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Using Machine Learning to Detect Risky Behavior in Psychiatric Clinics

NCT06421480 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 1

Last updated 2024-06-11

No results posted yet for this study

Summary

The aim of this study is to ensure the safety of patients in a psychiatric clinic and to detect risky behaviors by using machine learning method. Risky behaviors are defined as behaviors that are personally, socially and developmentally undesirable and endanger life and health.Patient safety and maintaining a safe environment are among the primary duties of healthcare professionals. Suicide is the most important evidence-based risk factor, especially among individuals with psychiatric illnesses, and is one of the most important factors that threaten patient safety. At the end of this study, it is aimed to detect risky behaviors of patients before they harm themselves and to enable healthcare professionals to make early intervention for these behaviors, thus supporting a safe treatment environment, with the computer system that has been trained with the machine learning model installed in the clinics.

Conditions

  • Machine Learning
  • Dangerous Behavior

Interventions

OTHER

Detecting Risky Behaviors and Providing a Safe Environment in Patients Receiving Inpatient Treatment in a Psychiatric Clinic Using Machine Learning Model

Using machine learning, the computer will be trained to detect suicide and violent behavior. Cameras will be placed in patient rooms. These cameras will transfer the image to the computer. The computer will process these images and detect suicidal and violent behavior early. A warning will appear on the computer screen

Sponsors & Collaborators

  • Istanbul Medeniyet University

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-06-20
Primary Completion
2024-09-01
Completion
2024-09-20

Countries

  • Turkey (Türkiye)

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06421480 on ClinicalTrials.gov