Ignite Creation Date:
2024-06-16 @ 11:48 AM
Last Modification Date:
2024-10-26 @ 3:30 PM
Study NCT ID:
NCT06421480
Status:
NOT_YET_RECRUITING
Last Update Posted:
2024-06-11
First Post:
2024-04-20
Brief Title:
Using Machine Learning to Detect Risky Behavior in Psychiatric Clinics
Sponsor:
Istanbul Medeniyet University
Organization:
Istanbul Medeniyet University
Study Overview
Official Title:
Detecting Risky Behaviors and Providing a Safe Environment in Patients Receiving Inpatient Treatment in a Psychiatric Clinic Using Machine Learning Model
Status:
NOT_YET_RECRUITING
Status Verified Date:
2024-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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 healthPatient 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
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 patientemployee 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 resultspredictions 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
The behaviors that are desired to be detected are risky and high-risk behaviors The high-risk behavior that is targeted to be detected is an act of suicide through hanging Risky behavior Behaviors that include acts of violence such as slapping pushing dropping to the ground pulling hair pushing against the wall choking from behind kicking putting a pillow on ones face and struggling Our primary aim in our study is to detect risky and high-risk behaviors of patients treated in a psychiatric clinic by using machine learning method and to ensure that patients receive treatment in a safer environment The aim is to send an alert to healthcare workers phones and to the computer screen in the clinic where the system will be installed A red alarm with the room number for hanging which is a high-risk action and an orange alarm for violent behavior will be sent to both the healthcare workers phone and the clinic computer screen Body movements and limb movements will be used in the training of artificial intelligenceTwo-dimensional images of behaviors will be created with the Openpose application Then a Long Short Term Memory LSTM based deep learning model will be created In the final stage the success of the model will be evaluated with the F1 score
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 patientemployee 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 resultspredictions 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
The behaviors that are desired to be detected are risky and high-risk behaviors The high-risk behavior that is targeted to be detected is an act of suicide through hanging Risky behavior Behaviors that include acts of violence such as slapping pushing dropping to the ground pulling hair pushing against the wall choking from behind kicking putting a pillow on ones face and struggling Our primary aim in our study is to detect risky and high-risk behaviors of patients treated in a psychiatric clinic by using machine learning method and to ensure that patients receive treatment in a safer environment The aim is to send an alert to healthcare workers phones and to the computer screen in the clinic where the system will be installed A red alarm with the room number for hanging which is a high-risk action and an orange alarm for violent behavior will be sent to both the healthcare workers phone and the clinic computer screen Body movements and limb movements will be used in the training of artificial intelligenceTwo-dimensional images of behaviors will be created with the Openpose application Then a Long Short Term Memory LSTM based deep learning model will be created In the final stage the success of the model will be evaluated with the F1 score
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None