CORAL: Expert-Curated medical Oncology Reports to Advance Language Model Inference
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866929276891168768 |
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| author | Sushil, Madhumita Kennedy, Vanessa E. Mandair, Divneet Miao, Brenda Y. Zack, Travis Butte, Atul J. |
| author_facet | Sushil, Madhumita Kennedy, Vanessa E. Mandair, Divneet Miao, Brenda Y. Zack, Travis Butte, Atul J. |
| contents | Both medical care and observational studies in oncology require a thorough understanding of a patient's disease progression and treatment history, often elaborately documented in clinical notes. Despite their vital role, no current oncology information representation and annotation schema fully encapsulates the diversity of information recorded within these notes. Although large language models (LLMs) have recently exhibited impressive performance on various medical natural language processing tasks, due to the current lack of comprehensively annotated oncology datasets, an extensive evaluation of LLMs in extracting and reasoning with the complex rhetoric in oncology notes remains understudied. We developed a detailed schema for annotating textual oncology information, encompassing patient characteristics, tumor characteristics, tests, treatments, and temporality. Using a corpus of 40 de-identified breast and pancreatic cancer progress notes at University of California, San Francisco, we applied this schema to assess the zero-shot abilities of three recent LLMs (GPT-4, GPT-3.5-turbo, and FLAN-UL2) to extract detailed oncological history from two narrative sections of clinical progress notes. Our team annotated 9028 entities, 9986 modifiers, and 5312 relationships. The GPT-4 model exhibited overall best performance, with an average BLEU score of 0.73, an average ROUGE score of 0.72, an exact-match F1-score of 0.51, and an average accuracy of 68% on complex tasks (expert manual evaluation on subset). Notably, it was proficient in tumor characteristic and medication extraction, and demonstrated superior performance in relational inference like adverse event detection. However, further improvements are needed before using it to reliably extract important facts from cancer progress notes needed for clinical research, complex population management, and documenting quality patient care. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_03853 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CORAL: Expert-Curated medical Oncology Reports to Advance Language Model Inference Sushil, Madhumita Kennedy, Vanessa E. Mandair, Divneet Miao, Brenda Y. Zack, Travis Butte, Atul J. Computation and Language Both medical care and observational studies in oncology require a thorough understanding of a patient's disease progression and treatment history, often elaborately documented in clinical notes. Despite their vital role, no current oncology information representation and annotation schema fully encapsulates the diversity of information recorded within these notes. Although large language models (LLMs) have recently exhibited impressive performance on various medical natural language processing tasks, due to the current lack of comprehensively annotated oncology datasets, an extensive evaluation of LLMs in extracting and reasoning with the complex rhetoric in oncology notes remains understudied. We developed a detailed schema for annotating textual oncology information, encompassing patient characteristics, tumor characteristics, tests, treatments, and temporality. Using a corpus of 40 de-identified breast and pancreatic cancer progress notes at University of California, San Francisco, we applied this schema to assess the zero-shot abilities of three recent LLMs (GPT-4, GPT-3.5-turbo, and FLAN-UL2) to extract detailed oncological history from two narrative sections of clinical progress notes. Our team annotated 9028 entities, 9986 modifiers, and 5312 relationships. The GPT-4 model exhibited overall best performance, with an average BLEU score of 0.73, an average ROUGE score of 0.72, an exact-match F1-score of 0.51, and an average accuracy of 68% on complex tasks (expert manual evaluation on subset). Notably, it was proficient in tumor characteristic and medication extraction, and demonstrated superior performance in relational inference like adverse event detection. However, further improvements are needed before using it to reliably extract important facts from cancer progress notes needed for clinical research, complex population management, and documenting quality patient care. |
| title | CORAL: Expert-Curated medical Oncology Reports to Advance Language Model Inference |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2308.03853 |