Joint modeling strategy for using electronic medical records data to build machine learning models: an example of intracerebral hemorrhage

Jianxiang Tang, Xiaoyu Wang, Hongli Wan, Chunying Lin, Zilun Shao, Yang Chang, Hexuan Wang, Yi Wu, Tao Zhang & Yu Du
Abstract Background Outliers and class imbalance in medical data could affect the accuracy of machine learning models. For physicians who want to apply predictive models, how to use the data at hand to build a model and what model to choose are very thorny problems. Therefore, it is necessary to consider outliers, imbalanced data, model selection, and parameter tuning when modeling. Methods This study used a joint modeling strategy consisting of: outlier detection and removal,...
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