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Exploring Language Markers for Mental Disorder Diagnosis

In this study, the researchers explored the use of language markers to diagnose mental disorders, a complex process involving genetic, environmental, and psychological factors. While previous research has primarily relied on the Linguistic Inquiry and Word Count (LIWC) program, the team employed a range of Natural Language Processing (NLP) techniques to analyze Dutch psychiatric interview transcriptions. Their primary objective was to predict whether a patient had been diagnosed with a mental disorder, and if so, the specific type of disorder.

Using LIWC in combination with the random forest classification algorithm, the team achieved an accuracy of 0.952 and a Cohen's kappa of 0.889 in predicting whether a person had a mental disorder or not. Additionally, SpaCy in combination with random forest performed best in predicting the specific mental disorder a patient had been diagnosed with, with an accuracy of 0.429 and a Cohen's kappa of 0.304. These findings could contribute to the development of more accurate and efficient methods for diagnosing mental disorders using language markers.

Exploring Language Markers of Mental Health in Psychiatric Stories

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