Extracting medical knowledge from healthcare texts enhances downstream tasks like medical knowledge graph construction and clinical decision-making. However, the construction and application of knowledge extraction models lack automation, reusability and unified management, leading to inefficiencies for researchers and high barriers for non-AI experts such as doctors, to utilize knowledge extraction. To address these issues, we propose a ModelOps-based intelligent medical knowledge extraction framework that offers a low-code system for model selection, training, evaluation and optimization. Specifically, the framework includes a dataset abstraction mechanism based on multi-layer callback functions, a reusable model training, monitoring and management mechanism. We also propose a model recommendation method based on dataset similarity, which helps users quickly find potentially suitable models for a given dataset. Our framework provides convenience for researchers to develop models and simplifies model access for non-AI experts such as doctors.
@article{arxiv.2310.02593,
title = {A ModelOps-based Framework for Intelligent Medical Knowledge Extraction},
author = {Hongxin Ding and Peinie Zou and Zhiyuan Wang and Junfeng Zhao and Yasha Wang and Qiang Zhou},
journal= {arXiv preprint arXiv:2310.02593},
year = {2023}
}