ArticleNature communications2024
Shareable artificial intelligence to extract cancer outcomes from electronic health records for precision oncology research.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Research trends and hotspots of circulating tumor DNA in colorectal cancer: a bibliometric study.Frontiers in oncology · 2025Pooled it
- Echo Chambers: Bias and Representation in Cardiac Imaging Datasets for Artificial Intelligence.Current cardiology reports · 2026Review
- Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.Cancer medicine · 2026Review
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Review
- Clinical large language model centered on electronic medical records.NPJ digital medicine · 2026Article
- Interpretable habitat and peritumoral radiomics from multiparametric MRI for preoperative high-risk prostate cancer prediction: a multi-institutional study.Journal of translational medicine · 2026Article
- Article
- Distillation of a Locally Deployable 8B Language Model for Lung Histology Abstraction: A 33-Report Proof-of-Concept Study.Journal of multidisciplinary healthcare · 2026Article
- Learning Patient Similarity from Genomics for Precision Oncology.medRxiv : the preprint server for health sciences · 2025Article
- Integrating a Shareable Artificial Intelligence Model Into Clinical Research for Cancer Recurrence in Patients With Breast and Colorectal Cancer.JCO clinical cancer informatics · 2025Article
- Systematic curation and analysis of ovarian cancer data across multiple electronic record systems held within the UK National Health Service: a tertiary referral centre experience.ESMO real world data and digital oncology · 2025Article
- Review
- Empirical evaluation of artificial intelligence distillation techniques for ascertaining cancer outcomes from electronic health records.NPJ digital medicine · 2025Article
- New horizons at the interface of artificial intelligence and translational cancer research.Cancer cell · 2025Review
- The clinical application of artificial intelligence in cancer precision treatment.Journal of translational medicine · 2025Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Databases that link molecular data to clinical outcomes can inform precision cancer research into novel prognostic and predictive biomarkers. However, outside of clinical trials, cancer outcomes are typically recorded only in text form within electronic health records (EHRs). Artificial intelligence (AI) models have been trained to extract outcomes from individual EHRs. However, patient privacy restrictions have historically precluded dissemination of these models beyond the centers at which they were trained. In this study, the vulnerability of text classification models trained directly on protected health information to membership inference attacks is confirmed. A teacher-student distillation approach is applied to develop shareable models for annotating outcomes from imaging reports and medical oncologist notes. 'Teacher' models trained on EHR data from Dana-Farber Cancer Institute (DFCI) are used to label imaging reports and discharge summaries from the Medical Information Mart for Intensive Care (MIMIC)-IV dataset. 'Student' models are trained to use these MIMIC documents to predict the labels assigned by teacher models and sent to Memorial Sloan Kettering (MSK) for evaluation. The student models exhibit high discrimination across outcomes in both the DFCI and MSK test sets. Leveraging private labeling of public datasets to distill publishable clinical AI models from academic centers could facilitate deployment of machine learning to accelerate precision oncology research.
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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.