Using Machine Learning to Detect Risky Behavior in Psychiatric Clinics

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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Machine Learning; Dangerous Behavior
• Intervention / Treatment: Other: Detecting Risky Behaviors and Providing a Safe Environment in Patients Receiving Inpatient Treatment in a Psychiatric Clinic Using Machine Learning Model

Detailed Description

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 encountered in clinics. It is known that the rate of suicide attempts increases 5-10 times in hospitalized patients. In clinics with inpatients, suicide attempts as well as other risky behaviors are frequently encountered.

Preventing risky behavior and providing a safe environment in psychiatric clinics is an important issue in our country and in the world. In order to detect risky behaviors and ensure patient/employee safety, there are measures to monitor patients with cameras in psychiatric clinics within the scope of quality standards in health. However, these measures are not sufficient to completely solve the problem. In psychiatric clinics, patient monitoring is provided by a nurse who constantly monitors the camera images placed in the rooms on the computer screen. The low number of nurses, especially on night shifts, makes camera monitoring difficult during night shifts and poses a problem in terms of patient safety. Constant monitoring of monitors by the nurse reduces the time spent with the patient and increases the workload. Additionally, when screen monitoring is not done, risky behaviors cannot be detected. Therefore, new methods need to be developed to ensure a safe environment in psychiatric clinics. In this sense, the machine learning method, which is increasingly used in artificial intelligence and data analysis, is a specialized sub-branch of artificial intelligence algorithms that tries to derive meaningful results/predictions from existing data. Machine learning method is frequently used in the field of health, and psychiatry is one of these fields. The main purpose of this study is to detect risky and high-risk behaviors of patients treated in a psychiatric clinic using machine learning method and to ensure that patients receive treatment in a safer environment.

• Study Type: Observational
• Enrollment (Estimated): 1

Contacts and Locations

• Study Contact: Name: ceyda öztürk akdeniz, 1; Phone Number: 05394124524; Email: ceyda.ozturk@artvin.edu.tr

Study Locations

• Turkey
  • Istanbul, Turkey, 34000
    • Detecting Risky Behaviors and Providing a Safe Environment in Patients Receiving Inpatient Treatment in a Psychiatric Clinic Using Machine Learning Model

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: N/A

• 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

Description

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

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Targeted Output	Detection of suicide and violent behaviors using machine learning method	01.05.2024-01.08.2024

Collaborators and Investigators

• Sponsor: Istanbul Medeniyet University

Study record dates

Study Major Dates

• Study Start (Estimated): May 20, 2024
• Primary Completion (Estimated): August 1, 2024
• Study Completion (Estimated): August 20, 2024

Study Registration Dates

• First Submitted: April 20, 2024
• First Submitted That Met QC Criteria: May 14, 2024
• First Posted (Actual): May 20, 2024

Study Record Updates

• Last Update Posted (Actual): May 31, 2024
• Last Update Submitted That Met QC Criteria: May 29, 2024
• Last Verified: May 1, 2024

More Information

Terms related to this study

• Keywords: machine learning; psychiatric clinic; risky behavior
• Additional Relevant MeSH Terms: Behavioral Symptoms; Self-Injurious Behavior; Suicide
• Other Study ID Numbers: İstanbulMedeniyet

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No