Evidence-Based Complementary and Alternative Medicine

Real-World Big Data Processing and Analysis for Traditional Chinese Medicine 2022


Publishing date
01 Mar 2023
Status
Closed
Submission deadline
28 Oct 2022

Lead Editor

1Beijing Jiaotong University, Beijing, China

2University of Sydney, Sydney, Australia

3Manchester Metropolitan University, Manchester, UK

4Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China

This issue is now closed for submissions.

Real-World Big Data Processing and Analysis for Traditional Chinese Medicine 2022

This issue is now closed for submissions.

Description

The concept of real-world studies (RWS) has emerged in recent years, differing from traditional randomized controlled trials (RCT) in both design and implementation. Real-world data (RWD) and real-world evidence (RWE), the basement and outcome of RWS, have become increasingly important in the fields of medical and health decision-making. Traditional Chinese medicine (TCM) is a particular kind of medical science reliant on real-world clinical practices and evidence. RWE plays an important role in promoting the individualized application of TCM diagnoses and treatment regularities for personalized medicine with optimal effectiveness.

The progress of big data analysis and artificial intelligence (AI) techniques provides opportunities for the efficient collection, preprocessing, and analysis of real-world TCM clinical data, including full-text electronic medical records, herbal prescriptions, biomedical features and medical images, for clinical decision making and clinical knowledge discovery. Therefore, big data and AI techniques, when integrated with RWD, are vital in promoting the capability of disease prediction and proving the clinical effectiveness and safety of TCM for chronic diseases and infectious diseases. Specifically, they are suitable for distilling empirical diagnosis and treatment regularities (e.g. syndrome differentiation, symptom clusters, herb-symptom relationships and effective herbal prescriptions) from the large-scale clinical data of TCM treatment for chronic diseases. In addition, while integrated with network medicine, RWS would be useful to help investigating the underlying pharmacological mechanisms of real-world herbal prescriptions or herbs, which would provide a promising approach for novel drug discovery in TCM fields.

This Special Issue will focus on topics relating to TCM and covering real-world big data and AI techniques and applications, such as RWD collection, text mining of electronic medical records, medical terminologies and knowledge graphs, data mining and AI algorithms and applications, and translational biomedical informatics for TCM diagnosis and treatment. We welcome both original studies, including methodology research and real-world based clinical research, and review articles.

Potential topics include but are not limited to the following:

  • Real-world curation and preprocessing of TCM clinical data
  • Quality control and processing of real-world TCM clinical data
  • Clinical terminologies and information standards for real-world TCM data
  • Knowledge graph and ontology development in TCM fields
  • Clinical data warehousing and clinical decision support
  • Machine learning for TCM diagnosis and treatment planning
  • TCM data mining techniques and applications
  • Text mining and natural language processing (NLP) for electronic medical records
  • Clinical effectiveness evaluation studies based on real-world TCM data
  • Translational biomedical informatics for TCM diagnosis and treatment
  • Network pharmacology derived from real-world effective herbal prescriptions
Evidence-Based Complementary and Alternative Medicine
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CiteScore3.500
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