asthma-detection-research-based-on-voice-signal-processing-machine

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

To analyze voice signals and identify asthma patients using voice signal analysis and machine learning techniques, we collected clear, low-noise ...

machine-learning-based-cough-tone-classification-diagnostic

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

The objective of this study was to develop and evaluate a non-invasive method for distinguishing patients with chronic obstructive pulmonary disease ...

China Academy Of Chinese Medical Sciences

Chinese Academy of Traditional Chinese Medicine

Because herbal compatibility is one of the most important reasons why Traditional Chinese Medcine (TCM) formulas are effective for acquired immunodeficiency syndrome (AIDS), our study aimed to determine the compatibility of herbs based on published AIDS clinical research in Chinese periodicals. To achieve this aim, we designed a new data-mining algorithm according to TCM data characteristics.

Herbal compatibility of traditional Chinese medical formulas for acquired immunodeficiency syndrome.

Yin and Yang, two concepts adapted from classical Chinese philosophy, play a diagnostic role in Traditional Chinese Medicine (TCM). The Yin and Yang in harmonious balance indicate health, whereas imbalances to either side indicate unhealthiness, which may result in diseases. Yin-yang disharmony is considered to be the cause of pathological changes. Syndrome differentiation of yin-yang is crucial to clinical diagnosis. It lays a foundation for subsequent medical judgments, including therapeutic methods, and formula, among many others. However, because of the complexities of the mechanisms and manifestations of disease, it is difficult to exactly point out which one, yin or yang, is disharmonious. There has been inadequate research conducted on syndrome differentiation of yin and yang from a computational perspective. In this study, we present a computational method, viz. an end-to-end syndrome differentiation of yin deficiency and yang deficiency.

End-to-End syndrome differentiation of Yin deficiency and Yang deficiency in traditional Chinese medicine.

Traditional Chinese Medicine (TCM) clinical intelligent decision-making assistance has been a research hotspot in recent years. However, the recommendations of TCM disease diagnosis based on the current symptoms are difficult to achieve a good accuracy rate because of the ambiguity of the names of TCM diseases. The medical record data downloaded from ancient and modern medical records cloud platform developed by the Institute of Medical Information on TCM of the Chinese Academy of Chinese Medical Sciences (CACMC) and the practice guidelines data in the TCM clinical decision supporting system were utilized as the corpus. Based on the empirical analysis, a variety of improved Naïve Bayes algorithms are presented. The research findings show that the Naïve Bayes algorithm with main symptom weighted and equal probability has achieved better results, with an accuracy rate of 84.2%, which is 15.2% higher than the 69% of the classic Naïve Bayes algorithm (without prior probability). The performance of the Naïve Bayes classifier is greatly improved, and it has certain clinical practicability. The model is currently available at http://tcmcdsmvc.yiankb.com/.

Research on Diagnosis Prediction of Traditional Chinese Medicine Diseases Based on Improved Bayesian Combination Model.

To provide proof of the evidence-based medicine and decision-making information for the clinical decision of functional gastrointestinal disorders(FGIDs), this study evaluated and compared the efficacy, safety, and economy of four oral Chinese patent medicines(CPMs) in the treatment of FGIDs using the method of rapid health technology assessment. The literature was systematically retrieved from CNKI, Wanfang, VIP, SinoMed, EMbase, PubMed, Cochrane Library and ClinicalTrials.gov from the establishment of the databases to May 1, 2022. Two evaluators screened out the literature, extracted data, evaluated the quality of the literature, and descriptively analyzed the results according to the prepared standard. Eventually, 16 studies were included, all of which was rando-mized controlled trial(RCT). The results showed that Renshen Jianpi Tablets, Renshen Jianpi Pills, Shenling Baizhu Granules, and Buzhong Yiqi Granules all had certain effects on the treatment of FGIDs. Renshen Jianpi Tablets treated FGIDs and persistent diarrhea. Shenling Baizhu Granules treated diarrhea with irritable bowel syndrome and FGIDs. Buzhong Yiqi Granules treated diarrhea with irritable bowel syndrome, FGIDs, and chronic diarrhea in children. Renshen Jianpi Pills treated chronic diarrhea. The four oral CPMs all have certain effects on the treatment of FGIDs and have specific advantages for specific patients. Compared with other CPMs, Renshen Jianpi Tablets have higher clinical universality. However, there are problems such as insufficient clinical research evidence, generally low quality of evidence, lack of comparative analysis among medicines, and lack of academic evaluation. More high-quality clinical research and the economic research should be carried out in the future, so as to provide more evidence for the evaluation of the four CPMs.

