Extracting structured information from unstructured histopathology reports using generative pre-trained transformer 4 (GPT-4)
Publikation: Beitrag in Fachzeitschrift › Forschungsartikel › Beigetragen › Begutachtung
Beitragende
Abstract
Deep learning applied to whole-slide histopathology images (WSIs) has the potential to enhance precision oncology and alleviate the workload of experts. However, developing these models necessitates large amounts of data with ground truth labels, which can be both time-consuming and expensive to obtain. Pathology reports are typically unstructured or poorly structured texts, and efforts to implement structured reporting templates have been unsuccessful, as these efforts lead to perceived extra workload. In this study, we hypothesised that large language models (LLMs), such as the generative pre-trained transformer 4 (GPT-4), can extract structured data from unstructured plain language reports using a zero-shot approach without requiring any re-training. We tested this hypothesis by utilising GPT-4 to extract information from histopathological reports, focusing on two extensive sets of pathology reports for colorectal cancer and glioblastoma. We found a high concordance between LLM-generated structured data and human-generated structured data. Consequently, LLMs could potentially be employed routinely to extract ground truth data for machine learning from unstructured pathology reports in the future.
Details
Originalsprache | Englisch |
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Seitenumfang | 10 |
Fachzeitschrift | Journal of pathology |
Jahrgang | 262 |
Ausgabenummer | 3 |
Publikationsstatus | Veröffentlicht - 2023 |
Peer-Review-Status | Ja |
Externe IDs
Mendeley | e5fceb30-77fd-3047-a650-21d3ae19355b |
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Schlagworte
Ziele für nachhaltige Entwicklung
ASJC Scopus Sachgebiete
Schlagwörter
- artificial intelligence, large language models, named entity recognition, natural language processing, pathology report, text mining