Ying Liu, Yongjun Ma
Clinical applications of metagenomics next-generation sequencing in infectious diseases
Journal of Zhejiang University. Science. B
existing natural language processing nlp methods convert freetext clinical notes structured data often require problemspecific annotations model training study aims evaluate chatgpts capacity extract information freetext medical notes efficiently comprehensively developed large language model llmbased workflow utilizing systems engineering methodology spiral prompt engineering process leveraging openais api batch querying chatgpt evaluated effectiveness method using dataset 1000 lung cancer pathology reports dataset 191 pediatric osteosarcoma pathology reports comparing chatgpt35 gpt35turbo16k outputs expertcurated structured data chatgpt35 demonstrated ability extract pathological classifications overall accuracy 89 lung cancer dataset outperforming performance two traditional nlp methods performance influenced design instructive prompt case analysis shows misclassifications due lack highly specialized pathology terminology erroneous interpretation tnm staging rules reproducibility shows relatively stable performance chatgpt35 time pediatric osteosarcoma dataset chatgpt35 accurately classified grades margin status accuracy 986 100 respectively study shows feasibility using chatgpt process large volumes clinical notes structured information extraction without requiring extensive taskspecific human annotation model training results underscore potential role llms transforming unstructured healthcare data structured formats thereby supporting research aiding clinical decisionmaking