首页|Recent Studies from Chinese Academy of Medical Sciences Add New Data to Machine Learning (A novel higher performance nomogram based on explainable machine learn ing for predicting mortality risk in stroke patients within 30 days based on ... )
Recent Studies from Chinese Academy of Medical Sciences Add New Data to Machine Learning (A novel higher performance nomogram based on explainable machine learn ing for predicting mortality risk in stroke patients within 30 days based on ... )
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news reporting originating from the Chi nese Academy of Medical Sciences by NewsRx correspondents, research stated, "Thi s study aimed to develop a higher performance nomogram based on explainable mach ine learning methods, and to predict the risk of death of stroke patients within 30 days based on clinical characteristics on the first day of intensive care un its (ICU) admission. Data relating to stroke patients were extracted from the Me dical Information Marketplace of the Intensive Care (MIMIC) IV and III database. " Funders for this research include Beijing Natural Science Foundation; The Cams I nnovation Fund For Medical Sciences; The Program of Chinese Academy of Medical S ciences. Our news journalists obtained a quote from the research from Chinese Academy of Medical Sciences: "The LightGBM machine learning approach together with Shapely additive explanations (termed as explain machine learning, EML) was used to sele ct clinical features and define cut-off points for the selected features. These selected features and cut-off points were then evaluated using the Cox proportio nal hazards regression model and Kaplan-Meier survival curves. Finally, logistic regression-based nomograms for predicting 30-day mortality of stroke patients w ere constructed using original variables and variables dichotomized by cut-off p oints, respectively. The performance of two nomograms were evaluated in overall and individual dimension. A total of 2982 stroke patients and 64 clinical featur es were included, and the 30-day mortality rate was 23.6% in the M IMIC-IV datasets. 10 variables (‘sofa (sepsis-related organ failure assessment)' , ‘minimum glucose', ‘maximum sodium', ‘age', ‘mean spo2 (blood oxygen saturatio n)', ‘maximum temperature', ‘maximum heart rate', ‘minimum bun (blood urea nitro gen)', ‘minimum wbc (white blood cells)' and ‘charlson comorbidity index') and r espective cut-off points were defined from the EML. In the Cox proportional haza rds regression model (Cox regression) and Kaplan-Meier survival curves, after gr ouping stroke patients according to the cut-off point of each variable, patients belonging to the high-risk subgroup were associated with higher 30-day mortalit y than those in the low-risk subgroup. The evaluation of nomograms found that th e EML-based nomogram not only outperformed the conventional nomogram in NIR (net reclassification index), brier score and clinical net benefits in overall dimen sion, but also significant improved in individual dimension especially for low ‘ maximum temperature' patients."
Chinese Academy of Medical SciencesCyb orgsEmerging TechnologiesMachine LearningRisk and Prevention
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