ArticleNature communications2022
Uncovering interpretable potential confounders in electronic medical records.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- CLEAR: an auditable foundation model for radiology grounded in clinical concepts.Nature biomedical engineering · 2026Article
- Intelligent MDT treatment decision making for stage III NSCLC using dual level embedding and three level explanation.Scientific reports · 2026Article
- Utilization of Computable Phenotypes in Electronic Health Record Research: A Review and Case Study in Atopic Dermatitis.The Journal of investigative dermatology · 2025Review
- Accurate treatment effect estimation using inverse probability of treatment weighting with deep learning.JAMIA open · 2025Article
- Step-by-step causal analysis of EHRs to ground decision-making.PLOS digital health · 2025Article
- An open-source framework for end-to-end analysis of electronic health record data.Nature medicine · 2024Article
- Reconstruction of patient-specific confounders in AI-based radiologic image interpretation using generative pretraining.Cell reports. Medicine · 2024Article
- MF-MNER: Multi-models Fusion for MNER in Chinese Clinical Electronic Medical Records.Interdisciplinary sciences, computational life sciences · 2024Article
- Challenges in and Opportunities for Electronic Health Record-Based Data Analysis and Interpretation.Gut and liver · 2024Review
- Safety Issues of Herb-Warfarin Interactions.Current drug metabolism · 2024Review
- Design, implementation, and inferential issues associated with clinical trials that rely on data in electronic medical records: a narrative review.BMC medical research methodology · 2023Review
- Intelligent oncology: The convergence of artificial intelligence and oncology.Journal of the National Cancer Center · 2023Review
- Diagnostic Accuracy of Differential-Diagnosis Lists Generated by Generative Pretrained Transformer 3 Chatbot for Clinical Vignettes with Common Chief Complaints: A Pilot Study.International journal of environmental research and public health · 2023Article
- Practical Considerations for Developing Clinical Natural Language Processing Systems for Population Health Management and Measurement.JMIR medical informatics · 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
5 authors.
Funding
Abstract
Randomized clinical trials (RCT) are the gold standard for informing treatment decisions. Observational studies are often plagued by selection bias, and expert-selected covariates may insufficiently adjust for confounding. We explore how unstructured clinical text can be used to reduce selection bias and improve medical practice. We develop a framework based on natural language processing to uncover interpretable potential confounders from text. We validate our method by comparing the estimated hazard ratio (HR) with and without the confounders against established RCTs. We apply our method to four cohorts built from localized prostate and lung cancer datasets from the Stanford Cancer Institute and show that our method shifts the HR estimate towards the RCT results. The uncovered terms can also be interpreted by oncologists for clinical insights. We present this proof-of-concept study to enable more credible causal inference using observational data, uncover meaningful insights from clinical text, and inform high-stakes medical decisions.
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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.