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Effectiveness of Artificial Intelligence Methods in Personalized Aggression Risk Prediction within Inpatient
Jing Ling Tay1, Ziqiang Li1, Kang Sim1,2,3
1West Region, Institute of Mental Health, Buangkok Green Medical Park, 10 Buangkok View, Singapore 539747, Singapore.
Journal of Personalized Medicine
|September 23, 2022
Summary
Artificial intelligence (AI) shows promise in predicting patient aggression in psychiatric settings. Machine learning algorithms accurately identify risk factors, aiding in prevention strategies.
Area of Science:
- Psychiatry
- Artificial Intelligence
- Machine Learning
Background:
- Aggression risk assessments are crucial in psychiatric settings to prevent patient and staff injuries.
- Artificial intelligence (AI) and machine learning (ML) are increasingly used to identify factors associated with aggression.
Purpose of the Study:
- To evaluate the predictive accuracy of AI-based aggression risk prediction in psychiatric inpatients.
- To identify clinical variables associated with AI-driven aggression risk prediction.
Main Methods:
- A systematic review using Cooper's five-stage framework was conducted.
- Searched PubMed, Cochrane, Scopus, PsycINFO, CINAHL databases until April 2022.
- Eight studies were appraised using Joanna Briggs Institute critical appraisal tools.
Main Results:
- Most studies (87.5%) used health records for aggression prediction, achieving acceptable to excellent accuracy (AUC 0.75-0.87).
- No single ML algorithm consistently outperformed others (AUC 0.61-0.87).
- Key risk factors included demographics, social profile, past aggression, forensic history, psychiatric history, psychopathology, and challenging behaviors.
Conclusions:
- AI methods show potential for clarifying aggression risk factors in psychiatric inpatients.
- Further research is needed to optimize AI applications for aggression prediction and prevention.
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