curve Article automated language analysis classifier clinical outcome cohort analysis controlled study female grammar high risk population human language machine learning major clinical study male measurement accuracy prediction priority journal psychosis receiver operating characteristic semantics speech speech analysis validation study validity young adult Language and speech are the primary source of data for psychiatrists to diagnose and treat mental disorders. In psychosis, the very structure of language can be disturbed, including semantic coherence (e.g., derailment and tangentiality) and syntactic complexity (e.g., concreteness). Subtle disturbances in language are evident in schizophrenia even prior to first psychosis onset, during prodromal stages. Using computer-based natural language processing analyses, we previously showed that, among English-speaking clinical (e.g., ultra) high-risk youths, baseline reduction in semantic coherence (the flow of meaning in speech) and in syntactic complexity could predict subsequent psychosis onset with high accuracy. Herein, we aimed to cross-validate these automated linguistic analytic methods in a second larger risk cohort, also English-speaking, and to discriminate speech in psychosis from normal speech. We identified an automated machine-learning speech classifier – comprising decreased semantic coherence, greater variance in that coherence, and reduced usage of possessive pronouns – that had an 83% accuracy in predicting psychosis onset (intra-protocol), a cross-validated accuracy of 79% of psychosis onset prediction in the original risk cohort (cross-protocol), and a 72% accuracy in discriminating the speech of recent-onset psychosis patients from that of healthy individuals. The classifier was highly correlated with previously identified manual linguistic predictors. Our findings support the utility and validity of automated natural language processing methods to characterize disturbances in semantics and syntax across stages of psychotic disorder. The next steps will be to apply these methods in larger risk cohorts to further test reproducibility, also in languages other than English, and identify sources of variability. This technology has the potential to improve prediction of psychosis outcome among at-risk youths and identify linguistic targets for remediation and preventive intervention. More broadly, automated linguistic analysis can be a powerful tool for diagnosis and treatment across neuropsychiatry. © 2018 World Psychiatric Association 2018 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_17238617_v17_n1_p67_Corcoran http://hdl.handle.net/20.500.12110/paper_17238617_v17_n1_p67_Corcoran |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Automated language analysis high-risk youths machine learning prediction of psychosis semantic coherence syntactic complexity adolescent adult area under the curve Article automated language analysis classifier clinical outcome cohort analysis controlled study female grammar high risk population human language machine learning major clinical study male measurement accuracy prediction priority journal psychosis receiver operating characteristic semantics speech speech analysis validation study validity young adult |
| spellingShingle |
Automated language analysis high-risk youths machine learning prediction of psychosis semantic coherence syntactic complexity adolescent adult area under the curve Article automated language analysis classifier clinical outcome cohort analysis controlled study female grammar high risk population human language machine learning major clinical study male measurement accuracy prediction priority journal psychosis receiver operating characteristic semantics speech speech analysis validation study validity young adult Prediction of psychosis across protocols and risk cohorts using automated language analysis |
| topic_facet |
Automated language analysis high-risk youths machine learning prediction of psychosis semantic coherence syntactic complexity adolescent adult area under the curve Article automated language analysis classifier clinical outcome cohort analysis controlled study female grammar high risk population human language machine learning major clinical study male measurement accuracy prediction priority journal psychosis receiver operating characteristic semantics speech speech analysis validation study validity young adult |
| description |
Language and speech are the primary source of data for psychiatrists to diagnose and treat mental disorders. In psychosis, the very structure of language can be disturbed, including semantic coherence (e.g., derailment and tangentiality) and syntactic complexity (e.g., concreteness). Subtle disturbances in language are evident in schizophrenia even prior to first psychosis onset, during prodromal stages. Using computer-based natural language processing analyses, we previously showed that, among English-speaking clinical (e.g., ultra) high-risk youths, baseline reduction in semantic coherence (the flow of meaning in speech) and in syntactic complexity could predict subsequent psychosis onset with high accuracy. Herein, we aimed to cross-validate these automated linguistic analytic methods in a second larger risk cohort, also English-speaking, and to discriminate speech in psychosis from normal speech. We identified an automated machine-learning speech classifier – comprising decreased semantic coherence, greater variance in that coherence, and reduced usage of possessive pronouns – that had an 83% accuracy in predicting psychosis onset (intra-protocol), a cross-validated accuracy of 79% of psychosis onset prediction in the original risk cohort (cross-protocol), and a 72% accuracy in discriminating the speech of recent-onset psychosis patients from that of healthy individuals. The classifier was highly correlated with previously identified manual linguistic predictors. Our findings support the utility and validity of automated natural language processing methods to characterize disturbances in semantics and syntax across stages of psychotic disorder. The next steps will be to apply these methods in larger risk cohorts to further test reproducibility, also in languages other than English, and identify sources of variability. This technology has the potential to improve prediction of psychosis outcome among at-risk youths and identify linguistic targets for remediation and preventive intervention. More broadly, automated linguistic analysis can be a powerful tool for diagnosis and treatment across neuropsychiatry. © 2018 World Psychiatric Association |
| title |
Prediction of psychosis across protocols and risk cohorts using automated language analysis |
| title_short |
Prediction of psychosis across protocols and risk cohorts using automated language analysis |
| title_full |
Prediction of psychosis across protocols and risk cohorts using automated language analysis |
| title_fullStr |
Prediction of psychosis across protocols and risk cohorts using automated language analysis |
| title_full_unstemmed |
Prediction of psychosis across protocols and risk cohorts using automated language analysis |
| title_sort |
prediction of psychosis across protocols and risk cohorts using automated language analysis |
| publishDate |
2018 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_17238617_v17_n1_p67_Corcoran http://hdl.handle.net/20.500.12110/paper_17238617_v17_n1_p67_Corcoran |
| _version_ |
1768546557153509376 |