JMIR Publications

ABSTRACT

Background:

Common data models (CDMs) help standardize electronic health record data and facilitate outcome analysis for observational and longitudinal research. An analysis of pathology reports is required to establish fundamental information infrastructure for data-driven colon cancer research. The Observational Medical Outcomes Partnership (OMOP) CDM is used in distributed research networks for clinical data; however, it requires conversion of free-text–based pathology reports into the CDM’s format. There are few use cases of representing cancer data in CDM.

Objective:

In this study, we aimed to construct a CDM database of colon-cancer–related pathology with natural language processing (NLP) for a research platform that can utilize both clinical and omics data. The essential text entities from the pathology reports are extracted, standardized, and converted to the OMOP CDM format in order to utilize the pathology data in cancer research.

Methods:

We extracted clinical text entities, mapped them to the standard concepts in the Observational Health Data Sciences and Informatics (OHDSI) vocabularies, and built databases and defined relations for the CDM tables. Major clinical entities were extracted through NLP on pathology reports of surgical specimens, immunohistochemical studies, and molecular study of colon cancer patients at a tertiary general hospital in South Korea. Items were extracted from each report using regular expressions in Python. Unstructured data, such as text that does not have a pattern, were handled with expert advice by adding regular expression rules. Our own dictionary was used for normalization and standardization to deal with biomarker and gene names and other ungrammatical expressions. The extracted clinical and genetic information was mapped to the Logical Observation Identifiers Names and Codes (LOINC) databases and the Systematized Nomenclature of Medicine (SNOMED) standard terminologies recommended by the OMOP CDM. The database–table relationships were newly defined through SNOMED standard terminology concepts. The standardized data were inserted into the CDM tables. For evaluation, 100 reports were randomly selected and independently annotated by a medical informatics expert and a nurse.

Results:

We examined and standardized 1,848 immunohistochemical study reports, 3,890 molecular study reports, and 12,352 pathology reports of surgical specimens (from 2017 to 2018). The constructed and updated database contained the following extracted colorectal entities: 1) NOTE_NLP, 2) MEASUREMENT, 3) CONDITION_OCCURRENCE, 4) SPECIMEN, and 5) FACT_RELATIONSHIP of specimen with condition and measurement.

Conclusions:

This study was aimed at preparing CDM data for a research platform to take advantage of all omics clinical and patient data at Seoul National University Bundang Hospital (SNUBH) for colon cancer pathology. A more sophisticated preparation of the pathology data is needed for further research on cancer genomics, and various types of text narratives are the next target for additional research on the use of data in the CDM.