[Rapid health technology assessment of four oral Chinese patent medicines in treatment of functional gastrointestinal disorders].

Lung cancer (LC) is the second-highest incidence and the first-highest mortality cancer worldwide. Early screening and precise treatment of LC have been the research hotspots in this field. Artificial intelligence (AI) technology has advantages in many aspects of LC and widely used such as LC early diagnosis, LC differential classification, treatment and prognosis prediction.

The global research of artificial intelligence in lung cancer: a 20-year bibliometric analysis.

Respiratory diseases are one of the major health problems worldwide. Early diagnosis of the disease types is of vital importance. As one of the main symptoms of many respiratory diseases, cough may contain information about different pathological changes in the respiratory system. Therefore, many researchers have used cough sounds to diagnose different diseases through artificial intelligence in recent years. The acoustic features and data augmentation methods commonly used in speech tasks are used to achieve better performance. Although these methods are applicable, previous studies have not considered the characteristics of cough sound signals. In this paper, we designed a cough-based respiratory disease classification system and proposed audio characteristic-dependent feature extraction and data augmentation methods. Firstly, according to the short durations and rapid transition of different cough stages, we proposed maximum overlapping mel-spectrogram to avoid missing inter-frame information caused by traditional framing methods. Secondly, we applied various data augmentation methods to mitigate the problem of limited labeled data. Based on the frequency energy distributions of different diseased cough audios, we proposed a parameter-independent self-energy-based augmentation method to enhance the differences between different frequency bands. Finally, in the model testing stage, we leveraged test-time augmentation to further improve the classification performance by fusing the test results of the original and multiple augmented audios. The proposed methods were validated on the Coswara dataset through stratified four-fold cross-validation. Compared to the baseline model using mel-spectrogram as input, the proposed methods achieved an average absolute performance improvement of 3.33% and 3.10% in macro Area Under the Receiver Operating Characteristic (macro AUC) and Unweighted Average Recall (UAR), respectively. The visualization results through Gradient-weighted Class Activation Mapping (Grad-CAM) showed the contributions of different features to model decisions.

Novel audio characteristic-dependent feature extraction and data augmentation methods for cough-based respiratory disease classification.

Traditional Chinese Medicine (TCM) is one of the important non-material cultural heritages of the Chinese nation. It is an important development strategy of Chinese medicine to collect, analyzes, and manages the knowledge assets of TCM health care. As a novel and massive knowledge management technology, knowledge graph provides an ideal technical means to solve the problem of "Knowledge Island" in the field of traditional Chinese medicine. In this study, we construct a large-scale knowledge graph, which integrates terms, documents, databases and other knowledge resources. This knowledge graph can facilitate various knowledge services such as knowledge visualization, knowledge retrieval, and knowledge recommendation, and helps the sharing, interpretation, and utilization of TCM health care knowledge.

ArticleTotal : 2	Year
Publication title	2025
Publication title	2025

Article	Year
Herbal compatibility of traditional Chinese medical formulas for acquired immunodeficiency syndrome. Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan\| PubMed ID: 23297551	2012
End-to-End syndrome differentiation of Yin deficiency and Yang deficiency in traditional Chinese medicine. Computer methods and programs in biomedicine\| PubMed ID: 30376987	2019
Research on Diagnosis Prediction of Traditional Chinese Medicine Diseases Based on Improved Bayesian Combination Model. Evidence-based complementary and alternative medicine : eCAM\| PubMed ID: 34211562	2021
[Rapid health technology assessment of four oral Chinese patent medicines in treatment of functional gastrointestinal disorders]. Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica\| PubMed ID: 36872282	2023
The global research of artificial intelligence in lung cancer: a 20-year bibliometric analysis. Frontiers in oncology\| PubMed ID: 38371616	2024
Novel audio characteristic-dependent feature extraction and data augmentation methods for cough-based respiratory disease classification. Computers in biology and medicine\| PubMed ID: 39029433	2024
Knowledge graph for TCM health preservation: Design, construction, and applications. Artificial intelligence in medicine\| PubMed ID: 28545611	2017

Jinghua Li

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