ArticleCureus2019
Development of a Natural Language Processing Tool to Extract Radiation Treatment Sites.
Article in Cureus, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis 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
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies.Journal of biomedical semantics · 2020Pooled it
- Integrating natural language processing into radiation oncology: a practical guide to transformer architecture and large language models.BJR artificial intelligence · 2025Review
- 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Currently, radiation oncology-specific electronic medical records (EMRs) allow providers to input the radiation treatment site using free text. The purpose of this study is to develop a natural language processing (NLP) tool to extract encoded data from radiation treatment sites in an EMR. Treatment sites were extracted from all patients who completed treatment in our department from April 1, 2011, to April 30, 2013. A system was designed to extract the Unified Medical Language System (UMLS) concept codes using a sample of 11,018 unique site names from 31118 radiation therapy (RT) sites. Among those, 5500 unique site name strings that constitute approximately half of the sample were spared as a test set to evaluate the final system. A dictionary and calculated n-gram statistics using UMLS concepts from related semantic types were combined with manually encoded data. There was an average of 2.2 sites per patient. Prior to extraction, the 20 most common unique treatment sites were used 4215 times (38.3%). The most common treatment site was whole brain RT, which was entered using 27 distinct terms for a total of 1063 times. The customized NLP solution displayed great gains as compared to other systems, with a recall of 0.99 and a precision of 0.99. A customized NLP tool was extracting encoded data from radiation treatment sites in an EMR with great accuracy. This can be integrated into a repository of demographic, genomic, treatment, and outcome data to advance personalized oncologic care.
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.