ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2019
Achievability to Extract Specific Date Information for Cancer Research.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Large language models for cancer registry abstraction: a real-world evaluation across models, variables, and cancer types.medRxiv : the preprint server for health sciences · 2026Article
- Review
- 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
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Approach to machine learning for extraction of real-world data variables from electronic health records.Frontiers in pharmacology · 2023Article
- 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
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Accurate identification of temporal information such as date is crucial for advancing cancer research which often requires precise date information associated with related cancer events. However, there is a gap for existing natural language processing (NLP) systems to identify dates for specific cancer research studies. Illustrated with two case studies, we investigated the feasibility, evaluated the performances and discussed the challenges of date information extraction for cancer research.
Indexed as
Identifiers
32308886PMC7153063What 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.