Automatic Language Analysis Identifies and Predicts Schizophrenia in First-episode of Psychosis

Overview

Journal Schizophrenia (Heidelb)

Date 2022 Jul 19

PMID 35853943

Authors

Alicia Figueroa-Barra

Daniel Del Aguila

Mauricio Cerda

Pablo A Gaspar

Lucas D Terissi

Manuel Duran

Camila Valderrama

Affiliations

Soon will be listed here.

Abstract

Automated language analysis of speech has been shown to distinguish healthy control (HC) vs chronic schizophrenia (SZ) groups, yet the predictive power on first-episode psychosis patients (FEP) and the generalization to non-English speakers remain unclear. We performed a cross-sectional and longitudinal (18 months) automated language analysis in 133 Spanish-speaking subjects from three groups: healthy control or HC (n = 49), FEP (n = 40), and chronic SZ (n = 44). Interviews were manually transcribed, and the analysis included 30 language features (4 verbal fluency; 20 verbal productivity; 6 semantic coherence). Our cross-sectional analysis showed that using the top ten ranked and decorrelated language features, an automated HC vs SZ classification achieved 85.9% accuracy. In our longitudinal analysis, 28 FEP patients were diagnosed with SZ at the end of the study. Here, combining demographics, PANSS, and language information, the prediction accuracy reached 77.5% mainly driven by semantic coherence information. Overall, we showed that language features from Spanish-speaking clinical interviews can distinguish HC vs chronic SZ, and predict SZ diagnosis in FEP patients.

Citing Articles

Approximating the semantic space: word embedding techniques in psychiatric speech analysis.

Palominos C, He R, Frohlich K, Mulfarth R, Seuffert S, Sommer I Schizophrenia (Heidelb). 2024; 10(1):114.

PMID: 39622800 PMC: 11612388. DOI: 10.1038/s41537-024-00524-7.

Protocol for Designing a Model to Predict the Likelihood of Psychosis From Electronic Health Records Using Natural Language Processing and Machine Learning.

Stavers-Sosa I, Cronkite D, Gerstley L, Kelley A, Kiel L, Kline-Simon A Perm J. 2024; 28(3):23-36.

PMID: 39219312 PMC: 11404646. DOI: 10.7812/TPP/23.139.

Natural Language Processing and Schizophrenia: A Scoping Review of Uses and Challenges.

Deneault A, Dumais A, Desilets M, Hudon A J Pers Med. 2024; 14(7).

PMID: 39063998 PMC: 11278236. DOI: 10.3390/jpm14070744.

Language and communication rehabilitation in patients with schizophrenia: A narrative review.

Jimeno N Heliyon. 2024; 10(2):e24897.

PMID: 38312547 PMC: 10835363. DOI: 10.1016/j.heliyon.2024.e24897.

Framework to Detect Schizophrenia in Brain MRI Slices with Mayfly Algorithm-Selected Deep and Handcrafted Features.

Manic K, Rajinikanth V, Al-Bimani A, Taniar D, Kadry S Sensors (Basel). 2023; 23(1).

PMID: 36616876 PMC: 9823879. DOI: 10.3390/s23010280.

References

Tandon R, Nasrallah H, Keshavan M . Schizophrenia, "just the facts" 5. Treatment and prevention. Past, present, and future. Schizophr Res. 2010; 122(1-3):1-23. DOI: 10.1016/j.schres.2010.05.025. View

Gaspar P, Castillo R, Maturana A, Villar M, Ulloa K, Gonzalez G . Early psychosis detection program in Chile: A first step for the South American challenge in psychosis research. Early Interv Psychiatry. 2018; 13(2):328-334. PMC: 6436982. DOI: 10.1111/eip.12766. View

Kuperberg G . Language in schizophrenia Part 1: an Introduction. Lang Linguist Compass. 2010; 4(8):576-589. PMC: 2950318. DOI: 10.1111/j.1749-818X.2010.00216.x. View

Pawelczyk A, Kotlicka-Antczak M, Lojek E, Ruszpel A, Pawelczyk T . Schizophrenia patients have higher-order language and extralinguistic impairments. Schizophr Res. 2017; 192:274-280. DOI: 10.1016/j.schres.2017.04.030. View

