Using Machine Learning to Detect Risky Behavior in Psychiatric Clinics

NCT06421480 | Not Yet Recruiting

• 1 other identifier: İstanbulMedeniyet
• Study Type: observational
• Target: 1
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

psychiatric clinic; machine learning; risky behavior

Outcome Measures

Primary Outcomes (1)

• Targeted Output

  Detection of suicide and violent behaviors using machine learning method

  01.05.2024-01.08.2024

Interventions

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

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

Eligibility Criteria

• Sex: all
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The population of the study was theater and drama actors who randomly exhibited suicidal and violent behavior, using an empty room in a psychiatric clinic for training and testing the machine learning model. The number of actors is 4-5 people

You may qualify if:

• It is suitable for all adult patients receiving inpatient treatment in psychiatric clinics. It is designed for the room where patients sleep.

You may not qualify if:

• People under the age of 18 will be excluded from the study

Sponsors & Collaborators

• Istanbul Medeniyet University: lead

Study Sites (1)

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

Istanbul, 34000, Turkey (Türkiye)

Location

MeSH Terms

Conditions

Dangerous Behavior

Condition Hierarchy (Ancestors)

Behavior

Central Study Contacts

ceyda öztürk akdeniz, 1

CONTACT

05394124524; ceyda.ozturk@artvin.edu.tr

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: April 20, 2024
• First Posted: May 20, 2024
• Study Start: June 20, 2024
• Primary Completion: September 1, 2024
• Study Completion: September 20, 2024
• Last Updated: June 11, 2024

Record last verified: 2024-06

Locations

TU (1)