Diagnosing Psychiatric Patients: Can Large Language and Machine Learning Models Perform Effectively in Emergency Cases?
This addresses the challenge of accurately identifying mental disorders in emergency cases, where misdiagnosis is common due to lack of visible symptoms, potentially improving patient outcomes.
The paper tackled the problem of diagnosing psychiatric patients in emergency situations by evaluating traditional machine learning and large language models (LLMs) like Llama 3.1 on data from a rescue station in Germany, finding that these models can effectively assess patients based on behavioral patterns to provide diagnostic assessments.
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.