ArticleJournal of machine learning research : JMLR2022
Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping.
Article in Journal of machine learning research : JMLR, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- CRCFound: A Colorectal Cancer CT Image Foundation Model Based on Self-Supervised Learning.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Semi-supervised Triply Robust Inductive Transfer Learning.Journal of the American Statistical Association · 2025Article
- PATIENT RECRUITMENT USING ELECTRONIC HEALTH RECORDS UNDER SELECTION BIAS: A TWO-PHASE SAMPLING FRAMEWORK.The annals of applied statistics · 2024Article
- Estimation and Inference for High-Dimensional Generalized Linear Models with Knowledge Transfer.Journal of the American Statistical Association · 2024Article
- Surrogate Assisted Semi-supervised Inference for High Dimensional Risk Prediction.Journal of machine learning research : JMLRArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Electronic Health Record (EHR) data, a rich source for biomedical research, have been successfully used to gain novel insight into a wide range of diseases. Despite its potential, EHR is currently underutilized for discovery research due to its major limitation in the lack of precise phenotype information. To overcome such difficulties, recent efforts have been devoted to developing supervised algorithms to accurately predict phenotypes based on relatively small training datasets with gold standard labels extracted via chart review. However, supervised methods typically require a sizable training set to yield generalizable algorithms, especially when the number of candidate features,
Indexed as
Identifiers
37974910PMC10653017What 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.