Transfer Learning in Electronic Health Records through Clinical Concept Embedding
This addresses the under-investigated issue of assessing learned representations in EHR research, which is crucial for facilitating transfer learning in healthcare applications.
The study tackled the problem of evaluating and benchmarking clinical concept embeddings in electronic health records by training prominent disease embedding techniques on data from 3.1 million patients and providing pre-trained embeddings for transfer learning.
Deep learning models have shown tremendous potential in learning representations, which are able to capture some key properties of the data. This makes them great candidates for transfer learning: Exploiting commonalities between different learning tasks to transfer knowledge from one task to another. Electronic health records (EHR) research is one of the domains that has witnessed a growing number of deep learning techniques employed for learning clinically-meaningful representations of medical concepts (such as diseases and medications). Despite this growth, the approaches to benchmark and assess such learned representations (or, embeddings) is under-investigated; this can be a big issue when such embeddings are shared to facilitate transfer learning. In this study, we aim to (1) train some of the most prominent disease embedding techniques on a comprehensive EHR data from 3.1 million patients, (2) employ qualitative and quantitative evaluation techniques to assess these embeddings, and (3) provide pre-trained disease embeddings for transfer learning. This study can be the first comprehensive approach for clinical concept embedding evaluation and can be applied to any embedding techniques and for any EHR concept.