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arXiv:2212.05150 (cs)
[Submitted on 10 Dec 2022]

Title:Improving Precancerous Case Characterization via Transformer-based Ensemble Learning

Authors:Yizhen Zhong, Jiajie Xiao, Thomas Vetterli, Mahan Matin, Ellen Loo, Jimmy Lin, Richard Bourgon, Ofer Shapira
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Abstract:The application of natural language processing (NLP) to cancer pathology reports has been focused on detecting cancer cases, largely ignoring precancerous cases. Improving the characterization of precancerous adenomas assists in developing diagnostic tests for early cancer detection and prevention, especially for colorectal cancer (CRC). Here we developed transformer-based deep neural network NLP models to perform the CRC phenotyping, with the goal of extracting precancerous lesion attributes and distinguishing cancer and precancerous cases. We achieved 0.914 macro-F1 scores for classifying patients into negative, non-advanced adenoma, advanced adenoma and CRC. We further improved the performance to 0.923 using an ensemble of classifiers for cancer status classification and lesion size named entity recognition (NER). Our results demonstrated the potential of using NLP to leverage real-world health record data to facilitate the development of diagnostic tests for early cancer prevention.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2212.05150 [cs.LG]
  (or arXiv:2212.05150v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2212.05150

Submission history

From: Yizhen Zhong [view email]
[v1] Sat, 10 Dec 2022 00:06:28 UTC (1,848 KB)
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