An Ontology-Based Artificial Intelligence Model for Medicine Side-Effect Prediction: Taking Traditional Chinese Medicine as An Example

In this work, an ontology-based model for AI-assisted medicine side-effect (SE) prediction is developed, where three main components, including the drug model, the treatment model, and the AI-assisted prediction model, of proposed model are presented. To validate the proposed model, an ANN structure is established and trained by two hundred and forty-two TCM prescriptions. These data are gathered and classified from the most famous ancient TCM book and more than one thousand SE reports, in which two ontology-based attributions, hot and cold, are introduced to evaluate whether the prescription will cause SE or not. The results preliminarily reveal that it is a relationship between the ontology-based attributions and the corresponding predicted indicator that can be learnt by AI for predicting the SE, which suggests the proposed model has a potential in AI-assisted SE prediction. However, it should be noted that, the proposed model highly depends on the sufficient clinic data, and hereby, much deeper exploration is important for enhancing the accuracy of the prediction

Integrating findings of traditional medicine with modern pharmaceutical research: the potential role of linked open data

One of the biggest obstacles to progress in modern pharmaceutical research is the difficulty of integrating all available research findings into effective therapies for humans. Studies of traditionally used pharmacologically active plants and other substances in traditional medicines may be valuable sources of previously unknown compounds with therapeutic actions. However, the integration of findings from traditional medicines can be fraught with difficulties and misunderstandings. This article proposes an approach to use linked open data and Semantic Web technologies to address the heterogeneous data integration problem. The approach is based on our initial experiences with implementing an integrated web of data for a selected use-case, i.e., the identification of plant species used in Chinese medicine that indicate potential antidepressant activities

Semantic Web for data harmonization in Chinese medicine

Scientific studies to investigate Chinese medicine with Western medicine have been generating a large amount of data to be shared preferably under a global data standard. This article provides an overview of Semantic Web and identifies some representative Semantic Web applications in Chinese medicine. Semantic Web is proposed as a standard for representing Chinese medicine data and facilitating their integration with Western medicine data

シカク ショウガイシャ ヘ ノ シンキュウ キョウイク ニ オケル オントロジー ベース キョウザイ ノ ユウコウセイ

我々は視覚障害者向けの鍼灸、特に経絡と経穴に関する教育材料の研究に取り組んだ。この教材では、鍼灸分野における概念間の関係の体系的な理解を可能にすべく、オントロジーを用いた。オントロジーの概念図によりオントロジー教材を作成し、これを鍼灸の講義で使用した群とオントロジー教材未使用群とで経絡・経穴の試験の結果を比較し、その有効性を解析した。その結果、オントロジー教材を使用した学生の経絡・経穴の試験の得点は、オントロジー教材未使用群の学生の得点よりも有意(P < 0.001)に高く、経絡と経穴の学習にオントロジー教材有効である事が明らかになった。この結果は、オントロジーの視覚障害者教育への利用が視覚障害者の鍼灸理論の学習へも有効であることを示唆している。In this study we developed learning material of acupuncture - acupoints and meridians - for visually impaired people. For this learning material, we used ontology to construct the relations between the concepts in the acupuncture and moxibustion field which also enables tactical understanding similarly as visual understanding. We then used our learning material in the acupuncture lecture for the visually impaired students, and quantitatively analysed its efficacy to compare the results with the group which did not use the learning material. As a result, the students, who used our learning material, scored significantly higher in the examination than the students of non-use group (P < 0.001), suggesting that our learning material was effective for learning of acupuncture theory

Expression model for multiple relationships in the ontology of traditional Chinese medicine knowledge

AbstractObjectiveTo explore multiple relationships in traditional Chinese medicine (TCM) knowledge by comparing binary and multiple relationships during knowledge organization.MethodsCharacteristics of binary and multiple semantic relationships as well as their associations are described. A method to classify multiple relationships based on the involvement of time is proposed and theoretically validated using examples from the ancient TCM classic Important Formulas Worth a Thousand Gold Pieces. The classification includes parallel multiple relationships, restricted multiple relationships, multiple relationships that involve time, and multiple relationships that involve time restriction. Next, construction of multiple semantic relationships for TCM concepts in each classification using Protégé, an ontology editing tool is described.ResultsProtégé is superior to a binary relationship and less than ideal with multiple relationships during the constitution of concept relationships.ConclusionWhen applied in TCM, the semantic relationships constructed by Protégé are superior than those constructed by correlation and/or attribute relationships, but less ideal than those constructed by the human cognitive process

Exploring the Potential of Large Language models in Traditional Korean Medicine: A Foundation Model Approach to Culturally-Adapted Healthcare

