ReviewJournal of biomedical informatics2019
A frame semantic overview of NLP-based information extraction for cancer-related EHR notes.
Review in Journal of biomedical informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 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
46 citing papers in PubMed, 2 syntheses or guidelines pooled it, 4 citations in OpenAlex.
- Performance of Natural Language Processing for Information Extraction From Electronic Health Records Within Cancer: Systematic Review.JMIR medical informatics · 2025Pooled it
- Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2022Pooled it
- Using large language models for clinical staging of colorectal cancer from imaging reports: a pilot study.Annals of surgical treatment and research · 2025Article
- Large Language Models in Population Oncology: A Contemporary Review on the Use of Large Language Models to Support Data Collection, Aggregation, and Analysis in Cancer Care and Research.JCO clinical cancer informatics · 2025Review
- Article
- Open-Source Hybrid Large Language Model Integrated System for Extraction of Breast Cancer Treatment Pathway From Free-Text Clinical Notes.JCO clinical cancer informatics · 2025Article
- Using Large Language Models to Automate Data Extraction From Surgical Pathology Reports: Retrospective Cohort Study.JMIR formative research · 2025Article
- Advantages and limitations of large language models for antibiotic prescribing and antimicrobial stewardship.npj antimicrobials and resistance · 2025Review
- Post-discharge suicide prediction among US veterans using natural language processing-enriched social and behavioral determinants of health.Npj mental health research · 2025Article
- Development of an AI model for predicting hypoxia status and prognosis in non-small cell lung cancer using multi-modal data.Translational lung cancer research · 2024Article
- CACER: Clinical concept Annotations for Cancer Events and Relations.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Prediction of candidemia with machine learning techniques: state of the art.Future microbiology · 2024Review
- Natural language processing systems for extracting information from electronic health records about activities of daily living. A systematic review.JAMIA open · 2024Review
- Advancing equity in breast cancer care: natural language processing for analysing treatment outcomes in under-represented populations.BMJ health & care informatics · 2024Article
- Collecting routine and timely cancer stage at diagnosis by implementing a cancer staging tiered framework: the Western Australian Cancer Registry experience.BMC health services research · 2024Article
- Predicting Suicide Among US Veterans Using Natural Language Processing-enriched Social and Behavioral Determinants of Health.Research square · 2024Article
- Article
- From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer.Diagnostics (Basel, Switzerland) · 2024Review
- A review of research on eligibility criteria for clinical trials.Clinical and experimental medicine · 2023Review
- Adverse event signal extraction from cancer patients' narratives focusing on impact on their daily-life activities.Scientific reports · 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
3 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveThere is a lot of information about cancer in Electronic Health Record (EHR) notes that can be useful for biomedical research provided natural language processing (NLP) methods are available to extract and structure this information. In this paper, we present a scoping review of existing clinical NLP literature for cancer.
methodsWe identified studies describing an NLP method to extract specific cancer-related information from EHR sources from PubMed, Google Scholar, ACL Anthology, and existing reviews. Two exclusion criteria were used in this study. We excluded articles where the extraction techniques used were too broad to be represented as frames (e.g., document classification) and also where very low-level extraction methods were used (e.g. simply identifying clinical concepts). 78 articles were included in the final review. We organized this information according to frame semantic principles to help identify common areas of overlap and potential gaps.
resultsFrames were created from the reviewed articles pertaining to cancer information such as cancer diagnosis, tumor description, cancer procedure, breast cancer diagnosis, prostate cancer diagnosis and pain in prostate cancer patients. These frames included both a definition as well as specific frame elements (i.e. extractable attributes). We found that cancer diagnosis was the most common frame among the reviewed papers (36 out of 78), with recent work focusing on extracting information related to treatment and breast cancer diagnosis.
conclusionThe list of common frames described in this paper identifies important cancer-related information extracted by existing NLP techniques and serves as a useful resource for future researchers requiring cancer information extracted from EHR notes. We also argue, due to the heavy duplication of cancer NLP systems, that a general purpose resource of annotated cancer frames and corresponding NLP tools would be valuable.
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.