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.

基于 ModelOps 的智能医学知识提取框架，通过多层回调函数的数据集抽象机制、可重用的模型训练、监控和管理机制，以及基于数据集相似性的模型推荐方法，提供了低代码系统，方便医学研究者开发模型，并简化了非人工智能专家如医生对模型的访问。