ArticleJournal of biomedical informatics2012
Standardizing clinical laboratory data for secondary use.
Article in Journal of biomedical informatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- Harmonizing Logical Observation Identifiers Names and Codes (LOINC) Codes and Units in Real-World Oncology Data: Method Development and Evaluation.JMIR medical informatics · 2026Article
- Enhancing Healthcare Data Integration: A Machine Learning Approach to Harmonizing Laboratory Labels.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- A SARS-CoV-2 minimum data standard to support national serology reporting.Annals of clinical biochemistry · 2024Article
- lab2clean: a novel algorithm for automated cleaning of retrospective clinical laboratory results data for secondary uses.BMC medical informatics and decision making · 2024Article
- Why do probabilistic clinical models fail to transport between sites.NPJ digital medicine · 2024Review
- Article
- Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices.Journal of machine learning research : JMLR · 2023Article
- Toxicology Test Results for Public Health Surveillance of the Opioid Epidemic: Retrospective Analysis.Online journal of public health informatics · 2023Article
- lab: an R package for generating analysis-ready data from laboratory records.PeerJ. Computer science · 2023Article
- BGLM: big data-guided LOINC mapping with multi-language support.JAMIA open · 2022Article
- Semantic data interoperability, digital medicine, and e-health in infectious disease management: a review.European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology · 2019Review
- Variation in Laboratory Test Naming Conventions in EHRs Within and Between Hospitals: A Nationwide Longitudinal Study.Medical care · 2019Article
- Secondary Use of Laboratory Data: Potentialities and Limitations.Iranian journal of pathology · 2019Review
- Including household effects in Big Data research: the experience of building a longitudinal residence algorithm using linked administrative data in Wales.International journal of population data science · 2018Article
- Automated mapping of laboratory tests to LOINC codes using noisy labels in a national electronic health record system database.Journal of the American Medical Informatics Association : JAMIA · 2018Article
- Meaningful Use and Electronic Laboratory Reporting: Challenges Health Information Technology Vendors Face in Kentucky.Online journal of public health informatics · 2017Article
- Patient crossover and potentially avoidable repeat computed tomography exams across a health information exchange.Journal of the American Medical Informatics Association : JAMIA · 2017Article
- MIMIC-III, a freely accessible critical care database.Scientific data · 2016Article
- Metadata-driven Clinical Data Loading into i2b2 for Clinical and Translational Science Institutes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2016Article
- Coding of Electronic Laboratory Reports for Biosurveillance, Selected United States Hospitals, 2011.Online journal of public health informatics · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Clinical databases provide a rich source of data for answering clinical research questions. However, the variables recorded in clinical data systems are often identified by local, idiosyncratic, and sometimes redundant and/or ambiguous names (or codes) rather than unique, well-organized codes from standard code systems. This reality discourages research use of such databases, because researchers must invest considerable time in cleaning up the data before they can ask their first research question. Researchers at MIT developed MIMIC-II, a nearly complete collection of clinical data about intensive care patients. Because its data are drawn from existing clinical systems, it has many of the problems described above. In collaboration with the MIT researchers, we have begun a process of cleaning up the data and mapping the variable names and codes to LOINC codes. Our first step, which we describe here, was to map all of the laboratory test observations to LOINC codes. We were able to map 87% of the unique laboratory tests that cover 94% of the total number of laboratory tests results. Of the 13% of tests that we could not map, nearly 60% were due to test names whose real meaning could not be discerned and 29% represented tests that were not yet included in the LOINC table. These results suggest that LOINC codes cover most of laboratory tests used in critical care. We have delivered this work to the MIMIC-II researchers, who have included it in their standard MIMIC-II database release so that researchers who use this database in the future will not have to do this work.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.