首页|Central South University Reports Findings in Machine Learning (Upper gastrointestinal haemorrhage patients' survival: A causal inference and prediction study)
Central South University Reports Findings in Machine Learning (Upper gastrointestinal haemorrhage patients' survival: A causal inference and prediction study)
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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 the subject of a report. According to news reporting originating from Changsha, People's Republic of China, by NewsRx correspondents, research stated, "Upper gastrointestinal (GI) bleeding is a common medical emergency. This study aimed to develop models to predict critically ill patients with upper GI bleeding in-hospital and 30-day survival, identify the correlation factor and infer the causality." Our news editors obtained a quote from the research from Central South University, "A total of 2898 patients with upper GI bleeding were included from the Medical Information Mart for Intensive Care-IV and eICU-Collaborative Research Database, respectively. To identify the most critical factors contributing to the prognostic model, we used SHAP (SHapley Additive exPlanations) for machine learning interpretability. We performed causal inference using inverse probability weighting for survival-associated prognostic factors. The optimal model using the light GBM (gradient boosting algorithm) algorithm achieved an AUC of .93 for in-hospital survival, .81 for 30-day survival in internal testing and .87 for in-hospital survival in external testing. Important factors for in-hospital survival, according to SHAP, were SOFA (Sequential organ failure assessment score), GCS (Glasgow coma scale) motor score and length of stay in ICU (Intensive critical care). In contrast, essential factors for 30-day survival were SOFA, length of stay in ICU, total bilirubin and GCS verbal score. Our model showed improved performance compared to SOFA alone. Our interpretable machine learning model for predicting in-hospital and 30-day mortality in critically ill patients with upper gastrointestinal bleeding showed excellent accuracy and high generalizability."
ChangshaPeople's Republic of ChinaAsiaCyborgsEmerging TechnologiesGastroenterologyHealth and MedicineHospitalsMachine Learning
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