Diagnosing Psychiatric Patients: Can Large Language and Machine Learning Models Perform Effectively in Emergency Cases?

• URL: http://arxiv.org/abs/2509.00026v1
• Date: Wed, 20 Aug 2025 08:42:19 GMT
• Title: Diagnosing Psychiatric Patients: Can Large Language and Machine Learning Models Perform Effectively in Emergency Cases?
• Authors: Abu Shad Ahammed, Sayeri Mukherjee, Roman Obermaisser,
• Abstract summary: We have conducted research on how traditional machine learning and large language models (LLM) can assess psychiatric patients.<n>Data from emergency psychiatric patients were collected from a rescue station in Germany.<n>Various machine learning models, including Llama 3.1, were used to assess if the predictive capabilities of the models can serve as an efficient tool for identifying patients with unhealthy mental disorders.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Mental disorders are clinically significant patterns of behavior that are associated with stress and/or impairment in social, occupational, or family activities. People suffering from such disorders are often misjudged and poorly diagnosed due to a lack of visible symptoms compared to other health complications. During emergency situations, identifying psychiatric issues is that's why challenging but highly required to save patients. In this paper, we have conducted research on how traditional machine learning and large language models (LLM) can assess these psychiatric patients based on their behavioral patterns to provide a diagnostic assessment. Data from emergency psychiatric patients were collected from a rescue station in Germany. Various machine learning models, including Llama 3.1, were used with rescue patient data to assess if the predictive capabilities of the models can serve as an efficient tool for identifying patients with unhealthy mental disorders, especially in rescue cases.

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