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.

翻译：从医疗文本中抽取医学知识能够增强下游任务，例如医学知识图谱构建和临床决策支持。然而，知识抽取模型的构建和应用缺乏自动化、可复用性和统一管理，导致研究人员效率低下，并使得非人工智能专家（如医生）难以利用知识抽取技术。为解决这些问题，我们提出了一种基于ModelOps的智能医学知识抽取框架，该框架提供低代码系统，支持模型选择、训练、评估和优化。具体而言，该框架包含基于多层回调函数的数据集抽象机制，以及可复用的模型训练、监控和管理机制。我们还提出了一种基于数据集相似性的模型推荐方法，帮助用户快速找到适用于给定数据集的潜在合适模型。该框架既为研究人员开发模型提供了便利，也简化了非人工智能专家（如医生）对模型的使用流程。