ArticleNature cancer2021
An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication.
Article in Nature cancer, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 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
46 citing papers in PubMed, 101 citations in OpenAlex.
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- A non-invasive end-to-end intelligent assistance system for breast ultrasound.Nature communications · 2026Article
- Artificial Intelligence in Oncology: A Comprehensive Cross-Cancer Translational Readiness Analysis Across 18 Malignancies.Cancers · 2026Review
- Integrating Artificial Intelligence with Global Genomic Resources: A Narrative Review of Implications for Precision Medicine.Journal of multidisciplinary healthcare · 2026Review
- Virtual medicine: medical AI in human health and diseases.Military Medical Research · 2026Review
- Artificial Intelligence in Radiology: Advancing Precision, Accuracy, and Early Detection in Cancer Diagnosis.Cureus · 2025Review
- Evaluating the application of dynamic prediction models in oncological prognostic studies with repeated measurement predictors.NPJ precision oncology · 2025Article
- Integrating big data and artificial intelligence to predict progression in multiple sclerosis: challenges and the path forward.Journal of neuroengineering and rehabilitation · 2025Review
- Self-Normalizing Multi-Omics Neural Network for Pan-Cancer Prognostication.International journal of molecular sciences · 2025Article
- Review
- Diagnostic framework to validate clinical machine learning models locally on temporally stamped data.Communications medicine · 2025Article
- Advances in molecular pathology and therapy of non-small cell lung cancer.Signal transduction and targeted therapy · 2025Review
- Multi-institutional atlas of brain metastases informs spatial modeling for precision imaging and personalized therapy.Nature communications · 2025Article
- From manual clinical criteria to machine learning algorithms: Comparing outcome endpoints derived from diverse electronic health record data modalities.PLOS digital health · 2025Article
- AI-assisted SERS imaging method for label-free and rapid discrimination of clinical lymphoma.Journal of nanobiotechnology · 2025Article
- Continuous multimodal data supply chain and expandable clinical decision support for oncology.NPJ digital medicine · 2025Article
- Decoding pan-cancer treatment outcomes using multimodal real-world data and explainable artificial intelligence.Nature cancer · 2025Article
- Engineering of Generative Artificial Intelligence and Natural Language Processing Models to Accurately Identify Arrhythmia Recurrence.Circulation. Arrhythmia and electrophysiology · 2025Article
- Artificial intelligence for precision management of epithelial ovarian cancer: a comprehensive review.Frontiers in medicine · 2025Review
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Authors and funding
18 authors at 6 institutions in 4 countries.
Funding
Abstract
Despite widespread adoption of electronic health records (EHRs), most hospitals are not ready to implement data science research in the clinical pipelines. Here, we develop MEDomics, a continuously learning infrastructure through which multimodal health data are systematically organized and data quality is assessed with the goal of applying artificial intelligence for individual prognosis. Using this framework, currently composed of thousands of individuals with cancer and millions of data points over a decade of data recording, we demonstrate prognostic utility of this framework in oncology. As proof of concept, we report an analysis using this infrastructure, which identified the Framingham risk score to be robustly associated with mortality among individuals with early-stage and advanced-stage cancer, a potentially actionable finding from a real-world cohort of individuals with cancer. Finally, we show how natural language processing (NLP) of medical notes could be used to continuously update estimates of prognosis as a given individual's disease course unfolds.
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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.