ArticleClinical and translational science2018
Electronic Health Record Phenotypes for Precision Medicine: Perspectives and Caveats From Treatment of Breast Cancer at a Single Institution.
Article in Clinical and translational science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review.BioMed research international · 2022Pooled it
- Review
- A genome-wide Association study of the Count of Codeine prescriptions.Scientific reports · 2024Article
- Article
- Natural Language Processing Applications for Computer-Aided Diagnosis in Oncology.Diagnostics (Basel, Switzerland) · 2023Review
- Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing.JCO clinical cancer informatics · 2022Article
- CancerBERT: a cancer domain-specific language model for extracting breast cancer phenotypes from electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Automated Phenotyping Tool for Identifying Developmental Language Disorder Cases in Health Systems Data (APT-DLD): A New Research Algorithm for Deployment in Large-Scale Electronic Health Record Systems.Journal of speech, language, and hearing research : JSLHR · 2020Article
- Common data elements of breast cancer for research databases: A systematic review.Journal of family medicine and primary care · 2020Review
- A frame semantic overview of NLP-based information extraction for cancer-related EHR notes.Journal of biomedical informatics · 2019Review
- Annotating and detecting phenotypic information for chronic obstructive pulmonary disease.JAMIA open · 2019Article
- Using Electronic Health Records To Generate Phenotypes For Research.Current protocols in human genetics · 2019Article
- From hype to reality: data science enabling personalized medicine.BMC medicine · 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
5 authors.
Funding
Abstract
Precision medicine is at the forefront of biomedical research. Cancer registries provide rich perspectives and electronic health records (EHRs) are commonly utilized to gather additional clinical data elements needed for translational research. However, manual annotation is resource-intense and not readily scalable. Informatics-based phenotyping presents an ideal solution, but perspectives obtained can be impacted by both data source and algorithm selection. We derived breast cancer (BC) receptor status phenotypes from structured and unstructured EHR data using rule-based algorithms, including natural language processing (NLP). Overall, the use of NLP increased BC receptor status coverage by 39.2% from 69.1% with structured medication information alone. Using all available EHR data, estrogen receptor-positive BC cases were ascertained with high precision (P = 0.976) and recall (R = 0.987) compared with gold standard chart-reviewed patients. However, status negation (R = 0.591) decreased 40.2% when relying on structured medications alone. Using multiple EHR data types (and thorough understanding of the perspectives offered) are necessary to derive robust EHR-based precision medicine phenotypes.
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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.