Evidence map›Paper›PMID 37034815›Full record

ArticlemedRxiv : the preprint server for health sciences2023

The SMART Text2FHIR Pipeline.

Timothy A Miller, Andrew J McMurry, James Jones, Daniel Gottlieb, Kenneth D Mandl

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Timothy A MillerComputational Health Informatics Program, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
Andrew J McMurryComputational Health Informatics Program, Boston Children's Hospital, Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
James JonesComputational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.
Daniel GottliebComputational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.
Kenneth D MandlComputational Health Informatics Program, Boston Children's Hospital, Department of Pediatrics, Department of Biomedical Informatics, Harvard Medical School, 401 Park Drive, Landmark Center, 5th Floor East, Boston, MA 02215, U.S.A.
Boston Children's Hospital · US

Funding

Instrumenting the Delivery System for a Genomics Research Information CommonsU01TR002623 · NCATS · BOSTON CHILDREN'S HOSPITAL · PI MANDL, KENNETH D. · 2019 to 2024
$8.6M
Learning Universal Patient Representations with Hierarchical TransformersR01LM012973 · NLM · BOSTON CHILDREN'S HOSPITAL · PI Timothy A Miller · 2019 to 2026
$3.5M
Automated domain adaptation for clinical natural language processingR01LM012918 · NLM · BOSTON CHILDREN'S HOSPITAL · PI MILLER, TIMOTHY A · 2018 to 2020
$1.2M
NCATS NIH HHS U01 TR002623NLM NIH HHS R01 LM012918NLM NIH HHS R01 LM012973
6 · The paper itself

Abstract

Objective: To implement an open source, free, and easily deployable high throughput natural language processing module to extract concepts from clinician notes and map them to Fast Healthcare Interoperability Resources (FHIR). Materials and Methods: Using a popular open-source NLP tool (Apache cTAKES), we create FHIR resources that use modifier extensions to represent negation and NLP sourcing, and another extension to represent provenance of extracted concepts. Results: The SMART Text2FHIR Pipeline is an open-source tool, released through standard package managers, and publicly available container images that implement the mappings, enabling ready conversion of clinical text to FHIR. Discussion: With the increased data liquidity because of new interoperability regulations, NLP processes that can output FHIR can enable a common language for transporting structured and unstructured data. This framework can be valuable for critical public health or clinical research use cases. Conclusion: Future work should include mapping more categories of NLP-extracted information into FHIR resources and mappings from additional open-source NLP tools.

Indexed as

electronic health recordsInteroperabilityNatural language processing

Identifiers

PMID37034815
PMCPMC10081439
OpenAlexW4360823172

What OpenQuestion holds

Textmetadata
LicenceCC BY-ND
Read underepoch 390

Registered trials

None linked

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.