Semantic Characteristics of Schizophrenic Speech

Bar, Kfir; Zilberstein, Vered; Ziv, Ido; Baram, Heli; Dershowitz, Nachum; Itzikowitz, S.; Harel, Eiran Vadim

doi:10.18653/v1/w19-3010

Cited by 13 publications

(21 citation statements)

References 19 publications

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“…Iter et al ( 13 ) recently improved LSA-based models ( 11 , 12 ) by preprocessing their data set, filtering stop words and fillers and using modern word and sentence embeddings that were shown to outperform LSA ( 29 , 30 ). Word embeddings were also used by Rezaii, Walker, and Wolff ( 31 ) who were able to predict psychosis onset in high-risk individuals based on word embeddings and participants’ choice of words, and Bar et al ( 32 ) who found that NAP patients adhered less to a topic throughout a conversation than controls. Word embeddings such as Global Vectors for Word Representation [GloVe; ( 33 )] create a vector space based on a large number of texts.…”

Section: Introductionmentioning

confidence: 99%

Modeling Incoherent Discourse in Non-Affective Psychosis

et al. 2020

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Background Computational linguistic methodology allows quantification of speech abnormalities in non-affective psychosis. For this patient group, incoherent speech has long been described as a symptom of formal thought disorder. Our study is an interdisciplinary attempt at developing a model of incoherence in non-affective psychosis, informed by computational linguistic methodology as well as psychiatric research, which both conceptualize incoherence as associative loosening. The primary aim of this pilot study was methodological: to validate the model against clinical data and reduce bias in automated coherence analysis. Methods Speech samples were obtained from patients with a diagnosis of schizophrenia or schizoaffective disorder, who were divided into two groups of n = 20 subjects each, based on different clinical ratings of positive formal thought disorder, and n = 20 healthy control subjects. Results Coherence metrics that were automatically derived from interview transcripts significantly predicted clinical ratings of thought disorder. Significant results from multinomial regression analysis revealed that group membership (controls vs. patients with vs. without formal thought disorder) could be predicted based on automated coherence analysis when bias was considered. Further improvement of the regression model was reached by including variables that psychiatric research has shown to inform clinical diagnostics of positive formal thought disorder. Conclusions Automated coherence analysis may capture different features of incoherent speech than clinical ratings of formal thought disorder. Models of incoherence in non-affective psychosis should include automatically derived coherence metrics as well as lexical and syntactic features that influence the comprehensibility of speech.

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Section: Introductionmentioning

confidence: 99%

Modeling Incoherent Discourse in Non-Affective Psychosis

et al. 2020

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“…Of the 52 articles that were examined only eight discussed the implications of how the language we express is influenced by our identity and demographic features. The articles that did discuss the influence of social factors predominantly focused on the influence of personality and educational level in on the outputs of the NLP models [9,10,[39][40][41][42][43][44][45]. Of the eight studies, two studies provided a table of the demographic features of the initial dataset, however no studies stratified the outputs of their NLP models by by demographic features.…”

Section: Expression Of Datamentioning

confidence: 99%

“…Lexical approaches have been the mainstay of NLP models which use tokenization of text corpora and then attempt to infer the sentimental meaning of the corpus [5,46]. In recent years, more novel AI approaches have evolved which utilize deep learning and neural nets to understand PLOS ONE language and infer sentiment/meaning [32,[41][42][43][44][45]. Part of this approach includes the use of 'word embeddings', which will be a key focus of this study.…”

Section: Analysis Of Datamentioning

confidence: 99%

Artificial Intelligence in mental health and the biases of language based models

Straw

Callison-Burch

2020

PLoS ONE

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Background The rapid integration of Artificial Intelligence (AI) into the healthcare field has occurred with little communication between computer scientists and doctors. The impact of AI on health outcomes and inequalities calls for health professionals and data scientists to make a collaborative effort to ensure historic health disparities are not encoded into the future. We present a study that evaluates bias in existing Natural Language Processing (NLP) models used in psychiatry and discuss how these biases may widen health inequalities. Our approach systematically evaluates each stage of model development to explore how biases arise from a clinical, data science and linguistic perspective. Design/Methods A literature review of the uses of NLP in mental health was carried out across multiple disciplinary databases with defined Mesh terms and keywords. Our primary analysis evaluated biases within ‘GloVe’ and ‘Word2Vec’ word embeddings. Euclidean distances were measured to assess relationships between psychiatric terms and demographic labels, and vector similarity functions were used to solve analogy questions relating to mental health. Results Our primary analysis of mental health terminology in GloVe and Word2Vec embeddings demonstrated significant biases with respect to religion, race, gender, nationality, sexuality and age. Our literature review returned 52 papers, of which none addressed all the areas of possible bias that we identify in model development. In addition, only one article existed on more than one research database, demonstrating the isolation of research within disciplinary silos and inhibiting cross-disciplinary collaboration or communication. Conclusion Our findings are relevant to professionals who wish to minimize the health inequalities that may arise as a result of AI and data-driven algorithms. We offer primary research identifying biases within these technologies and provide recommendations for avoiding these harms in the future.

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“…Though the use of NLP for mental health research and practice is growing, its application to the schizophrenia spectrum has only begun to realize its potential. The common approach used by researchers implementing NLP to study schizophrenia is to develop classifiers predicting the presence or future development of the disease, primarily by identifying signs of associative thinking (Bar, Zilberstein, Ziv et al ., 2019; Bedi, Carrillo, Cecchi et al ., 2015; Iter, Yoon & Jurafsky, 2018; Rezaii, Walker & Wolff, 2019). However, to the best of our knowledge, there is no systematic characterization of morphological differences in the language used by patients diagnosed with schizophrenia relative to the general population.…”

Section: Introductionmentioning

confidence: 99%

Morphological characteristics of spoken language in schizophrenia patients – an exploratory study

Ziv

Baram

Bar

et al. 2021

Scandinavian J Psychology

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Psychosis is diagnosed based on disruptions in the structure and use of language, including reduced syntactic complexity, derailment, and tangentiality. With the development of computational analysis, natural language processing (NLP) techniques are used in many areas of life to make evaluations and inferences regarding people's thoughts, feelings and behavior. The present study explores morphological characteristic of schizophrenia inpatients using NLP. Transcripts of recorded stories by 49 male subjects (24 inpatients diagnosed with schizophrenia and 25 controls) about 14 Thematic Apperception Test (TAT) pictures were morphologically analyzed. Relative to the control group, the schizophrenic inpatients employed: (1) a similar ratio of nouns, but fewer verbs, adjectives and adverbs; (2) a higher ratio of lemmas to token (LTR) and type to token (TTR); (3) a smaller gap between LTR and TTR; and (4) greater use of the first person. The results were cross‐verified using three well‐known fitting classifier algorithms (Random Forest, XGBoost and a support vector machine). Tests of prediction accuracy, precision and recall found correct attribution of patients to the schizophrenia group at a rate of between 80 and 90%. Overall, the results suggest that the language of schizophrenic inpatients is significantly different from that of healthy controls, being morphologically less complex, more associative and more focused on the self. The findings support NLP analysis as a complementary addition to the traditional clinical psychosis evaluation for schizophrenia.

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Semantic Characteristics of Schizophrenic Speech

Cited by 13 publications

References 19 publications

Modeling Incoherent Discourse in Non-Affective Psychosis

Modeling Incoherent Discourse in Non-Affective Psychosis

Artificial Intelligence in mental health and the biases of language based models

Morphological characteristics of spoken language in schizophrenia patients – an exploratory study

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