A ModelOps-Based Framework for Intelligent Medical Knowledge Extraction

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 usage barriers for non-AI experts like doctors. To address these issues, we propose a ModelOps-based intelligent medical knowledge extraction framework that offers a low-code system for model construction and application. Specifically, the framework includes a dataset abstraction mechanism based on multi-layer callback functions, a reusable model training, monitoring and management mechanism, alongside a model recommendation method based on dataset similarity for quickly finding potentially suitable models for a given dataset. Our frame-work provides convenience for researchers to develop models and simplifies model access for non-AI experts like doctors.