With the background weight of 1.5, all the classes have an equal weight. The Std Dev Diff represents the standard deviation over validation sections of the differences of the metrics to the default ba

Figshare2024-03-29 更新10

Data_Sheet_3_Machine learning-based prediction of symptomatic intracerebral hemorrhage after intravenous thrombolysis for stroke: a large multicenter study.PDF

卒中后症状性颅内出血预测

临床预测模型

BackgroundThis study aimed to compare the performance of different machine learning models in predicting symptomatic intracranial hemorrhage (sICH) after thrombolysis treatment for ischemic stroke. Me

NIAID Data Ecosystem00

Supplementary Material for: A MARKED EOSINOPHILIC INFILTRATION IN MUCOSA COULD BE A BETTER PREDICTIVE FACTOR FOR INTRACTABLE NON-ESOPHAGEAL EOSINOPHILIC GASTROINTESTINAL DISORDERS.

嗜酸性粒细胞性胃肠道疾病

临床预测模型

Introduction：Non-esophageal eosinophilic gastrointestinal disorders (non-EoE EGIDs) are rare, but their prevalence has recently increased. Although it has been reported that one-half of patients with

Figshare2023-04-21 更新40

Table 1_Machine learning-based prediction of intensive care unit admission in COVID-19 patients presenting with mild respiratory failure.docx

COVID-19重症监护预测

临床预测模型

IntroductionPrevious studies applying machine learning to predict severe respiratory failure in COVID-19 patients have shown inconsistent results due to variations in study populations and predictor v

NIAID Data Ecosystem00

C-indices for predicting outcomes, BEST and validation sets (BEST LOOCV, MOCHA).

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