ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024
Derivation and Experimental Performance of Standard and Novel Uncertainty Calibration Techniques.
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. Cited by 2 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
2 citing papers in PubMed.
- Predictive performance precision analysis in medicine: identification of low-confidence predictions at patient and profile levels (MED3pa I).Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Auditor models to suppress poor artificial intelligence predictions can improve human-artificial intelligence collaborative performance.Journal of the American Medical Informatics Association : JAMIA · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
To aid in the transparency of state-of-the-art machine learning models, there has been considerable research performed in uncertainty quantification (UQ). UQ aims to quantify what a model does not know by measuring variation of the model under stochastic conditions and has been demonstrated to be a potentially powerful tool for medical AI. Evaluation of UQ, however, is largely constrained to visual analysis. In this work, we expand upon the Rejection Classification Index (RC-Index) and introduce the relative RC-Index as measures of uncertainty based on rejection classification curves. We hypothesize that rejection classification curves can be used as a basis to derive a metric of how well a given arbitrary uncertainty quantification metric can identify potentially incorrect predictions by an ML model. We compare RC-Index and rRC-Index to established measures based on lift curves.
Indexed as
Identifiers
40417559PMC12099399What 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.