药方
计算机科学
中医药
嵌入
临床实习
人工智能
替代医学
医疗保健
数据科学
传统医学
机器学习
数据挖掘
医学
家庭医学
护理部
病理
经济
经济增长
作者
Chunyang Ruan,Ye Wang,Yanchun Zhang,Yun Ye
标识
DOI:10.1007/978-3-030-18590-9_35
摘要
Regularities of prescriptions are important for both clinical practice and novel healthcare development in clinical traditional Chinese medicine (TCM). To address this issue and meet clinical demand for determining treatments, we propose an unsupervised analysis model termed AMNE to determine effective herbs for diverse symptoms in prescriptions. Results confirmed by human physicians demonstrate AMNE can outperform several previous TCM regularity discovery models in prescriptions.
科研通智能强力驱动
Strongly Powered by AbleSci AI