A ModelOps-based Framework for Intelligent Medical Knowledge Extraction
This work addresses inefficiencies in medical knowledge extraction for researchers and doctors, but it is incremental as it builds on existing ModelOps concepts.
The paper tackles the lack of automation and reusability in medical knowledge extraction models by proposing a ModelOps-based framework with a low-code system, dataset abstraction, and model recommendation, resulting in improved efficiency for researchers and easier access for non-AI experts like doctors.
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.