Focusing on the reform initiatives of Chinese Academy of Medical Sciences(CAMS) & Peking Union Medical College(PUMC) in medical scientific and technological innovation from perspectives of deepening the reform and...Focusing on the reform initiatives of Chinese Academy of Medical Sciences(CAMS) & Peking Union Medical College(PUMC) in medical scientific and technological innovation from perspectives of deepening the reform and optimizing the ecosystem of science and technology innovation,this article summarizes the highlights of CAMS & PUMC’s efforts in safeguarding people’ s health and promoting the Healthy China 2030 strategy through scientific and technological innovation in the fields including basic research,disease prevention and treatment,and medical technology in the past ten years.These achievements embody the endeavors and responsibility of CAMS& PUMC in realizing self-reliance and self-improvement of Chinese medical science and technology and highlight its contributions to the development of medical science and technology of China.展开更多
Objective To compare the performance of five machine learning models and SAPSⅡ score in predicting the 30-day mortality amongst patients with sepsis.Methods The sepsis patient-related data were extracted from the MIM...Objective To compare the performance of five machine learning models and SAPSⅡ score in predicting the 30-day mortality amongst patients with sepsis.Methods The sepsis patient-related data were extracted from the MIMIC-Ⅳ database.Clinical features were generated and selected by mutual information and grid search.Logistic regression,Random forest,LightGBM,XGBoost,and other machine learning models were constructed to predict the mortality probability.Five measurements including accuracy,precision,recall,F1 score,and area under curve(AUC) were acquired for model evaluation.An external validation was implemented to avoid conclusion bias.Results LightGBM outperformed other methods,achieving the highest AUC(0.900),accuracy(0.808),and precision(0.559).All machine learning models performed better than SAPSⅡ score(AUC=0.748).LightGBM achieved 0.883 in AUC in the external data validation.Conclusions The machine learning models are more effective in predicting the 30-day mortality of patients with sepsis than the traditional SAPS Ⅱ score.展开更多
文摘Focusing on the reform initiatives of Chinese Academy of Medical Sciences(CAMS) & Peking Union Medical College(PUMC) in medical scientific and technological innovation from perspectives of deepening the reform and optimizing the ecosystem of science and technology innovation,this article summarizes the highlights of CAMS & PUMC’s efforts in safeguarding people’ s health and promoting the Healthy China 2030 strategy through scientific and technological innovation in the fields including basic research,disease prevention and treatment,and medical technology in the past ten years.These achievements embody the endeavors and responsibility of CAMS& PUMC in realizing self-reliance and self-improvement of Chinese medical science and technology and highlight its contributions to the development of medical science and technology of China.
文摘Objective To compare the performance of five machine learning models and SAPSⅡ score in predicting the 30-day mortality amongst patients with sepsis.Methods The sepsis patient-related data were extracted from the MIMIC-Ⅳ database.Clinical features were generated and selected by mutual information and grid search.Logistic regression,Random forest,LightGBM,XGBoost,and other machine learning models were constructed to predict the mortality probability.Five measurements including accuracy,precision,recall,F1 score,and area under curve(AUC) were acquired for model evaluation.An external validation was implemented to avoid conclusion bias.Results LightGBM outperformed other methods,achieving the highest AUC(0.900),accuracy(0.808),and precision(0.559).All machine learning models performed better than SAPSⅡ score(AUC=0.748).LightGBM achieved 0.883 in AUC in the external data validation.Conclusions The machine learning models are more effective in predicting the 30-day mortality of patients with sepsis than the traditional SAPS Ⅱ score.
