ArticleJCO oncology advances2025
Development and Validation of a Natural Language Processing Algorithm Using Electronic Health Record Data to Identify Breast Cancer Patients With Low Social Support.
Article in JCO oncology advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Evaluation of the Electronic Health Record-Support Social Support Score in Breast Cancer: Comparison of Count and Item Response Theory Scores.Population health management · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
purposeSocial support is important in the management of breast cancer. We developed a series of structured variables or modules from electronic health record (EHR) data to form the basis for the development of EHRsupport, an EHR-based measure of social support.
methodsWe built a natural language processing (NLP) algorithm from clinical notes in 7,989 women diagnosed from January 2006 to September 2021 with invasive breast cancer. Our team reviewed charts, developed a lexicon, manually crafted rules, applied the NLP algorithm to 565,258 EHR notes and 68,760 patient messages -1 to +3 months around diagnosis, combined terms into separate modules, and tuned (N = 40), updated, and tested (N = 100) these versus chart review. We further developed modules from structured data sources.
resultsWe identified and developed 11 modules from unstructured data: spouse/partner status, parenthood status, (clinical) visit support, living situation (alone or with others), friend or other support, positive social support, negative (lack of) social support, deceased person, transportation issues, relationship conflict or stress, and social isolation. Module data availability ranged from 1.4% for social isolation to 92.0% for spouse/partner. Modules were accurate (0.81-0.95). The most highly available groups had moderate to excellent F1 scores (0.75-1.00), precision or positive predictive value (0.61-1.00), and recall or sensitivity (0.75-1.00). Recall and precision were more variable among rarer modules. Five percent of patients lacked an in-state emergency contact and 3% of patients had diagnostic codes for low support at any time point.
conclusionThe EHRsupport algorithm accurately identified social support data, supporting the development of a clinical tool that can be used to identify patients with low social support.
Identifiers
What 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.