Application of random forest algorithm in identifying the risk of adverse nursing events.
Objective: To investigate the application value of the random forest algorithm in identifying key risk factors associated with adverse nursing events, to thereby provide clinical evidence for the early recognition and intervention of potential risks. Methods: A retrospective analysis was conducted o...
| Publicado en: | Chinese Nursing Research Vol. 39; no. 24; pp. 4172 - 4176 |
|---|---|
| Autores principales: | , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Chinese Nursing Research Editorial Office
Dec2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190811755&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190811755 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10096493 YV6 jtl: Chinese Nursing Research issn: 10096493 maglogo: N pubinfo: dt: Dec2025 vid: 39 iid: 24 pid: 37375 pub: Chinese Nursing Research Editorial Office artinfo: ui: 190811755 190811755 190811755 10.12102/j.issn.1009-6493.2025.24.013 190811755 ppf: 4172 ppct: 4 formats: tig: atl: Application of random forest algorithm in identifying the risk of adverse nursing events. aug: au: WANG, Jing HE, Jinyang FANG, Pingping WU, Yiling LIU, Xiao affil: Jiangxi Maternal and Child Health Hospital, Jiangxi 330006 China sug: subj: Random Forest Algorithms Prediction Models Risk Assessment Adverse Health Care Event Quality of Nursing Care China Human Retrospective Design Tertiary Health Care Hospitals Accidental Falls Risk Factors Barthel Index Scales Age Factors Educational Status Patient Compliance Decision Making, Clinical Nursing Units Validity ROC Curve Productivity Early Diagnosis Early Intervention ab: Objective: To investigate the application value of the random forest algorithm in identifying key risk factors associated with adverse nursing events, to thereby provide clinical evidence for the early recognition and intervention of potential risks. Methods: A retrospective analysis was conducted on 659 cases of adverse nursing events among inpatients at a tertiary grade A hospital. Using Python programming language integrated with the Scikit-learn library and implemented via the Jupyter environment, a random forest prediction model was developed. The dataset was partitioned into training and validation sets at a 3 : 1 ratio, followed by model training and evaluation to assess predictive performance. Results: The incidence rate of adverse nursing events was 0.352%. Random forest analysis revealed that the patient's fall risk score, Barthel index, and months of clinical experience among newly recruited nurses exhibited the higher variable importance. Additional influencing factors included patient age, department, shift time, nurse qualification level, patient compliance, nurse decision-making capability, and hospital ward area. The model achieved an accuracy of 74.7%, a recall rate of 75%, an F1 score of 72%, and an area under the receiver operating characteristic curve of 0.89. Conclusions: The application of the random forest algorithm in analyzing critical risk factors enables targeted early warning strategies. It way offer valuable insights for the prevention and management of adverse nursing events. This approach also provides a pathway for advancing high-quality productivity in nursing practice. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: Chinese refInfo: holdings: @attributes: islocal: N |
|---|