ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2017
Correlating Lab Test Results in Clinical Notes with Structured Lab Data: A Case Study in HbA1c and Glucose.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Clinical Information Extraction with Large Language Models: A Case Study on Organ Procurement.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Scalable and interpretable alternative to chart review for phenotype evaluation using standardized structured data from electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Generating real-world evidence from unstructured clinical notes to examine clinical utility of genetic tests: use case in BRCAness.BMC medical informatics and decision making · 2021Article
- Evaluation of provider documentation patterns as a tool to deliver ongoing patient-centred diabetes education and support.International journal of clinical practice · 2020Article
- Integrity of clinical information in computerized order requisitions for diagnostic imaging.Journal of the American Medical Informatics Association : JAMIA · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
It is widely acknowledged that information extraction of unstructured clinical notes using natural language processing (NLP) and text mining is essential for secondary use of clinical data for clinical research and practice. Lab test results are currently structured in most of the electronic health record (EHR) systems. However, for referral patients or lab tests that can be done in non-clinical setting, the results can be captured in unstructured clinical notes. In this study, we proposed a rule-based information extraction system to extract the lab test results with temporal information from clinical notes. The lab test results of glucose and HbA1c from 104 randomly sampled diabetes patients selected from 1996 to 2015 are extracted and further correlated with structured lab test information in the Mayo Clinic EHRs. The system has high F1-scores of 0.964, 0.967 and 0.966 in glucose, HbA1c and overall extraction, respectively.
Identifiers
28815133PMC5543347What OpenQuestion holds
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.