Exploration to the Application of Hybird Technology in Special Types of Congenital Heart Diseases in Children

Liu, Yuhang; Wang, Ning; Wen, Ping

doi:10.21203/rs.3.rs-38985/v1

Cited by 1 publication

(2 citation statements)

References 18 publications

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“…Natural product development Deep neurol network [47,50], RF [53,54,58,59,93], SVM [51,53,54,57,59,93], DT [59,93], neural network [53,59] RF was better than SVM, neurol network and DT in screening hepatotoxic compounds [59]. RF model is more accurate than SVM and DT in identifying molecular characteristics of natural product compounds with the meridians of TCM [93] Disease diagnosis SVM [10,61,66,[81][82][83], DT [68,[81][82][83], neural network [45, 61-63, 65, 82, 83], RF [61,64,67,82,83], CNN [64,67,[70][71][72][73][74][75][76][77][78]81], RNN…”

Section: Performance Of the Algorithmmentioning

confidence: 99%

“…Fernando [80] proposed a heart sound segmentation method based on the combination of RNN with attention mechanism, which can effectively learn features from irregular and noisy heart sounds. Liu [81] used gradientenhanced DT, SVM, CNN, and residual convolutional recurrent networks to analyze heart sound signals. The results showed that the residual convolutional recurrent network model has the highest recognition accuracy and sensitivity for the four types of coronary heart disease heart sounds.…”

Section: Applications Of Machine Learning In Disease Diagnosismentioning

confidence: 99%

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Machine learning in TCM with natural products and molecules: current status and future perspectives

Liu

et al. 2023

Chin Med

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Traditional Chinese medicine (TCM) has been practiced for thousands of years with clinical efficacy. Natural products and their effective agents such as artemisinin and paclitaxel have saved millions of lives worldwide. Artificial intelligence is being increasingly deployed in TCM. By summarizing the principles and processes of deep learning and traditional machine learning algorithms, analyzing the application of machine learning in TCM, reviewing the results of previous studies, this study proposed a promising future perspective based on the combination of machine learning, TCM theory, chemical compositions of natural products, and computational simulations based on molecules and chemical compositions. In the first place, machine learning will be utilized in the effective chemical components of natural products to target the pathological molecules of the disease which could achieve the purpose of screening the natural products on the basis of the pathological mechanisms they target. In this approach, computational simulations will be used for processing the data for effective chemical components, generating datasets for analyzing features. In the next step, machine learning will be used to analyze the datasets on the basis of TCM theories such as the superposition of syndrome elements. Finally, interdisciplinary natural product-syndrome research will be established by unifying the results of the two steps outlined above, potentially realizing an intelligent artificial intelligence diagnosis and treatment model based on the effective chemical components of natural products under the guidance of TCM theory. This perspective outlines an innovative application of machine learning in the clinical practice of TCM based on the investigation of chemical molecules under the guidance of TCM theory.

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