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Record W2175176996 · doi:10.1155/2015/460490

Bioinformatics/Medical Informatics in Traditional Medicine and Integrative Medicine

2015· editorial· en· W2175176996 on OpenAlex
Zhaohui Liang, Byeongsang Oh, Josiah Poon

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Scientific World JOURNAL · 2015
Typeeditorial
Languageen
FieldMedicine
TopicTraditional Chinese Medicine Studies
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsHealth informaticsInformaticsComputer scienceIntegrative medicineData scienceTranslational research informaticsPrecision medicineAlternative medicineMedicineBioinformaticsHealth Administration InformaticsBiologyPathologyPublic healthPolitical science

Abstract

fetched live from OpenAlex

Traditional Chinese Medicine (TCM) and integrative medicine are key components of the cultural heritage from Eastern Asia with thousands-of-years history in research and healthcare delivery. Traditional oriental medicine contributes significantly to the prosperity of Chinese and Eastern Asian culture. After the introduction of western biomedicine to Asia, traditional medicine still plays an important role in the healthcare system of many Asian countries and integrated with the mainstream medical treatments as a new track of healthcare named as integrative medicine. With the current trend of globalization, traditional medicine and integrative medicine are receiving gradual acceptance in the Western world. As a result, studies on traditional medicine attract more and more attention from researchers with various knowledge backgrounds and technologies. Medical informatics is a new interdisciplinary branch in medical science when computer science and information technology are combined with research of health science. The application of medical informatics that has extended to the studies of traditional medicine and other therapies of complementary and alternative medicine (CAM). The special issue supported by this journal provides a forum for traditional and integrative medical researchers and practitioners to share and exchange their new ideas on using computer science and information technology to explore and solve problems in healthcare. It is proposed with the Fifth International Workshop on Information Technology for Chinese Medicine (ITCM 2014) in Guangzhou, China, on 12 to 14 December 2014. The workshop is in conjunction with the 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM'14), which was held in Belfast, UK, on 2 to 5 November 2014. Professor Xusheng Liu, Professor Honglai Zhang, and Professor Guozheng Li cochaired the workshop. The conference invited top experts from the US, UK, Australia, and Hong Kong to present their inspiring research outcomes and prospect the future of traditional and integrative medicine. However, numerous scientists and researchers were unable to introduce their excellent idea due to time limit of the workshop. The ITCM 2014 received about 100 submissions. All papers were anonymously reviewed by members of the IEEE conference organization committee. The accepted papers were published in the Proceedings of the 2014 IEEE International Conference on Bioinformatics and Biomedicine Workshops (IEEE-BIBMW) (ISBN 978-1-4799-1309-1). Just a few excellent papers were later invited to submit the extension version to the special issue alongside external submissions for consideration of publishing. This special issue has received 37 submissions. All papers have gone through rigorous view, and only 10 of them (27%) are finally accepted for publication. This special issue reflects the up-to-date progress in applications of information technology to traditional and integrative medicine. The papers are categorized to represent the four aspects of medical informatics research of the discipline. In the paper entitled “Standardization of Syndrome Differentiation Defined by Traditional Chinese Medicine in Operative Breast Cancer: A Modified Delphi Study,” Q. Guo and Q. Chen present their research on TCM syndromes. Five papers are selected to demonstrate the research progress in disease diagnosis and treatment. G.-X. Shi et al. report a clinical study on vascular dementia. Z. Chen presents a new mathematics method to explore the classical theory of five elements in TCM in his work “Researches on Mathematical Relationship of Five Elements of Containing Notes and Fibonacci Sequence Modulo 5.” In “Syndrome Differentiation Analysis on MARS500 Data of Traditional Chinese Medicine,” Y.-Z. Li et al. succeed to use MARS500 to process the data of traditional medicine. The paper entitled “Detecting Disease in Radiographs with Intuitive Confidence” by S. Jaeger introduces the new idea to use informatics method to detect disease. Three papers are about information processing of traditional medicine. The paper entitled “Patterns Exploration on Patterns of Empirical Herbal Formula of Chinese Medicine by Association Rules” by L. Huang et al. used association rules to retrieve patterns from classical traditional medical formula. B. Zhang et al. proposed a bioinformatics approach to explore the latent patterns from conventional formula Shuang-Huang-Lian in their work “Using Bioinformatics Approach to Explore the Pharmacological Mechanisms of Multiple Ingredients in Shuang-Huang-Lian.” The paper entitled “Pulse-Diagnosis Signals Analysis of Fatty Liver Disease and Cirrhosis Patients by Using Machine Learning” by N. Wang et al. introduces new data mining method to process diagnostic data of liver disease. Finally, the paper entitled “An Ensemble Learning Based Framework for Traditional Chinese Medicine Data Analysis with ICD-10 Labels” by G. Zhang et al. and the paper entitled “ISMAC: An Intelligent System for Customized Clinical Case Management and Analysis” introduce the applications of machine learning to electronic data analysis of traditional medicine.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.085
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0010.006
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.006
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.055
GPT teacher head0.328
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it