This report analyzes the existing problems in terminology referring to clinical symptoms of traditional Chinese medicine(TCM)from the viewpoint of data sharing and elaborates the necessity of establishing a standard d...This report analyzes the existing problems in terminology referring to clinical symptoms of traditional Chinese medicine(TCM)from the viewpoint of data sharing and elaborates the necessity of establishing a standard directory of clinical data elements of TCM.We evaluated the principles and methods of data element extraction according to the status quo of the clinical information system and characteristics of symptoms for TCM and consequently proposed a three-layer model for optimal extraction.展开更多
Objective Natural language processing (NLP) was used to excavate and visualize the core content of syndrome element syndrome differentiation (SESD). Methods The first step was to build a text mining and analysis envir...Objective Natural language processing (NLP) was used to excavate and visualize the core content of syndrome element syndrome differentiation (SESD). Methods The first step was to build a text mining and analysis environment based on Python language, and built a corpus based on the core chapters of SESD. The second step was to digitalize the corpus. The main steps included word segmentation, information cleaning and merging, document-entry matrix, dictionary compilation and information conversion. The third step was to mine and display the internal information of SESD corpus by means of word cloud, keyword extraction and visualization. Results NLP played a positive role in computer recognition and comprehension of SESD. Different chapters had different keywords and weights. Deficiency syndrome elements were an important component of SESD, such as "Qi deficiency""Yang deficiency" and "Yin deficiency". The important syndrome elements of substantiality included "Blood stasis""Qi stagnation", etc. Core syndrome elements were closely related. Conclusions Syndrome differentiation and treatment was the core of SESD. Using NLP to excavate syndromes differentiation could help reveal the internal relationship between syndromes differentiation and provide basis for artificial intelligence to learn syndromes differentiation.展开更多
Large-Scale Language Models(LLMs)have initiated transformative changes in Traditional Chinese Medicine(TCM),yet existing LLM-based diagnostic approaches face challenges such as prolonged training cycles and high imple...Large-Scale Language Models(LLMs)have initiated transformative changes in Traditional Chinese Medicine(TCM),yet existing LLM-based diagnostic approaches face challenges such as prolonged training cycles and high implementation costs due to reliance on medical expertise.To address this,we propose a systematic framework integrating multimodal data and LLM technologies.First,we analyze bottlenecks in traditional diagnosis(e.g.,subjectivity)and modernization challenges.The framework leverages open-s ource foundation models(e.g.,Baichuan2,LLaMA)pre-trained on"symptom-syndrome-medication"associations,fine-tuned with clinical data to simulate diagnostic workflows.Key components include:(1)a Data Input Layer capturing tongue image features(via YOLOv5s6/U-Net),speech spectra,BERT-encoded inquiry texts,and pulse waveforms;(2)a Feature Fusion Layer constructing syndrome differentiation vectors through multimodal feature concatenation;and(3)a Prediction&Feedback Layer generating diagnostic probabilities with reinforcement learning based on clinical efficacy.Finally,we discuss critical issues,including risks of physician replacement,professional competence degradation,and liability attr ibution in diagnostic errors.This framework aims t o enhance TCM diagnostic efficiency while ensuring clinical reliability.展开更多
基金funding support from the Innovation Platform Open Fund Project of Hunan Provincial Universities (No. 13K076)National Key Discipline Open Fund Project of TCM diagnostics in Hunan University of Chinese Medicine (2015zyzd18)
文摘This report analyzes the existing problems in terminology referring to clinical symptoms of traditional Chinese medicine(TCM)from the viewpoint of data sharing and elaborates the necessity of establishing a standard directory of clinical data elements of TCM.We evaluated the principles and methods of data element extraction according to the status quo of the clinical information system and characteristics of symptoms for TCM and consequently proposed a three-layer model for optimal extraction.
基金the funding support from the National Natural Science Foundation of China (No. 81874429)Digital and Applied Research Platform for Diagnosis of Traditional Chinese Medicine (No. 49021003005)+1 种基金2018 Hunan Provincial Postgraduate Research Innovation Project (No. CX2018B465)Excellent Youth Project of Hunan Education Department in 2018 (No. 18B241)
文摘Objective Natural language processing (NLP) was used to excavate and visualize the core content of syndrome element syndrome differentiation (SESD). Methods The first step was to build a text mining and analysis environment based on Python language, and built a corpus based on the core chapters of SESD. The second step was to digitalize the corpus. The main steps included word segmentation, information cleaning and merging, document-entry matrix, dictionary compilation and information conversion. The third step was to mine and display the internal information of SESD corpus by means of word cloud, keyword extraction and visualization. Results NLP played a positive role in computer recognition and comprehension of SESD. Different chapters had different keywords and weights. Deficiency syndrome elements were an important component of SESD, such as "Qi deficiency""Yang deficiency" and "Yin deficiency". The important syndrome elements of substantiality included "Blood stasis""Qi stagnation", etc. Core syndrome elements were closely related. Conclusions Syndrome differentiation and treatment was the core of SESD. Using NLP to excavate syndromes differentiation could help reveal the internal relationship between syndromes differentiation and provide basis for artificial intelligence to learn syndromes differentiation.
文摘Large-Scale Language Models(LLMs)have initiated transformative changes in Traditional Chinese Medicine(TCM),yet existing LLM-based diagnostic approaches face challenges such as prolonged training cycles and high implementation costs due to reliance on medical expertise.To address this,we propose a systematic framework integrating multimodal data and LLM technologies.First,we analyze bottlenecks in traditional diagnosis(e.g.,subjectivity)and modernization challenges.The framework leverages open-s ource foundation models(e.g.,Baichuan2,LLaMA)pre-trained on"symptom-syndrome-medication"associations,fine-tuned with clinical data to simulate diagnostic workflows.Key components include:(1)a Data Input Layer capturing tongue image features(via YOLOv5s6/U-Net),speech spectra,BERT-encoded inquiry texts,and pulse waveforms;(2)a Feature Fusion Layer constructing syndrome differentiation vectors through multimodal feature concatenation;and(3)a Prediction&Feedback Layer generating diagnostic probabilities with reinforcement learning based on clinical efficacy.Finally,we discuss critical issues,including risks of physician replacement,professional competence degradation,and liability attr ibution in diagnostic errors.This framework aims t o enhance TCM diagnostic efficiency while ensuring clinical reliability.
