Abstract
Indigenous peoples and ethnic minorities worldwide have a higher incidence of psychosis, primarily schizophrenia. In New Zealand, Māori individuals experience greater rates of anxiety and depression compared to non-Māori, with a significantly higher incidence of mental illness among Māori. We propose natural language processing (NLP) trained on speech samples from Māori patients as a potential solution to the problem of culturally biased psychometric screening tools for psychosis and schizophrenia. This research examines NLP's ability to diagnose psychosis in Māori patients by analysing speech and language abnormalities as indicators of severe mental illnesses such as schizophrenia. Our research emphasizes the need for inclusive language models and investigates cross-cultural applicability. We employed a three-part method: conducting clinical interviews, pre-processing data with the Natural Language Toolkit (NLTK) and applying language classifiers. The study's results demonstrate the promise of NLP, but limited patient data necessitates further research, including standardizing datasets and integrating NLP with indigenous languages. This research represents a step towards improving diagnostic accuracy and support for Māori people suffering from psychosis, aligning with healthcare's goal of fair and culturally responsive mental health screening.
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