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Multistage analysis method for detection of effective herb prescription from clinical data

Kuo Yang, Runshun Zhang, Liyun He, Yubing Li, Wenwen Liu, Changhe Yu, Yanhong Zhang, Xinlong Li, Yan Liu, Weiming Xu, Xuezhong Zhou, Baoyan Liu

Frontiers of Medicine 2018, Volume 12, Issue 2,   Pages 206-217 doi: 10.1007/s11684-017-0525-8

Abstract:

Determining effective traditional Chinese medicine (TCM) treatments for specific disease conditions or particular patient groups is a difficult issue that necessitates investigation because of the complicated personalized manifestations in real-world patients and the individualized combination therapies prescribed in clinical settings. In this study, a multistage analysis method that integrates propensity case matching, complex network analysis, and herb set enrichment analysis was proposed to identify effective herb prescriptions for particular diseases (e.g., insomnia). First, propensity case matching was applied to match clinical cases. Then, core network extraction and herb set enrichment were combined to detect core effective herb prescriptions. Effectiveness-based mutual information was used to detect strong herb–symptom relationships. This method was applied on a TCM clinical data set with 955 patients collected from well-designed observational studies. Results revealed that groups of herb prescriptions with higher effectiveness rates (76.9% vs. 42.8% for matched samples; 94.2% vs. 84.9% for all samples) compared with the original prescriptions were found. Particular patient groups with symptom manifestations were also identified to help investigate the indications of the effective herb prescriptions.

Keywords： effective prescription detection     herb set enrichment analysis     core network extraction     insomnia     personalized    

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LC-ELSD analysis for simultaneous determination of five components in the Xue-Fu-Zhu-Yu capsules

Ying GAO, Wenyuan GAO, Xuan DONG, Fengge LI, Xinying YANG,

Frontiers of Chemical Science and Engineering 2010, Volume 4, Issue 1,   Pages 96-101 doi: 10.1007/s11705-009-0309-z

Abstract: developed for simultaneous determination of five constituents in Xue-Fu-Zhu-Yu capsule, a widely used prescription

Keywords： chromatographic separation     prescription     Symmetry     chromatography     evaporative    

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Parallel cognition: hybrid intelligence for human-machine interaction and management Research Article

Peijun YE, Xiao WANG, Wenbo ZHENG, Qinglai WEI, Fei-Yue WANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 12,   Pages 1765-1779 doi: 10.1631/FITEE.2100335

Abstract: As an interdisciplinary research approach, traditional cognitive science adopts mainly the experiment, induction, modeling, and validation paradigm. Such models are sometimes not applicable in cyber-physical-social-systems (CPSSs), where the large number of human users involves severe heterogeneity and dynamics. To reduce the decision-making conflicts between people and machines in human-centered systems, we propose a new research paradigm called parallel cognition that uses the system of intelligent techniques to investigate cognitive activities and functionals in three stages: descriptive cognition based on artificial cognitive systems (ACSs), predictive cognition with computational deliberation experiments, and prescriptive cognition via parallel . To make iteration of these stages constantly on-line, a hybrid learning method based on both a psychological model and user behavioral data is further proposed to adaptively learn an individual's cognitive knowledge. Preliminary experiments on two representative scenarios, urban travel and cognitive visual reasoning, indicate that our parallel cognition learning is effective and feasible for human , and can thus facilitate human-machine cooperation in both complex engineering and social systems.

Keywords： Cognitive learning     Artificial intelligence     Behavioral prescription    

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Remote Sensing and Precision Agriculture Technologies for Crop Disease Detection and Management with a Practical Application Example Review

Chenghai Yang

Engineering 2020, Volume 6, Issue 5,   Pages 528-532 doi: 10.1016/j.eng.2019.10.015

Abstract: fungal disease, in cotton fields and how site-specific fungicide application has been implemented using prescription

Keywords： Crop disease     Airborne imagery     High-resolution satellite imagery     Cotton root rot Prescription map    

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Platform for Small-Molecule Screening Reveals Potential Targets of Bioactive Compounds from a Herbal Prescription Article

Hui Xiong, Ai-Hua Zhang, Ya-Jing Guo, Xiao-Hang Zhou, Hui Sun, Le Yang, Heng Fang, Guang-Li Yan, Xi-Jun Wang

Engineering 2021, Volume 7, Issue 9,   Pages 1293-1305 doi: 10.1016/j.eng.2020.12.016

Abstract:

A herbal prescription in traditional Chinese medicine (TCM) has great complexity, with multiple componentssuccessfully applied for the effective discovery of the active compounds and therapeutic targets of an herbal prescription

Keywords： Efficacy     Bioactive compound     Small molecule     Targets     Herbal medicine     Metabolomics    

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Title Author Date Type Operation

Multistage analysis method for detection of effective herb prescription from clinical data

Kuo Yang, Runshun Zhang, Liyun He, Yubing Li, Wenwen Liu, Changhe Yu, Yanhong Zhang, Xinlong Li, Yan Liu, Weiming Xu, Xuezhong Zhou, Baoyan Liu

Journal Article

LC-ELSD analysis for simultaneous determination of five components in the Xue-Fu-Zhu-Yu capsules

Ying GAO, Wenyuan GAO, Xuan DONG, Fengge LI, Xinying YANG,

Journal Article

Parallel cognition: hybrid intelligence for human-machine interaction and management

Peijun YE, Xiao WANG, Wenbo ZHENG, Qinglai WEI, Fei-Yue WANG

Journal Article

Remote Sensing and Precision Agriculture Technologies for Crop Disease Detection and Management with a Practical Application Example

Chenghai Yang

Journal Article

Platform for Small-Molecule Screening Reveals Potential Targets of Bioactive Compounds from a Herbal Prescription

Hui Xiong, Ai-Hua Zhang, Ya-Jing Guo, Xiao-Hang Zhou, Hui Sun, Le Yang, Heng Fang, Guang-Li Yan, Xi-Jun Wang

Journal Article