ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2018
A Frame-Based NLP System for Cancer-Related Information Extraction.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- Performance of Natural Language Processing for Information Extraction From Electronic Health Records Within Cancer: Systematic Review.JMIR medical informatics · 2025Pooled it
- Extracting cancer concepts from clinical notes using natural language processing: a systematic review.BMC bioinformatics · 2023Pooled it
- Development and Evaluation of Natural Language Processing Methods for Extracting Key Melanoma Pathology Concepts.Studies in health technology and informatics · 2025Article
- An End-to-End Natural Language Processing System for Automatically Extracting Radiation Therapy Events From Clinical Texts.International journal of radiation oncology, biology, physics · 2023Article
- Article
- Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing.JCO clinical cancer informatics · 2022Article
- Using Natural Language Processing and Machine Learning to Preoperatively Predict Lymph Node Metastasis for Non-Small Cell Lung Cancer With Electronic Medical Records: Development and Validation Study.JMIR medical informatics · 2022Article
- Extracting clinical named entity for pituitary adenomas from Chinese electronic medical records.BMC medical informatics and decision making · 2022Article
- Automatic Extraction of Lung Cancer Staging Information From Computed Tomography Reports: Deep Learning Approach.JMIR medical informatics · 2021Article
- Detecting sarcasm in multi-domain datasets using convolutional neural networks and long short term memory network model.PeerJ. Computer science · 2021Article
- Natural language processing systems for pathology parsing in limited data environments with uncertainty estimation.JAMIA open · 2020Article
- Deep learning in clinical natural language processing: a methodical review.Journal of the American Medical Informatics Association : JAMIA · 2020Review
- Building Cancer Diagnosis Text to OncoTree Mapping Pipelines for Clinical Sequencing Data Integration and Curation.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Natural language processing for populating lung cancer clinical research data.BMC medical informatics and decision making · 2019Article
- A frame semantic overview of NLP-based information extraction for cancer-related EHR notes.Journal of biomedical informatics · 2019Review
- Enhancing clinical concept extraction with contextual embeddings.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- Relation Extraction from Clinical Narratives Using Pre-trained Language Models.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
We propose a frame-based natural language processing (NLP) method that extracts cancer-related information from clinical narratives. We focus on three frames: cancer diagnosis, cancer therapeutic procedure, and tumor description. We utilize a deep learning-based approach, bidirectional Long Short-term Memory (LSTM) Conditional Random Field (CRF), which uses both character and word embeddings. The system consists of two constituent sequence classifiers: a frame identification (lexical unit) classifier and a frame element classifier. The classifier achieves an F
Indexed as
Identifiers
30815198PMC6371330W2942217618What 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.