Curation of a Cardiology Interface Terminology for Highlighting Electronic Health Records using Machine Learning

arXiv cs.AI Papers
healthcare cardiology electronic-health-records machine-learning natural-language-processing terminology clinical-informatics

Summary

This paper proposes a method for curating a Cardiology Interface Terminology (CIT) to highlight details in electronic health record notes using a machine learning technique. The approach involves three phases, including deriving training data and training an ML model to identify candidate concepts, achieving high completeness and coverage on test data.

arXiv:2606.08311v1 Announce Type: new Abstract: Electronic health record (EHR) notes are dense medical documents containing large amounts of information, often filled with complex medical jargon. Highlighting all details in EHRs helps reduce the likelihood of missing crucial information by drawing attention to key content. This study proposes the design of a Cardiology Interface Terminology (CIT) to accurately highlight all details in EHR notes of cardiology patients. We introduce an innovative Machine Learning (ML) technique for the design of CIT. The ML technique requires training data. Manual preparation of such training data is time-consuming and expensive. The process of the CIT design includes three phases. In the first two phases, we innovatively derive a training data CIT to be used by the third phase, ML technique. We start by designing an initial CIT, composed of several components: the cardiology-related sub-hierarchies of SNOMED, other SNOMED concepts mined from EHRs of build set, and necessary components of terms e.g., medical abbreviations and medications. Utilizing an iterative process, fine-grained phrases containing initial CIT concepts are extracted from build set as CIT concept candidates. The candidate concepts are semi-automatically reviewed before being added to CIT, yielding the training data CIT, TCIT. In the third phase, a ML model is trained with TCIT to identify candidates fitting to be concepts in the CIT. This model is used to extract further concepts from build set, yielding the final CIT. The final CIT is then used to highlight the test set and evaluate the extent to which it captures details in an unseen EHR dataset. For this purpose, four evaluation metrics, coverage, breadth, completeness, and conciseness are used. The highlighted test set has a coverage of 74.21%, with a breadth of 1.68. For 20 random notes in test set, the average completeness is 98.2% and average conciseness is 84.2%.
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# Curation of a Cardiology Interface Terminology for Highlighting Electronic Health Records using Machine Learning
Source: [https://arxiv.org/abs/2606.08311](https://arxiv.org/abs/2606.08311)
[View PDF](https://arxiv.org/pdf/2606.08311)

> Abstract:Electronic health record \(EHR\) notes are dense medical documents containing large amounts of information, often filled with complex medical jargon\. Highlighting all details in EHRs helps reduce the likelihood of missing crucial information by drawing attention to key content\. This study proposes the design of a Cardiology Interface Terminology \(CIT\) to accurately highlight all details in EHR notes of cardiology patients\. We introduce an innovative Machine Learning \(ML\) technique for the design of CIT\. The ML technique requires training data\. Manual preparation of such training data is time\-consuming and expensive\. The process of the CIT design includes three phases\. In the first two phases, we innovatively derive a training data CIT to be used by the third phase, ML technique\. We start by designing an initial CIT, composed of several components: the cardiology\-related sub\-hierarchies of SNOMED, other SNOMED concepts mined from EHRs of build set, and necessary components of terms e\.g\., medical abbreviations and medications\. Utilizing an iterative process, fine\-grained phrases containing initial CIT concepts are extracted from build set as CIT concept candidates\. The candidate concepts are semi\-automatically reviewed before being added to CIT, yielding the training data CIT, TCIT\. In the third phase, a ML model is trained with TCIT to identify candidates fitting to be concepts in the CIT\. This model is used to extract further concepts from build set, yielding the final CIT\. The final CIT is then used to highlight the test set and evaluate the extent to which it captures details in an unseen EHR dataset\. For this purpose, four evaluation metrics, coverage, breadth, completeness, and conciseness are used\. The highlighted test set has a coverage of 74\.21%, with a breadth of 1\.68\. For 20 random notes in test set, the average completeness is 98\.2% and average conciseness is 84\.2%\.

## Submission history

From: Mahshad Koohi Habibi Dehkordi \[[view email](https://arxiv.org/show-email/67790a79/2606.08311)\] **\[v1\]**Sat, 6 Jun 2026 19:52:41 UTC \(1,465 KB\)

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