首页|Lanzhou University Second Hospital Reports Findings in Machine Learning (Study o n medical dispute prediction model and its clinical-application effectiveness ba sed on machine learning)
Lanzhou University Second Hospital Reports Findings in Machine Learning (Study o n medical dispute prediction model and its clinical-application effectiveness ba sed on machine learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting from Lanzhou, People’s Republ ic of China, by NewsRx journalists, research stated, “Medical dispute is a globa l public health issue, which has been garnering increasing attention. In this st udy, we used machine learning (ML) method to establish a dispute prediction mode l and explored the clinical-application efficiency of this model in effectively reducing the occurrence of medical disputes.” The news correspondents obtained a quote from the research from Lanzhou Universi ty Second Hospital, “Retrospective study of All disputes filed by Gansu Medical Mediation Committee from 2019 to 2021 and patients with the same hospital level as that of the dispute group and hospitalization year were randomly selected as the control group in 1:1 ratio. SPSS software was used for univariate feature se lection of the 14 factors that may cause disputes, and factors with statistical differences were selected. The data were divided into training and test sets in a 7:3 ratio. Six ML models were selected, and Python was used to establish a dis pute prediction model. The area under the curve (AUC) of the receiver operating characteristic curve (ROC), sensitivity, specificity, accuracy, precision, avera ge precision (AP), and F1 score were used to characterize the fitting and accura cy of the models, while decision curve analysis (DCA) was used to evaluate their clinical utility. A total of 1189 patients in the dispute and control groups we re extracted. Following 11 influencing factors were selected: the inpatient depa rtment, doctor title, patient age, patient gender, patient occupation, payment m ethod, hospitalization days, hospitalization times, discharge method, blood tran sfusion volume, and hospitalization espenses. Compared to other models, the AUC (0.945, 95% CI 0.913-0.981), Sensitivity (0.887), Accuracy (0.887) , AP (0.834), and F1 score (0.880) of the random forest model were higher than t hose of other models, while the DCA curve indicated its high clinical benefits. Inpatient department, hospitalization expenses, and discharge type are the prima ry influencing factors of dispute.”
LanzhouPeople’s Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine Learning
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