Medical Text Knowledge Discovery and Clinical Decision Support Based on Natural Language Processing
TL;DR
This paper summarizes the main challenges currently facing, including medical data privacy and labeling problems, interpretability and clinical credibility barriers of the model, and systemic barriers to multimodal fusion.
Abstract
Natural language processing (NLP) technology is the key driving force to unlock the value of massive, unstructured medical text data and promote the development of smart medicine. This paper aims to systematically review the application of NLP in the field of health care, focusing on how “medical text mining” drives “knowledge discovery” and ultimately serves “clinical decision support”. Firstly, this paper reviews the evolution of technology from the early rule method to the current pre training language model. Then, the core technologies such as medical information extraction, knowledge map construction, text classification and generation and their application in typical scenarios such as electronic medical record analysis, auxiliary diagnosis, prognosis prediction, and patient management are reviewed. Through the induction and comparison of existing studies, this paper summarizes the main challenges currently facing, including medical data privacy and labeling problems, interpretability and clinical credibility barriers of the model, and systemic barriers to multimodal fusion. Finally, this paper looks forward to the future research directions, such as the development of interpretable AI and the construction of NLP system for real-world evidence, in order to promote the transformation of this technology from research to safe, reliable and efficient clinical landing.