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

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, data balancing, model fitting and prediction, performance evaluation. We collected medical record data for all ICH patients with admissions in 2017-2019 from Sichuan Province. Clinical and radiological variables were used to construct models to predict mortality outcomes 90 days after discharge. We used stackin

SUBMITTER: Tang J 

PROVIDER: S-EPMC9594939 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature