Why EHR Data Doesn't Fit Neat ML Tables

1. 1Understand the structure and content of EHR data
2. 2Identify the challenges of working with EHR data in ML models
3. 3Explore data preprocessing and feature engineering techniques to improve EHR data quality
4. 4Develop strategies to address the complexity and variability of EHR data

Data scientists and ML engineers working with EHR data can benefit from understanding its limitations and challenges, and product managers can use this insight to inform product development

💡 EHR data requires specialized handling and processing to be effectively used in ML models

📊 EHR data is complex and variable, making it challenging to work with in ML models 🤖