Evidence map›Paper›PMID 41726470›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024

From Food to Clinic: Mapping FoodOn to the UMLS to Enable Nutritional Decision Support.

Indra Neil Sarkar

Abstract read
In one paragraph

Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2024. 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.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Indra Neil SarkarCenter for Biomedical Informatics, Brown University, Providence, RI.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Food and nutrition knowledge is recognized as a fundamental factor for the health and well-being of communities; however, its integration into biomedical and health knowledge systems is limited by the absence of standardized ontologies that encapsulate food-related concepts. This study mapped FoodOn, an open-source food ontology, to the Unified Medical Language System (UMLS) metathesaurus, a compendium of biomedical ontologies. As the first systematic mapping of a food ontology to the UMLS, the results of this study provide an ontological foundation for incorporating dietary data into clinical and public health workflows. The findings suggest that expanding the representation of food concepts in biomedical ontologies could enhance the potential to incorporate food and nutritional into clinical decision-making and research. Furthermore, this work lays the groundwork for integrating food-based therapies from traditional medicine systems (e.g., Ayurveda and Traditional Chinese Medicine) into contemporary clinical knowledge frameworks to support more holistic approaches to health care.

Indexed as

Biological OntologiesDecision Support Systems, ClinicalFoodUnified Medical Language SystemHumans

Identifiers

PMID41726470
PMCPMC12919546

What Socratic holds

Textmetadata
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 Socratic graph.