ArticleJournal of biomedical informatics2022
Evaluation of an automated phenotyping algorithm for rheumatoid arthritis.
Article in Journal of biomedical informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled 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.
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, 1 synthesis or guideline pooled it.
- Accuracy of Diagnostic Codes and Algorithms Used to Identify Rheumatoid Arthritis and Juvenile Idiopathic Arthritis in Administrative Claims and Electronic Health Records: Systematic Review and Meta-Analysis.Arthritis care & research · 2026Pooled it
- What does artificial intelligence mean in rheumatology?Archives of rheumatology · 2024Review
- New developments in electronic health record analysis.Nature reviews. Rheumatology · 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
10 authors.
Funding
Abstract
To better understand the challenges of generally implementing and adapting computational phenotyping approaches, the performance of a Phenotype KnowledgeBase (PheKB) algorithm for rheumatoid arthritis (RA) was evaluated on a University of California, Los Angeles (UCLA) patient population, focusing on examining its performance on ambiguous cases. The algorithm was evaluated on a cohort of 4,766 patients, along with a chart review of 300 patients by rheumatologists against accepted diagnostic guidelines. The performance revealed low sensitivity towards specific subtypes of positive RA cases, which suggests revisions in features used for phenotyping. A close examination of select cases also indicated a significant portion of patients with missing data, drawing attention to the need to consider data integrity as an integral part of phenotyping pipelines, as well as issues around the usability of various codes for distinguishing cases. We use patterns in the PheKB algorithm's errors to further demonstrate important considerations when designing a phenotyping algorithm.
Indexed as
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.