Interoperability of standardised electronic healthcare records facilitates transfer learning
Remfry, E.; Henkin, R.
Show abstract
Electronic healthcare records (EHR) use codes from different vocabularies to describe medical occurrences, often varying by type of care and country. Common data models (CDM) such as the Observational Medical Outcomes Partnership (OMOP) have been developed to enable the combination and comparison of heterogeneous datasets. We use the OMOP Standard Vocabularies to standardise two English EHR datasets and assess their interoperability through a BERT-based transformer model via pretraining and fine-tuning. Our results show the potential for standardisation to empower transfer learning, with tradeoffs related to data loss.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Medication information extraction using local large language models 96%
- EHR-QC: A streamlined pipeline for automated electronic health records standardisation and preprocessing to predict clinical outcomes 95%
- Using computable knowledge mined from the literature to elucidate confounders for EHR-based pharmacovigilance 95%
Similar papers in this journal
- Can we trust the prediction model? Demonstrating the importance of external validation by investigating the COVID-19 Vulnerability (C-19) Index across an international network of observational healthcare datasets 93%
- Transformative potential of Large Language Models in data mining on Electronic Health Records. 92%
- FHIR-DHP: A Standardized Clinical Data Harmonisation Pipeline for scalable AI application deployment 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.