Using Machine Learning to Detect Risky Behavior in Psychiatric Clinics

Launched by ISTANBUL MEDENIYET UNIVERSITY · May 14, 2024

Keywords

ClinConnect Summary

The aim of this study is to detect risky behaviors of patients in a psychiatric clinic using machine learning method. Risky behavior; It is defined as behaviors that are personally, socially and developmentally undesirable and endanger life and health. Patient safety and ensuring a safe environment are among the primary duties of healthcare professionals. Suicide is the most important evidence-based risk factor, especially in individuals with psychiatric illnesses, and is one of the most important factors that threaten patient safety. Suicide attempt is a crisis situation frequently encount...

Gender

ALL

Eligibility criteria

• Inclusion Criteria:
• • It is suitable for all adult patients receiving inpatient treatment in psychiatric clinics. It is designed for the room where patients sleep.
• Exclusion Criteria:
• • People under the age of 18 will be excluded from the study