ReviewCancers2022
Considerations for the Use of Machine Learning Extracted Real-World Data to Support Evidence Generation: A Research-Centric Evaluation Framework.
Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed, 25 citations in OpenAlex.
- Applications of artificial intelligence in dyspnoea management for patients with advanced lung cancer: a scoping review protocol.BMJ open · 2026Article
- The Pan-Tumor Landscape of Gene Amplifications and Copy Number Amplification Ratio for Established and Emerging Clinical Targets.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Real-world progression-free survival 2 (PFS2) and tumor response of CDK4/6 inhibitors plus an aromatase inhibitor in patients with HR+/HER2- metastatic breast cancer in US routine clinical practice.Breast (Edinburgh, Scotland) · 2026Observational
- Real-world treatment durations, subsequent treatments, and switching of CDK4/6 inhibitors among patients with HR+/HER2- metastatic breast cancer.The oncologist · 2026Observational
- Maximizing the impact of real-world evidence on cancer treatment: what do we need to achieve it?Journal of comparative effectiveness research · 2026Review
- Ensuring Reliability of Curated Electronic Health Record-Derived Data: The Validation of Accuracy for Large Language Model-/Machine Learning-Extracted Information and Data (VALID) Framework.JCO clinical cancer informatics · 2026Article
- First-line cyclin-dependent kinase 4 and 6 inhibitors in combination with an aromatase inhibitor for HR+/HER2- metastatic breast cancer: A real-world cost-effectiveness assessment in a US Medicare-eligible population.Journal of managed care & specialty pharmacy · 2025Article
- Real-World Evidence Acceptability and Use in Breast Cancer Treatment Decision-Making in the United States: Call-to-Action from a Multidisciplinary Think Tank.Advances in therapy · 2025Article
- Human epidermal growth factor receptor 2 (HER2) expression dynamics between diagnosis and recurrence in patients with breast cancer using artificial intelligence and electronic health records: the RosHER study.ESMO real world data and digital oncology · 2025Article
- Article
- Assessing the use of unstructured electronic health record data to identify exposure to firearm violence.JAMIA open · 2024Article
- How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review.Mayo Clinic proceedings. Digital health · 2024Review
- Artificial Intelligence in Medical Affairs: A New Paradigm with Novel Opportunities.Pharmaceutical medicine · 2024Review
- Assessing Real-World Data From Electronic Health Records for Health Technology Assessment: The SUITABILITY Checklist: A Good Practices Report of an ISPOR Task Force.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2024Article
- Raising the Bar for Real-World Data in Oncology: Approaches to Quality Across Multiple Dimensions.JCO clinical cancer informatics · 2024Article
- Postprediction Inference for Clinical Characteristics Extracted With Machine Learning on Electronic Health Records.JCO clinical cancer informatics · 2023Article
- Replication of Real-World Evidence in Oncology Using Electronic Health Record Data Extracted by Machine Learning.Cancers · 2023Article
- Article
- Approach to machine learning for extraction of real-world data variables from electronic health records.Frontiers in pharmacology · 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
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A vast amount of real-world data, such as pathology reports and clinical notes, are captured as unstructured text in electronic health records (EHRs). However, this information is both difficult and costly to extract through human abstraction, especially when scaling to large datasets is needed. Fortunately, Natural Language Processing (NLP) and Machine Learning (ML) techniques provide promising solutions for a variety of information extraction tasks such as identifying a group of patients who have a specific diagnosis, share common characteristics, or show progression of a disease. However, using these ML-extracted data for research still introduces unique challenges in assessing validity and generalizability to different cohorts of interest. In order to enable effective and accurate use of ML-extracted real-world data (RWD) to support research and real-world evidence generation, we propose a research-centric evaluation framework for model developers, ML-extracted data users and other RWD stakeholders. This framework covers the fundamentals of evaluating RWD produced using ML methods to maximize the use of EHR data for research purposes.
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What 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.