ReviewCancer research2019
Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records.
Review in Cancer research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers, 2 of them syntheses 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
88 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic review of current natural language processing methods and applications in cardiology.Heart (British Cardiac Society) · 2022Pooled it
- Application of Artificial Intelligence Methods to Pharmacy Data for Cancer Surveillance and Epidemiology Research: A Systematic Review.JCO clinical cancer informatics · 2020Pooled it
- Review
- Natural language processing to enhance rheumatoid arthritis care in clinical studies: a scoping review of applications, data, approaches, challenges and future directions.Rheumatology international · 2026Article
- A comparative study of large language models (ChatGPT/DeepSeek) in generating structured thyroid ultrasound reports.Scientific reports · 2026Article
- Artificial Intelligence Applications for Automated Data Extraction and Secondary Use of Clinical Information in Uro-oncology: A Systematic Review.European urology open science · 2026Review
- An agentic AI system enhances clinical detection of immunotherapy toxicities: a multi-phase validation study.medRxiv : the preprint server for health sciences · 2026Article
- Enhancing cause of death prediction: development and validation of machine learning models using multimodal data across multiple health-care sites.JAMIA open · 2026Article
- Enabling Just-in-Time Clinical Oncology Analysis With Large Language Models: Feasibility and Validation Study Using Unstructured Synthetic Data.JMIR medical informatics · 2025Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery.Current oncology (Toronto, Ont.) · 2025Review
- MIC: Breast Cancer Multi-label Diagnostic Framework Based on Multi-modal Fusion Interaction.Journal of imaging informatics in medicine · 2025Article
- An Extraction Tool for Venous Thromboembolism Symptom Identification in Primary Care Notes to Facilitate Electronic Clinical Quality Measure Reporting: Algorithm Development and Validation Study.JMIR medical informatics · 2025Article
- Natural Language Processing of Radiology Reports to Assess Survival in Patients with Advanced Melanoma.Cancers · 2025Article
- Using Large Language Models to Automate Data Extraction From Surgical Pathology Reports: Retrospective Cohort Study.JMIR formative research · 2025Article
- Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review.JMIR cancer · 2025Article
- Tumor-Agnostic Therapies in Practice: Challenges, Innovations, and Future Perspectives.Cancers · 2025Review
- LCD benchmark: long clinical document benchmark on mortality prediction for language models.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Current status and future direction of cancer research using artificial intelligence for clinical application.Cancer science · 2025Review
- The Transformative Potential of Large Language Models in Mining Electronic Health Records Data: Content Analysis.JMIR medical informatics · 2025Article
28 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Current models for correlating electronic medical records with -omics data largely ignore clinical text, which is an important source of phenotype information for patients with cancer. This data convergence has the potential to reveal new insights about cancer initiation, progression, metastasis, and response to treatment. Insights from this real-world data will catalyze clinical care, research, and regulatory activities. Natural language processing (NLP) methods are needed to extract these rich cancer phenotypes from clinical text. Here, we review the advances of NLP and information extraction methods relevant to oncology based on publications from PubMed as well as NLP and machine learning conference proceedings in the last 3 years. Given the interdisciplinary nature of the fields of oncology and information extraction, this analysis serves as a critical trail marker on the path to higher fidelity oncology phenotypes from real-world data.
Indexed as
Identifiers
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.