There is no standard treatment for patients with locally advanced gastric cancer(LAGC).Neoadjuvant immunochemotherapy(NICT)is an emerging therapeutic strategy in LAGC.The prognosis of patients undergoing NICT plus rad...There is no standard treatment for patients with locally advanced gastric cancer(LAGC).Neoadjuvant immunochemotherapy(NICT)is an emerging therapeutic strategy in LAGC.The prognosis of patients undergoing NICT plus radical surgery varies.Hypercoagulation is frequently identified in cancer patients.A retrospective study by Li et al confirmed that in LAGC patients undergoing radical resection post-NICT,elevated D-dimer and fibrinogen levels were asso-ciated with poor prognosis,and their combined assessment improved predictive accuracy.This retrospective study has some limitations,and further prospective research is required to validate hypercoagulation as a prognostic indicator and develop a more precise predictive model.Establishing such a model can facilitate personalized treatment strategies for patients with LAGC.展开更多
Objective:This paper aims to address the clinical challenge of predicting the outcomes of stroke patients and proposes a comprehensive model called RGX to help clinicians adopt more personalized treatment plans.Impact...Objective:This paper aims to address the clinical challenge of predicting the outcomes of stroke patients and proposes a comprehensive model called RGX to help clinicians adopt more personalized treatment plans.Impact Statement:The comprehensive model is first proposed and applied to clinical datasets with missing data.The introduction of the Shapley additive explanations(SHAP)model to explain the impact of patient indicators on prognosis improves the accuracy of stroke patient mortality prediction.Introduction:At present,the prediction of stroke treatment outcomes faces many challenges,including the lack of models to quantify which clinical variables are closely related to patient survival.Methods:We developed a series of machine learning models to systematically predict the mortality of stroke patients.Additionally,by introducing the SHAP model,we revealed the contribution of risk factors to the prediction results.The performance of the models was evaluated using multiple metrics,including the area under the curve,accuracy,and specificity,to comprehensively measure the effectiveness and stability of the models.Results:The RGX model achieved an accuracy of 92.18%on the complete dataset,an improvement of 11.38%compared to that of the most advanced state-of-the-art model.Most importantly,the RGX model maintained excellent predictive ability even when faced with a dataset containing a large number of missing values,achieving an accuracy of 84.62%.Conclusion:In summary,the RGX ensemble model not only provides clinicians with a highly accurate predictive tool but also promotes the understanding of stroke patient survival prediction,laying a solid foundation for the development of precision medicine.展开更多
文摘There is no standard treatment for patients with locally advanced gastric cancer(LAGC).Neoadjuvant immunochemotherapy(NICT)is an emerging therapeutic strategy in LAGC.The prognosis of patients undergoing NICT plus radical surgery varies.Hypercoagulation is frequently identified in cancer patients.A retrospective study by Li et al confirmed that in LAGC patients undergoing radical resection post-NICT,elevated D-dimer and fibrinogen levels were asso-ciated with poor prognosis,and their combined assessment improved predictive accuracy.This retrospective study has some limitations,and further prospective research is required to validate hypercoagulation as a prognostic indicator and develop a more precise predictive model.Establishing such a model can facilitate personalized treatment strategies for patients with LAGC.
基金supported by the Beijing Municipal Natural Science Foundation(7244510).
文摘Objective:This paper aims to address the clinical challenge of predicting the outcomes of stroke patients and proposes a comprehensive model called RGX to help clinicians adopt more personalized treatment plans.Impact Statement:The comprehensive model is first proposed and applied to clinical datasets with missing data.The introduction of the Shapley additive explanations(SHAP)model to explain the impact of patient indicators on prognosis improves the accuracy of stroke patient mortality prediction.Introduction:At present,the prediction of stroke treatment outcomes faces many challenges,including the lack of models to quantify which clinical variables are closely related to patient survival.Methods:We developed a series of machine learning models to systematically predict the mortality of stroke patients.Additionally,by introducing the SHAP model,we revealed the contribution of risk factors to the prediction results.The performance of the models was evaluated using multiple metrics,including the area under the curve,accuracy,and specificity,to comprehensively measure the effectiveness and stability of the models.Results:The RGX model achieved an accuracy of 92.18%on the complete dataset,an improvement of 11.38%compared to that of the most advanced state-of-the-art model.Most importantly,the RGX model maintained excellent predictive ability even when faced with a dataset containing a large number of missing values,achieving an accuracy of 84.62%.Conclusion:In summary,the RGX ensemble model not only provides clinicians with a highly accurate predictive tool but also promotes the understanding of stroke patient survival prediction,laying a solid foundation for the development of precision medicine.
