Objectives: To access the accuracy of the Logical Observation Identifiers Names and Codes (LOINC) mapping to local laboratory test codes that is crucial to data integration across time and healthcare systems.
Materials And Methods: We used software tools and manual reviews to estimate the rate of LOINC mapping errors among 179 million mapped test results from 2 DataMarts in PCORnet. We separately reported unweighted and weighted mapping error rates, overall and by parts of the LOINC term.
Results: Of included 179 537 986 mapped results for 3029 quantitative tests, 95.4% were mapped correctly implying an 4.6% mapping error rate. Error rates were less than 5% for the more common tests with at least 100 000 mapped test results. Mapping errors varied across different LOINC classes. Error rates in chemistry and hematology classes, which together accounted for 92.0% of the mapped test results, were 0.4% and 7.5%, respectively. About 50% of mapping errors were due to errors in the property part of the LOINC name.
Discussions: Mapping errors could be detected automatically through inconsistencies in (1) qualifiers of the analyte, (2) specimen type, (3) property, and (4) method. Among quantitative test results, which are the large majority of reported tests, application of automatic error detection and correction algorithm could reduce the mapping errors further.
Conclusions: Overall, the mapping error rate within the PCORnet data was 4.6%. This is nontrivial but less than other published error rates of 20%-40%. Such error rate decreased substantially to 0.1% after the application of automatic detection and correction algorithm.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9846663 | PMC |
| http://dx.doi.org/10.1093/jamia/ocac215 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Intelligent Optimization Design Framework for Alternating Current Pulse Modulation Electrohydrodynamic Printing Process Parameters.
Small
January 2025
Key Laboratory for Micro/Nano Technology and System of Liaoning Province, Dalian University of Technology, Dalian, 116024, China.
To achieve efficient size tuning of printed microstructures on insulating substrates, an integrated process parameter intelligent optimization design framework for alternating current pulse modulation electrohydrodynamic (AC-EHD) printing is proposed for the first time. The framework is comprised of two stages: the construction of a prediction model and the acquisition of process parameters. The first stage employs the elk herd optimizer(EHO)-artificial neural network(ANN) to establish a mapping relationship between printing process parameters and the size of deposited droplets.
View Article and Find Full Text PDFRevolutionizing RIS Networks: LiDAR-Based Data-Driven Approach to Enhance RIS Beamforming.
Sensors (Basel)
December 2024
Department of Electrical and Computer Engineering, Iowa State University of Science and Technology, Ames, IA 50011, USA.
Reconfigurable Intelligent Surface (RIS) panels have garnered significant attention with the emergence of next-generation network technologies. This paper proposes a novel data-driven approach that leverages Light Detecting and Ranging (LiDAR) sensors to enhance user localization and beamforming in RIS-assisted networks. Integrating LiDAR sensors into the network will be instrumental, offering high-speed and precise 3D mapping capabilities, even in low light or adverse weather conditions.
View Article and Find Full Text PDFExperimental Investigation and Modeling of Surface Roughness in BTA Deep Hole Drilling with Vibration Assisted.
Materials (Basel)
December 2024
College of Mechanical Engineering, Baoji University of Arts and Sciences, Baoji 721016, China.
The surface roughness of hole machining greatly influences the mechanical properties of parts, such as early fatigue failure and corrosion resistance. The boring and trepanning association (BTA) deep hole drilling with axial vibration assistance is a compound machining process of the tool cutting and the guide block extrusion. At the same time, the surface of the hole wall is also ironed by the axial large amplitude and low-frequency vibration of the guide block.
View Article and Find Full Text PDFData-driven modeling of 600 MW supercritical unit under full operating conditions based on Transformer-XL.
ISA Trans
January 2025
School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China.
Improving the flexible and deep peak shaving capability of supercritical (SC) unit under full operating conditions to adapt a larger-scale renewable energy integrated into the power grid is the main choice of novel power system. However, it is particularly challenging to establish an accurate SC unit model under large-scale variable loads and deep peak shaving. To this end, a data-driven modeling strategy combining Transformer-Extra Long (Transformer-XL) and quantum chaotic nutcracker optimization algorithm is proposed.
View Article and Find Full Text PDFEvaluating prefrontal changes in depression using functional near-infrared spectroscopy utilizing Stroop test: A comparison with healthy controls.
Indian J Psychiatry
November 2024
Department of Psychiatry, All India Institute of Medical Sciences, New Delhi, India.
Background: Functional near-infrared spectroscopy (fNIRS) is being increasingly utilized to visualize the brain areas involved in cognitive activity to understand the human brain better. Its portability and easy setup give it an advantage over other functional brain imaging tools. The current study utilizes fNIRS while performing a Stroop test, which is commonly used to assess the impairment of information selection in depression.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!