Covington M, He C, Brown C, Naci L, McClain J, Fjordbak B . Schizophrenia and the structure of language: the linguist's view. Schizophr Res. 2005; 77(1):85-98. DOI: 10.1016/j.schres.2005.01.016. View

Hitczenko K, Mittal V, Goldrick M . Understanding Language Abnormalities and Associated Clinical Markers in Psychosis: The Promise of Computational Methods. Schizophr Bull. 2020; 47(2):344-362. PMC: 8480175. DOI: 10.1093/schbul/sbaa141. View

Foltz P, Rosenstein M, Elvevag B . Detecting clinically significant events through automated language analysis: Quo imus?. NPJ Schizophr. 2016; 2:15054. PMC: 4849434. DOI: 10.1038/npjschz.2015.54. View

Crockford C, Lesser R . Assessing functional communication in aphasia: clinical utility and time demands of three methods. Eur J Disord Commun. 1994; 29(2):165-82. DOI: 10.3109/13682829409041490. View

Alpert M, Kotsaftis A, Pouget E . At issue: speech fluency and schizophrenic negative signs. Schizophr Bull. 1997; 23(2):171-7. DOI: 10.1093/schbul/23.2.171. View

10.

Cohen A, Mitchell K, Docherty N, Horan W . Vocal expression in schizophrenia: Less than meets the ear. J Abnorm Psychol. 2016; 125(2):299-309. PMC: 4967358. DOI: 10.1037/abn0000136. View

11.

Stanislawski E, Bilgrami Z, Sarac C, Garg S, Heisig S, Cecchi G . Negative symptoms and speech pauses in youths at clinical high risk for psychosis. NPJ Schizophr. 2021; 7(1):3. PMC: 7822906. DOI: 10.1038/s41537-020-00132-1. View

12.

Clemmer E . Psycholinguistic aspects of pauses and temporal patterns in schizophrenic speech. J Psycholinguist Res. 1980; 9(2):161-85. DOI: 10.1007/BF01067469. View

13.

Andreasen N . Scale for the assessment of thought, language, and communication (TLC). Schizophr Bull. 1986; 12(3):473-82. DOI: 10.1093/schbul/12.3.473. View

14.

de Boer J, Voppel A, Brederoo S, Wijnen F, Sommer I . Language disturbances in schizophrenia: the relation with antipsychotic medication. NPJ Schizophr. 2020; 6(1):24. PMC: 7477551. DOI: 10.1038/s41537-020-00114-3. View

15.

Rezaii N, Walker E, Wolff P . A machine learning approach to predicting psychosis using semantic density and latent content analysis. NPJ Schizophr. 2019; 5(1):9. PMC: 6565626. DOI: 10.1038/s41537-019-0077-9. View

16.

Corcoran C, Carrillo F, Fernandez-Slezak D, Bedi G, Klim C, Javitt D . Prediction of psychosis across protocols and risk cohorts using automated language analysis. World Psychiatry. 2018; 17(1):67-75. PMC: 5775133. DOI: 10.1002/wps.20491. View

17.

Gupta T, Hespos S, Horton W, Mittal V . Automated analysis of written narratives reveals abnormalities in referential cohesion in youth at ultra high risk for psychosis. Schizophr Res. 2017; 192:82-88. PMC: 5656554. DOI: 10.1016/j.schres.2017.04.025. View

18.

Bedi G, Carrillo F, Cecchi G, Fernandez Slezak D, Sigman M, Mota N . Automated analysis of free speech predicts psychosis onset in high-risk youths. NPJ Schizophr. 2016; 1:15030. PMC: 4849456. DOI: 10.1038/npjschz.2015.30. View

19.

Minor K, Willits J, Marggraf M, Jones M, Lysaker P . Measuring disorganized speech in schizophrenia: automated analysis explains variance in cognitive deficits beyond clinician-rated scales. Psychol Med. 2018; 49(3):440-448. DOI: 10.1017/S0033291718001046. View

20.

Minor K, Marggraf M, Davis B, Luther L, Vohs J, Buck K . Conceptual disorganization weakens links in cognitive pathways: Disentangling neurocognition, social cognition, and metacognition in schizophrenia. Schizophr Res. 2015; 169(1-3):153-158. DOI: 10.1016/j.schres.2015.09.026. View