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Single and Cross-Disorder Detection for Autism and Schizophrenia

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cris.lastimport.scopus2024-02-12T20:11:24Z
dc.abstract.enDetection of mental disorders from textual input is an emerging field for applied machine and deep learning methods. Here, we explore the limits of automated detection of autism spectrum disorder (ASD) and schizophrenia (SCZ). We compared the performance of: (1) dedicated diagnostic tools that involve collecting textual data, (2) automated methods applied to the data gathered by these tools, and (3) psychiatrists. Our article tests the effectiveness of several baseline approaches, such as bag of words and dictionary-based vectors, followed by a machine learning model. We employed two more refined Sentic text representations using affective features and concept-level analysis on texts. Further, we applied selected state-of-the-art deep learning methods for text representation and inference, as well as experimented with transfer and zero-shot learning. Finally, we also explored few-shot methods dedicated to low data size scenarios, which is a typical problem for the clinical setting. The best breed of automated methods outperformed human raters (psychiatrists). Cross-dataset approaches turned out to be useful (only from SCZ to ASD) despite different data types. The few-shot learning methods revealed promising results on the SCZ dataset. However, more effort is needed to explore the approaches to efficiently training models, given the very limited amounts of labeled clinical data. Psychiatry is one of the few medical fields in which the diagnosis of most disorders is based on the subjective assessment of a psychiatrist. Therefore, the introduction of objective tools supporting diagnostics seems to be pivotal. This paper is a step in this direction.
dc.affiliationUniwersytet Warszawski
dc.contributor.authorWawer, Aleksander
dc.contributor.authorOkruszek, Łukasz
dc.contributor.authorSarzyńska-Wawer, Justyna
dc.contributor.authorChojnicka, Izabela
dc.date.accessioned2024-01-26T07:39:05Z
dc.date.available2024-01-26T07:39:05Z
dc.date.issued2022
dc.description.financeNie dotyczy
dc.description.volume14
dc.identifier.doi10.1007/S12559-021-09834-9
dc.identifier.issn1866-9956
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/119880
dc.identifier.weblinkhttp://link.springer.com/content/pdf/10.1007/s12559-021-09834-9.pdf
dc.languageeng
dc.pbn.affiliationpsychology
dc.relation.ispartofCognitive Computation
dc.relation.pages461–473
dc.rightsClosedAccess
dc.sciencecloudnosend
dc.subject.enZero-shot learning
dc.subject.enFew-shot learning
dc.subject.enTransfer learning
dc.subject.enSentic computing
dc.subject.enSchizophrenia detection
dc.subject.enAutism spectrum disorder detection
dc.titleSingle and Cross-Disorder Detection for Autism and Schizophrenia
dc.typeJournalArticle
dspace.entity.typePublication