Development of clinical contents model markup language for electronic health records

Mapping the Korean National Health Checkup Questionnaire to Standard Terminologies

Objectives

An increasing emphasis has been placed on the integration of clinical data and patient-generated health data (PGHD), which are generated outside of hospitals. This study explored the possibility of using standard terminologies to represent PGHD for data integration.

Methods

We chose the 2020 general health checkup questionnaire of the Korean Health Screening Program as a resource. We divided every component of the questionnaire into entities and values, which were mapped to standard terminologies—Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) version 2020-07-31 and Logical Observation Identifiers Names and Codes (LOINC) version 2.68.

Results

Eighty-nine items were derived from the 17 questions of the 2020 health examination questionnaire, of which 76 (85.4%) were mapped to standard terms. Fifty-two items were mapped to SNOMED CT and 24 items were mapped to LOINC. Among the items mapped to SNOMED CT, 35 were mapped to pre-coordinated expressions and 17 to post-coordinated expressions. Forty items had one-to-one relationships, and 17 items had one-to-many relationships.

Conclusions

We achieved a high mapping rate (85.4%) by using both SNOMED CT and LOINC. However, we noticed some issues while mapping the Korean general health checkup questionnaire (i.e., lack of explanations, vague questions, and overly narrow concepts). In particular, items combining two or more concepts into a single item were not appropriate for mapping using standard terminologies. Although it is not the case that all items need to be expressed in standard terminology, essential items should be presented in a way suitable for mapping to standard terminology by revising the questionnaire in the future.

Validation of an automated adenoma detection rate calculating system for quality improvement of colonoscopy

Purpose

This study aimed to validate an automated calculating system developed for determining the adenoma detection rate (ADR).

Methods

To calculate the automated ADR, the data linking processes were as follows: (1) matching the selected colonoscopy results with the pathological results, (2) matching the polyp number from colonoscopy with that from pathology and confirming the histopathological results of each colonic polyp, and (3) confirming the histopathological results, especially the adenoma status of each colonic polyp. To verify the accuracy of the automated ADR calculating system, we manually calculated the ADR for 3 months through medical record review. Accuracy was calculated by measuring the error rate for each value. The cause of error was analyzed by additional order and chart review.

Results

After excluding 318 cases, 2,543 patients (1,351 men and 1,192 women; median age, 57.9 years) who underwent colonoscopy were included in this study. When the automated calculating system was used, polyps were found in 1,336 cases (52.6%) and adenomas were found in 1,003 cases (39.4%). When the manual calculating system was used, polyps were found in 1,327 cases (52.2%) and adenomas were found in 1,003 cases (39.4%). The accuracies of the polyp detection rate and ADR according to the automated calculating system were 99.3% and 100%, respectively.

Conclusion

We developed a system to automatically calculate the ADR by extracting hospital electronic medical record results and verified that it provided satisfactory results. It may help to improve colonoscopy quality.

Korean Anaphora Recognition System to Develop Healthcare Dialogue-Type Agent

Objectives

Anaphora recognition is a process to identify exactly which noun has been used previously and relates to a pronoun that is included in a specific sentence later. Therefore, anaphora recognition is an essential element of a dialogue agent system. In the current study, all the merits of rule-based, machine learning-based, semantic-based anaphora recognition systems were combined to design and realize a new hybrid-type anaphora recognition system with an optimum capacity.

Methods

Anaphora recognition rules were encoded on the basis of the internal traits of referred expressions and adjacent contexts to realize a rule-based system and to serve as a baseline. A semantic database, related to predicate instances of sentences including referred expressions, was constructed to identify semantic co-relationships between the referent candidates (to which semantic tags were attached) and the semantic information of predicates. This approach would upgrade the anaphora recognition system by reducing the number of referent candidates. Additionally, to realize a machine learning-based system, an anaphora recognition model was developed on the basis of training data, which indicated referred expressions and referents. The three methods were further combined to develop a new single hybrid-based anaphora recognition system.

Results

The precision rate of the rule-based systems was 54.9%. However, the precision rate of the hybrid-based system was 63.7%, proving it to be the most efficient method.

Conclusions

The hybrid-based method, developed by the combination of rule-based and machine learning-based methods, represents a new system with enhanced functional capabilities as compared to other pre-existing individual methods.