An evaluation framework for ARDS prognostic prediction:datasets,models,and features
Acute respiratory distress syndrome(ARDS)prognosis aims to predict the probability of developing a certain risk at a later stage based on current physical condition of a patient.An effective prognostic strategy can significantly reduce mortality and optimize resource allocation.In recent years,researchers have improved the timeliness and accuracy of prognosis by enhancing the computational power of models.However,the problems of non-uniform data formats and inconsistent comparison baselines in ARDS prognostic studies are still severe,which limit the in-depth development of ARDS prognosis studies.To address above problems,this paper proposes an evaluation framework for ARDS prognostic prediction.On the one hand,it proposes a governance strategy for multi-source data to solve the problem of non-uniform ARDS data format;on the other hand,the framework forms an evaluation system consisting of 13 machine learning models and 20 feature sets to solve the problem of inconsistent comparison baselines.Experimental results show that the framework effectively addresses above problems and evaluates the impact of datasets,models,and features.
万方数据
维普
