ArticleJAMIA open2020
Experiences implementing scalable, containerized, cloud-based NLP for extracting biobank participant phenotypes at scale.
Article in JAMIA open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- The SMART Text2FHIR Pipeline.medRxiv : the preprint server for health sciences · 2023Article
- Evaluation of the portability of computable phenotypes with natural language processing in the eMERGE network.Scientific reports · 2023Article
- The SMART Text2FHIR Pipeline.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification.JAMIA open · 2021Article
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
objectiveTo develop scalable natural language processing (NLP) infrastructure for processing the free text in electronic health records (EHRs). MATERIALS AND
methodsWe extend the open-source Apache cTAKES NLP software with several standard technologies for scalability. We remove processing bottlenecks by monitoring component queue size. We process EHR free text for patients in the PrecisionLink Biobank at Boston Children's Hospital. The extracted concepts are made searchable via a web-based portal.
resultsWe processed over 1.2 million notes for over 8000 patients, extracting 154 million concepts. Our largest tested configuration processes over 1 million notes per day. DISCUSSION: The unique information represented by extracted NLP concepts has great potential to provide a more complete picture of patient status.
conclusionNLP large EHR document collections can be done efficiently, in service of high throughput phenotyping.
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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.