Predicting Fall Risk With Machine Learning and Computer Vision: Development of A Clinical Decision Support System in Nursing Care
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 177
- 试验地点
- 1
- 主要终点
- Fall Risk Categories Based on the Morse Fall Scale
研究概览
简要总结
The goal of this study is to develop a nursing clinical decision support system for fall risk prediction using machine learning and computer vision techniques. The system is intended to offer advantages over traditional scales, including real-time analysis, contactless monitoring, objective evaluation, and personalized risk prediction-ultimately aiming to improve patient safety and reduce complications related to falls in clinical settings.
This study aims to answer the following questions:
Can machine learning models serve as valid tools for fall risk prediction?
Is the proposed system feasible for use in clinical environments?
Inclusion criteria for participants:
- Aged 18 years or older
- Able to read and write in Turkish
- Able to walk with or without assistance
- Willing to voluntarily participate in the study
Exclusion criteria:
- Inability to speak or understand Turkish adequately
- Being intubated
- Being physically restrained
- Being immobile
- Having a diagnosed cognitive impairment
Participants' basic information-including age, height, and weight-will be collected through a demographic data form. Fall risk will be initially assessed using the Morse Fall Scale. Then, a walking assessment will be conducted using a digital camera-based computer vision system as participants walk at a comfortable pace in a clinical corridor. Additionally, an accelerometer placed in the participants' pockets will record three-axis acceleration (X, Y, Z) during walking.
The data obtained will be analyzed using machine learning algorithms to estimate lower and upper limb biomechanics in real time. Features such as step length, cadence, gait cycle, and range of motion (ROM) will be extracted. These features, combined with Morse Fall Scale scores, will be used to train and validate an artificial neural network (ANN).
The study aims to contribute to the development of a reliable, objective, and real-time system capable of predicting fall risk in clinical environments through gait analysis.
详细描述
Type of Research:
This research is planned as Design and Development Research as an innovative system will be developed.
Place and Time of the Research:
The research will be conducted at the Physical Medicine and Rehabilitation department of a research hospital located in eastern Turkey between June and August 2025.
Population and Sample of the Study:
研究设计
- 研究类型
- Observational
- 观察模型
- Other
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •18 years of age or older
- •Accepting voluntary participation in the study
- •Be able to read and write Turkish
- •Being able to walk with or without support
排除标准
- •Not being able to speak or understand Turkish adequately
- •Being intubated.
- •To be identified.
- •Being immobile
- •Having a mental disability.
结局指标
主要结局
Fall Risk Categories Based on the Morse Fall Scale
时间窗: Day 1
Assessment of the patient's fall risk with the Morse Fall Scale It is an effective and simple measurement tool that is frequently used in hospitals in Turkey and used to diagnose potential patient fall risks for the nursing profession. The scale consists of six criteria (secondary diagnosis, presence of a history of falls, mobilization support, presence of intravenous access or heparin use, gait/transfer, and mental status) that diagnose fall risk. According to this assessment tool, if the patient has a score below 25 points, he/she is in the low risk group for falls. If the score is between 25 and 50, the patient is in the medium risk group, and if the score is 51 and above, the patient is in the high risk group. A minimum score of 0 and a maximum score of 125 can be obtained from the scale. This scale allows a systematic determination of the fall risk of patients in clinical settings.
Fall Risk Classification Accuracy of the Decision Support System
时间窗: Day 1
Classification accuracy of the decision support system was evaluated based on the percentage of test units correctly classified. The scale ranges from 0% to 100%, where higher values indicate better performance. This metric reflects the proportion of correctly identified cases by the system during model evaluation.
Classification Performance Metrics of the Decision Support System (F1 Score, Precision, Recall)
时间窗: Day 1
This outcome measure evaluates the classification performance of a clinical decision support system using standard machine learning metrics: precision, recall, and F1-score. These metrics are based on a scale ranging from 0 to 1. Higher values indicate better classification performance. Precision is defined as the proportion of true positive predictions among all positive predictions. Recall is defined as the proportion of true positive predictions among all actual positives. The F1-score is the harmonic mean of precision and recall.
次要结局
未报告次要终点
研究者
Ahmet Ceviz
ACeviz [Ahmet CEVİZ] Principal Investigator
Inonu University