Introduction: Traditional Korean medicine (TKM) emphasizes individualized diagnosis and treatment, making AI modeling difficult due to limited data and implicit processes. GPT-3.5 and GPT-4, large language models, have shown impressive medical knowledge despite lacking medicine-specific training. This study aimed to assess the capabilities of GPT-3.5 and GPT-4 for TKM using the Korean National Licensing Examination for Korean Medicine Doctors. Methods: GPT-3.5 (February 2023) and GPT-4 (March 2023) models answered 340 questions from the 2022 examination across 12 subjects. Each question was independently evaluated five times in an initialized session. Results: GPT-3.5 and GPT-4 achieved 42.06% and 57.29% accuracy, respectively, with GPT-4 nearing passing performance. There were significant differences in accuracy by subjects, with 83.75% accuracy for neuropsychiatry compared to 28.75% for internal medicine (2). Both models showed high accuracy in recall-based and diagnosis-based questions but struggled with intervention-based ones. The accuracy for questions that require TKM-specialized knowledge was relatively lower than the accuracy for questions that do not GPT-4 showed high accuracy for table-based questions, and both models demonstrated consistent responses. A positive correlation between consistency and accuracy was observed. Conclusion: Models in this study showed near-passing performance in decision-making for TKM without domain-specific training. However, limits were also observed that were believed to be caused by culturally-biased learning. Our study suggests that foundation models have potential in culturally-adapted medicine, specifically TKM, for clinical assistance, medical education, and medical research.Comment: 31 pages, 6 figure

An E-Learning Semantic Grid for Life science Education

There are a lot of life science databases and services on the Internet nowadays, especially in life science e-science. In this paper, we will present an E-Learning Semantic Grid that integrates these resources provided by both teachers and scientists for life science education. It uses domain ontologies to integrate these heterogeneous life science database and service resources, and supports ontology-based e-learning data-sharing and service-coordination for life science teachers and students in an e-learning virtual organization. Our system provides life science students with semantically superior experience in learning activities, and also extends the function of life science e-science. It has a promising future in the domain of life science education

طرح نقشه نمایی مفاهیم طبّ سنّتی ایران در ساختار ابراصطلاحنامه و شبکه معنایی«(UMLS) نظام زبان واحد پزشکی »

مقدمه: هدف این پژوهش ارائ هی طرحی برای تعیین جایگاه واژگان و مفاهیم طبّ سنّتی ایران در ساختار زبان مشترک و تبیین (Unified Medical Language System = UMLS) ابراصطلاحنامه و شبکه معنایی نظام زبان واحد پزشکی جایگاه و سهم واژگان طبّ سنّتی ایرانی در واژگان و مفاهیم دانش جهانی پزشکی بود. روش بررسی: این طرح پژوهش سه بخش است: در بخش الف، نظام زبان واحد پزشکی جهت شناسایی ساختار کلی آن و شناسایی دقیق خلأ های آن در مورد مفاهیم طبّ سنّتی ایران، تحلیل م یشود. در بخش ب، متون و منابع اطلاعاتی مربوط به داروی مفرده » و « نشان هی رنگ ادرار » ،« بیماری صرع » : مفاهیم طبّ سنّتی ایران مطالعه؛ و نمون ههایی از مفاهیم کلی شامل با زیر شاخ ههای آ نها استخراج م یشود. در بخش ج، نمونه مفاهیم استخراج شده از میان مفاهیم پزشکی رایج « سنبل الطیب (غربی) ترسیم شده در ساختار نظام زبان واحد پزشکی- در خ لأهای مربوط به مفاهیم طبّ سنّتی ایران، درون ساختار ابراصطلاحنامه و شبکه معنایی این نظام، گنجانده م یشود. یافته ها: پیش نمونی از ساختار کلان طبّ سنّتی ایرانی که جایگاه آن در زبان پزشکی واحد ترسیم شده است با چگونگی تبادلات مفهومی طبّ سنّتی ایرانی با نظام زبان پزشکی واحد ارائه شد. نتیجه گیری: دامن هی فعلی یو ام ال اس تعداد قابل قبولی از مفاهیم مرتبط با طبّ سنّتی ایران را پوشش داده است. ولی جایگاه و دامن هی کامل و رسمی از دانش طبّ سنّتی ایران ارائه نم یکند. این پژوهش، موفق به تحلیل رد ههای کلان طبّ سنّتی ایرانی شده، و شیو هی برقراری تبادلات مفهومی را بین طبّ سنّتی ایرانی با طب امروزه جهانی، تحلیل کرده است