随机森林算法在识别护理不良事件风险中的应用

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AbstractObjetive：Toivestigatetheaplicationvalueoftherandomforestalgoritinidentifingkeyriskfactorsassociatedwith adversenursingevents，totherebyprovideclinicalevidencefortheearlyrecognitionandinterventionofpotentialrisks.MethodsA retrospectiveanalysiswasconductedon659casesofadversenursingeventsamonginpatientsatatertiarygradeAhospitalUsingPython programminglanguage integratedwiththeScikit-learlibraryandimplementedviatheJupyterenvironment，arandomforestrediction 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 revealedthatthepatient'sfallriskcore，arthelindex，andmonthsofliicalexperienceamongnewlyrecruitedursesexibiedte highervariableimportae.Adiioaliuencngfctorsincudedpatitgeparment，sifttiurseualificationlelait 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% ，andanarea under thereceiver operating characteristiccurveofO.89.Conclusions：Theapplicationof therandom forest algorithminanalyzingiticalriskfactorseablestargetedearlywaringstrategies.Itwayofervaluableinsightsforthepreventioand managementofadversenursingevents.Thisapproachalsoprovidesapathwayforadvancinghighqualityproductivityinnursingpractice.

Keywordsnursing quality;adverse events; random forest algorithm; nursing management

摘要目的：探讨随机森林算法在护理不良事件关键风险因子识别中的应用价值，为早期识别与干预不良事件潜在风险提供临床依据。（剩余5662字）