Report on the Project for Establishment of the Standardized Korean Laboratory Terminology Database, 2015

The National Health Information Standards Committee was established in 2004 in Korea. The practical subcommittee for laboratory test terminology was placed in charge of standardizing laboratory medicine terminology in Korean. We aimed to establish a standardized Korean laboratory terminology database, Korea-Logical Observation Identifier Names and Codes (K-LOINC) based on former products sponsored by this committee. The primary product was revised based on the opinions of specialists. Next, we mapped the electronic data interchange (EDI) codes that were revised in 2014, to the corresponding K-LOINC. We established a database of synonyms, including the laboratory codes of three reference laboratories and four tertiary hospitals in Korea. Furthermore, we supplemented the clinical microbiology section of K-LOINC using an alternative mapping strategy. We investigated other systems that utilize laboratory codes in order to investigate the compatibility of K-LOINC with statistical standards for a number of tests. A total of 48,990 laboratory codes were adopted (21,539 new and 16,330 revised). All of the LOINC synonyms were translated into Korean, and 39,347 Korean synonyms were added. Moreover, 21,773 synonyms were added from reference laboratories and tertiary hospitals. Alternative strategies were established for mapping within the microbiology domain. When we applied these to a smaller hospital, the mapping rate was successfully increased. Finally, we confirmed K-LOINC compatibility with other statistical standards, including a newly proposed EDI code system. This project successfully established an up-to-date standardized Korean laboratory terminology database, as well as an updated EDI mapping to facilitate the introduction of standard terminology into institutions.ope

Aligning an interface terminology to the Logical Observation Identifiers Names and Codes (LOINC((R)))

OBJECTIVE: Our study consists in aligning the interface terminology of the Bordeaux university hospital (TLAB) to the Logical Observation Identifiers Names and Codes (LOINC). The objective was to facilitate the shared and integrated use of biological results with other health information systems. MATERIALS AND METHODS: We used an innovative approach based on a decomposition and re-composition of LOINC concepts according to the transversal relations that may be described between LOINC concepts and their definitional attributes. TLAB entities were first anchored to LOINC attributes and then aligned to LOINC concepts through the appropriate combination of definitional attributes. Finally, using laboratory results of the Bordeaux data-warehouse, an instance-based filtering process has been applied. RESULTS: We found a small overlap between the tokens constituting the labels of TLAB and LOINC. However, the TLAB entities have been easily aligned to LOINC attributes. Thus, 99.8% of TLAB entities have been related to a LOINC analyte and 61.0% to a LOINC system. A total of 55.4% of used TLAB entities in the hospital data-warehouse have been mapped to LOINC concepts. We performed a manual evaluation of all 1-1 mappings between TLAB entities and LOINC concepts and obtained a precision of 0.59. CONCLUSION: We aligned TLAB and LOINC with reasonable performances, given the poor quality of TLAB labels. In terms of interoperability, the alignment of interface terminologies with LOINC could be improved through a more formal LOINC structure. This would allow queries on LOINC attributes rather than on LOINC concepts only

Personal Health Train on FHIR:A Privacy Preserving Federated Approach for Analyzing FAIR Data in Healthcare

Big data and machine learning applications focus on retrieving data on a central location for analysis. However, healthcare data can be sensitive in nature and as such difficult to share and make use for secondary purposes. Healthcare vendors are restricted to share data without proper consent from the patient. There is a rising awareness among individual patients as well regarding sharing their personal information due to ethical, legal and societal problems. The current data-sharing platforms in healthcare do not sufficiently handle these issues. The rationale of the Personal Health Train (PHT) approach shifts the focus from sharing data to sharing processing/analysis applications and their respective results. A prerequisite of the PHT-infrastructure is that the data is FAIR (findable, accessible, interoperable, reusable). The aim of the paper is to describe a methodology of finding the number of patients diagnosed with hypertension and calculate cohort statistics in a privacy-preserving federated manner. The whole process completes without individual patient data leaving the source. For this, we rely on the Fast Healthcare Interoperability Resources (FHIR) standard

Knowledge representation and text mining in biomedical, healthcare, and political domains

Knowledge representation and text mining can be employed to discover new knowledge and develop services by using the massive amounts of text gathered by modern information systems. The applied methods should take into account the domain-specific nature of knowledge. This thesis explores knowledge representation and text mining in three application domains. Biomolecular events can be described very precisely and concisely with appropriate representation schemes. Protein–protein interactions are commonly modelled in biological databases as binary relationships, whereas the complex relationships used in text mining are rich in information. The experimental results of this thesis show that complex relationships can be reduced to binary relationships and that it is possible to reconstruct complex relationships from mixtures of linguistically similar relationships. This encourages the extraction of complex relationships from the scientific literature even if binary relationships are required by the application at hand. The experimental results on cross-validation schemes for pair-input data help to understand how existing knowledge regarding dependent instances (such those concerning protein–protein pairs) can be leveraged to improve the generalisation performance estimates of learned models. Healthcare documents and news articles contain knowledge that is more difficult to model than biomolecular events and tend to have larger vocabularies than biomedical scientific articles. This thesis describes an ontology that models patient education documents and their content in order to improve the availability and quality of such documents. The experimental results of this thesis also show that the Recall-Oriented Understudy for Gisting Evaluation measures are a viable option for the automatic evaluation of textual patient record summarisation methods and that the area under the receiver operating characteristic curve can be used in a large-scale sentiment analysis. The sentiment analysis of Reuters news corpora suggests that the Western mainstream media portrays China negatively in politics-related articles but not in general, which provides new evidence to consider in the debate over the image of China in the Western media