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

## TL;DR

This study evaluates the effectiveness of traditional machine learning and large language models in diagnosing psychiatric patients during emergencies, aiming to improve rapid assessment accuracy.

## Contribution

It explores the application of LLMs like Llama 3.1 to emergency psychiatric diagnosis, a novel approach in this context.

## Key findings

- LLMs show promising diagnostic capabilities in emergency psychiatric assessments.
- Machine learning models outperform traditional methods in speed and accuracy.
- Potential for LLMs to assist clinicians in urgent mental health evaluations.

## 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.