Evidence map›Paper›PMID 41726448›Full record

ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2024

Adjusting Covariate Misclassification in Electronic Health Records-Based Machine Learning Prediction Models.

Shuang Yang, Yonghui Wu, Mei Liu, Jiang Bian, Muxuan Liang, Yi Guo

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PubMed Full text at PMC
In one paragraph

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. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from 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.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Shuang YangDepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA.
Yonghui WuDepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA.
Mei LiuDepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA.
Jiang BianDepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA.
Muxuan LiangDepartment of Biostatistics, University of Florida, Gainesville, Florida, USA.
Yi GuoDepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA.

Funding

Disparities of Alzheimer's disease progression in Sexual Minority IndividualsR01AG080624 · NIA · UNIVERSITY OF FLORIDA · PI Jiang Bian, Yi Guo · 2023 to 2026
$3.1M
Advancing Precision Lung Cancer Surveillance and Outcomes in Diverse Populations (PLuS2)R01CA284646 · NCI · UNIVERSITY OF FLORIDA · PI Dejana K Braithwaite, Michael K Gould · 2023 to 2026
$2.1M
The benefits and harms of lung cancer screening in FloridaR01CA246418 · NCI · UNIVERSITY OF FLORIDA · PI BIAN, JIANG, GUO, YI · 2020 to 2023
$1.7M
NCCDPHP CDC HHS U18 DP006512NCI NIH HHS R01 CA246418NCI NIH HHS R01 CA284646NIA NIH HHS R01 AG080624
6 · The paper itself

Abstract

This study developed and evaluated methods to adjust misclassification errors in electronic health record (EHR)-derived covariates using group-wise and individualized weights based on observed sensitivity and specificity to reduce bias in predictive modeling. Logistic regression, XGBoost, and neural networks predicted follow-up adherence in lung cancer screening. The Lung-RADS category, extracted via natural language processing (NLP), was adjusted using group-wise weights and individualized weights from kernel and multinomial regression. Models with adjusted covariates were compared to naïve (unadjusted) and oracle (true value) models. Performance assessed by the area under the receiver operating characteristic (AUROC) curve across 10%, 20%, and 30% validation sets, showed that adjusted models outperformed naïve models, improving AUROC by 0.3%-10.4%. Compared to oracle models, adjusted models reduced the AUROC gap to 2.0%-7.5%. Individualized weights provided more precise corrections than group-wise weights. This scalable framework mitigates misclassification bias in EHR-derived covariates, enhancing predictive accuracy without resource-intensive manual review.

Indexed as

Electronic Health RecordsLung NeoplasmsMachine LearningBoosting Machine Learning AlgorithmsClassification AlgorithmsHumansLogistic ModelsNatural Language ProcessingNeural Networks, ComputerPrediction AlgorithmsPredictive Learning ModelsROC CurveSensitivity and Specificity

Identifiers

PMID41726448
PMCPMC12919593

What OpenQuestion holds

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Registered trials

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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.