ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2021
Creation of a Mapped, Machine-Readable Taxonomy to Facilitate Extraction of Social Determinants of Health Data from Electronic Health Records.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Subtyping Social Determinants of Health in the "All of Us" Program: Network Analysis and Visualization Study.Journal of medical Internet research · 2025Article
- Development and validation of computable social phenotypes for health-related social needs.JAMIA open · 2025Article
- Subtyping Social Determinants of Health inmedRxiv : the preprint server for health sciences · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A comprehensive, mapped social determinants of health (SDH) taxonomy in machine readable format was developed. The framework is intended to facilitate the extraction of social risk factors (SRFs) out of electronic health record (EHR) data and categorize them by domain and determinant to facilitate interpretation. Where other SDH frameworks have been focused on data input, this framework is designed from a data extraction point of view using EHR data in conjunction with published literature, public health policy documents, and official crosswalk maps. Frameworks developed by leading public health organizations were reviewed and synthesized to create an SDH framework comprising of 97 distinct SRFs organized under 16 domains. 2,329 medical codes across three standardized medical vocabularies, 10,896 free-text diagnosis descriptors, and 25 health insurance keywords were mapped to individual SRFs in the SDH framework. The framework is available as an open-source resource in Python dictionary or JSON format.
Indexed as
Identifiers
35308929PMC8861691What 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.