ArticleJMIR AI2025
Deep Learning Models to Screen Electronic Health Records for Breast and Colorectal Cancer Progression: Performance Evaluation Study.
Article in JMIR AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
No grant is acknowledged in the PubMed record.
Abstract
backgroundCancer progression is an important outcome in cancer research. However, it is frequently documented only in electronic health records (EHRs) as unstructured text, which requires lengthy and costly chart reviews to extract for retrospective studies.
objectiveThis study aimed to evaluate the performance of 3 deep learning language models in determining breast and colorectal cancer progression in EHRs.
methodsEHRs for individuals diagnosed with stage 4 breast or colorectal cancer between 2004 and 2020 in Manitoba, Canada, were extracted. A chart review was conducted to identify cancer progression in each EHR. Data were analyzed with pretrained deep learning language models (Bio+ClinicalBERT, Clinical-BigBird, and Clinical-Longformer). Sensitivity, positive predictive value, area under the curve, and scaled Brier scores were used to evaluate performance. Influential tokens were identified by removing and adding tokens to EHRs and examining changes in predicted probabilities.
resultsClinical-BigBird and Clinical-Longformer models for breast and colorectal cancer cohorts demonstrated higher accuracy than the Bio+ClinicalBERT models (scaled Brier scores for breast cancer models: 0.70-0.79 vs 0.49-0.71; scaled Brier scores for colorectal cancer models: 0.61-0.65 vs 0.49-0.61). The same models also demonstrated higher sensitivity (breast cancer models: 86.6%-94.3% vs 76.6%-87.1%; colorectal cancer models: 73.1%-78.9% vs 62.8%-77.0%) and positive predictive value (breast cancer models: 77.9%-92.3% vs 80.6%-85.5%; colorectal cancer models: 81.6%-86.3% vs 72.9%-82.9%) compared to Bio+ClinicalBERT models. All models could remove more than 84% of charts from the chart review process. The most influential token was the word progression, which was influenced by the presence of other tokens and its position within an EHR.
conclusionsThe deep learning language models could help identify breast and colorectal cancer progression in EHRs and remove most charts from the chart review process. A limited number of tokens may influence model predictions. Improvements in model performance could be obtained by increasing the training dataset size and analyzing EHRs at the sentence level rather than at the EHR level.
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.