An analysis of Chinese nursing electronic medical records to predict violence in psychiatric inpatients using text mining and machine learning techniques

• Published in: PloS one Vol. 18; no. 6; p. e0286347
• Main Authors: Hu, Ya-Han, Hung, Jeng-Hsiu, Hu, Li-Yu, Huang, Sheng-Yun, Shen, Cheng-Che
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 07.06.2023; Public Library of Science (PLoS)
• Subjects: Aggression; Analysis; Biology and Life Sciences; Care and treatment; Computer and Information Sciences; Data mining; Datasets; Diagnosis; Electronic health records; Electronic medical records; Electronic records; Engineering and Technology; Feasibility studies; Feature selection; Health care industry; Health risks; Hospital patients; Hospitalization; Hospitals; Learning algorithms; Machine learning; Medical records; Medical research; Medicine and Health Sciences; Medicine, Experimental; Mental disorders; Mental illness; Methods; Natural language processing; Nurses; Nursing; Patients; Physical Sciences; Prediction models; Psychiatric hospital care; Psychiatric hospitals; Psychiatry; Research and Analysis Methods; Risk assessment; Social Sciences; Unstructured data; Variables; Violence; China; Taiwan
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0286347

The prevalence of violence in acute psychiatric wards is a critical concern. According to a meta-analysis investigating violence in psychiatric inpatient units, researchers estimated that approximately 17% of inpatients commit one or more acts of violence during their stay. Inpatient violence negatively affects health-care providers and patients and may contribute to high staff turnover. Therefore, predicting which psychiatric inpatients will commit violence is of considerable clinical significance. The present study aimed to estimate the violence rate for psychiatric inpatients and establish a predictive model for violence in psychiatric inpatients. We collected the structured and unstructured data from Chinese nursing electronic medical records (EMRs) for the violence prediction. The data was obtained from the psychiatry department of a regional hospital in southern Taiwan, covering the period between January 2008 and December 2018. Several text mining and machine learning techniques were employed to analyze the data. The results demonstrated that the rate of violence in psychiatric inpatients is 19.7%. The patients with violence in psychiatric wards were generally younger, had a more violent history, and were more likely to be unmarried. Furthermore, our study supported the feasibility of predicting aggressive incidents in psychiatric wards by using nursing EMRs and the proposed method can be incorporated into routine clinical practice to enable early prediction of inpatient violence. Our findings may provide clinicians with a new basis for judgment of the risk of violence in psychiatric wards.