Recent Advances in Predictive Modeling with Electronic Health Records

Area of Science:

• Healthcare Informatics
• Machine Learning
• Data Science

Background:

• Electronic health records (EHR) generate vast digitized patient data.
• EHR data presents unique challenges for predictive modeling.
• Deep learning shows promise for healthcare predictive analytics.

Purpose of the Study:

• To systematically review deep learning-based predictive models using EHR data.
• To categorize and summarize various predictive deep learning models.
• To identify challenges and future research directions in this field.

Main Methods:

• Literature review of recent advances in deep learning for EHR predictive modeling.
• Categorization of predictive deep models from multiple perspectives.
• Identification of relevant benchmarks and toolkits for healthcare predictive modeling.

Main Results:

• A comprehensive overview of deep learning techniques applied to EHR data.
• A structured categorization of predictive deep learning models.
• A discussion of current benchmarks and available toolkits.

Conclusions:

• Deep learning offers powerful tools for EHR predictive modeling.
• Further research is needed to address open challenges.
• Future directions include refining models and improving data utilization.