Understanding Missingness in Time-series Electronic Health Records for Individualized Representation

TL;DR

This paper explores the patterns of missing data in time-series electronic health records and discusses their implications for personalized machine learning models in healthcare.

Contribution

It provides new insights into missingness patterns in real-world EHR data, highlighting their importance for personalized predictive modeling.

Findings

Identifies common missingness patterns in EHRs

Discusses implications for personalized medicine

Bridges gap between theory and practice in missing data handling

Abstract

With the widespread of machine learning models for healthcare applications, there is increased interest in building applications for personalized medicine. Despite the plethora of proposed research for personalized medicine, very few focus on representing missingness and learning from the missingness patterns in time-series Electronic Health Records (EHR) data. The lack of focus on missingness representation in an individualized way limits the full utilization of machine learning applications towards true personalization. In this brief communication, we highlight new insights into patterns of missingness with real-world examples and implications of missingness in EHRs. The insights in this work aim to bridge the gap between theoretical assumptions and practical observations in real-world EHRs. We hope this work will open new doors for exploring directions for better representation in predictive modelling for true personalization.