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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2506.11410 |
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| _version_ | 1866909647611363328 |
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| author | Lau, Wilson Kim, Youngwon Parasa, Sravanthi Haque, Md Enamul Oka, Anand Nanduri, Jay |
| author_facet | Lau, Wilson Kim, Youngwon Parasa, Sravanthi Haque, Md Enamul Oka, Anand Nanduri, Jay |
| contents | The incidence rate of early-onset colorectal cancer (EoCRC, age < 45) has increased every year, but this population is younger than the recommended age established by national guidelines for cancer screening. In this paper, we applied 10 different machine learning models to predict EoCRC, and compared their performance with advanced large language models (LLM), using patient conditions, lab results, and observations within 6 months of patient journey prior to the CRC diagnoses. We retrospectively identified 1,953 CRC patients from multiple health systems across the United States. The results demonstrated that the fine-tuned LLM achieved an average of 73% sensitivity and 91% specificity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_11410 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting Early-Onset Colorectal Cancer with Large Language Models Lau, Wilson Kim, Youngwon Parasa, Sravanthi Haque, Md Enamul Oka, Anand Nanduri, Jay Computation and Language The incidence rate of early-onset colorectal cancer (EoCRC, age < 45) has increased every year, but this population is younger than the recommended age established by national guidelines for cancer screening. In this paper, we applied 10 different machine learning models to predict EoCRC, and compared their performance with advanced large language models (LLM), using patient conditions, lab results, and observations within 6 months of patient journey prior to the CRC diagnoses. We retrospectively identified 1,953 CRC patients from multiple health systems across the United States. The results demonstrated that the fine-tuned LLM achieved an average of 73% sensitivity and 91% specificity. |
| title | Predicting Early-Onset Colorectal Cancer with Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.11410 |