11,088 research outputs found
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
Conceptual graph-based knowledge representation for supporting reasoning in African traditional medicine
Although African patients use both conventional or modern and traditional healthcare simultaneously, it has been proven that 80% of people rely on African traditional medicine (ATM). ATM includes medical activities stemming from practices, customs and traditions which were integral to the distinctive African cultures. It is based mainly on the oral transfer of knowledge, with the risk of losing critical knowledge. Moreover, practices differ according to the regions and the availability of medicinal plants. Therefore, it is necessary to compile tacit, disseminated and complex knowledge from various Tradi-Practitioners (TP) in order to determine interesting patterns for treating a given disease. Knowledge engineering methods for traditional medicine are useful to model suitably complex information needs, formalize knowledge of domain experts and highlight the effective practices for their integration to conventional medicine. The work described in this paper presents an approach which addresses two issues. First it aims at proposing a formal representation model of ATM knowledge and practices to facilitate their sharing and reusing. Then, it aims at providing a visual reasoning mechanism for selecting best available procedures and medicinal plants to treat diseases. The approach is based on the use of the Delphi method for capturing knowledge from various experts which necessitate reaching a consensus. Conceptual graph formalism is used to model ATM knowledge with visual reasoning capabilities and processes. The nested conceptual graphs are used to visually express the semantic meaning of Computational Tree Logic (CTL) constructs that are useful for formal specification of temporal properties of ATM domain knowledge. Our approach presents the advantage of mitigating knowledge loss with conceptual development assistance to improve the quality of ATM care (medical diagnosis and therapeutics), but also patient safety (drug monitoring)
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
Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains
Biomedical taxonomies, thesauri and ontologies in the form of the
International Classification of Diseases (ICD) as a taxonomy or the National
Cancer Institute Thesaurus as an OWL-based ontology, play a critical role in
acquiring, representing and processing information about human health. With
increasing adoption and relevance, biomedical ontologies have also
significantly increased in size. For example, the 11th revision of the ICD,
which is currently under active development by the WHO contains nearly 50,000
classes representing a vast variety of different diseases and causes of death.
This evolution in terms of size was accompanied by an evolution in the way
ontologies are engineered. Because no single individual has the expertise to
develop such large-scale ontologies, ontology-engineering projects have evolved
from small-scale efforts involving just a few domain experts to large-scale
projects that require effective collaboration between dozens or even hundreds
of experts, practitioners and other stakeholders. Understanding how these
stakeholders collaborate will enable us to improve editing environments that
support such collaborations. We uncover how large ontology-engineering
projects, such as the ICD in its 11th revision, unfold by analyzing usage logs
of five different biomedical ontology-engineering projects of varying sizes and
scopes using Markov chains. We discover intriguing interaction patterns (e.g.,
which properties users subsequently change) that suggest that large
collaborative ontology-engineering projects are governed by a few general
principles that determine and drive development. From our analysis, we identify
commonalities and differences between different projects that have implications
for project managers, ontology editors, developers and contributors working on
collaborative ontology-engineering projects and tools in the biomedical domain.Comment: Published in the Journal of Biomedical Informatic
Knowledge-based Biomedical Data Science 2019
Knowledge-based biomedical data science (KBDS) involves the design and
implementation of computer systems that act as if they knew about biomedicine.
Such systems depend on formally represented knowledge in computer systems,
often in the form of knowledge graphs. Here we survey the progress in the last
year in systems that use formally represented knowledge to address data science
problems in both clinical and biological domains, as well as on approaches for
creating knowledge graphs. Major themes include the relationships between
knowledge graphs and machine learning, the use of natural language processing,
and the expansion of knowledge-based approaches to novel domains, such as
Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages
with 3 table
Clinical Decision Support System for Unani Medicine Practitioners
Like other fields of Traditional Medicines, Unani Medicines have been found
as an effective medical practice for ages. It is still widely used in the
subcontinent, particularly in Pakistan and India. However, Unani Medicines
Practitioners are lacking modern IT applications in their everyday clinical
practices. An Online Clinical Decision Support System may address this
challenge to assist apprentice Unani Medicines practitioners in their
diagnostic processes. The proposed system provides a web-based interface to
enter the patient's symptoms, which are then automatically analyzed by our
system to generate a list of probable diseases. The system allows practitioners
to choose the most likely disease and inform patients about the associated
treatment options remotely. The system consists of three modules: an Online
Clinical Decision Support System, an Artificial Intelligence Inference Engine,
and a comprehensive Unani Medicines Database. The system employs advanced AI
techniques such as Decision Trees, Deep Learning, and Natural Language
Processing. For system development, the project team used a technology stack
that includes React, FastAPI, and MySQL. Data and functionality of the
application is exposed using APIs for integration and extension with similar
domain applications. The novelty of the project is that it addresses the
challenge of diagnosing diseases accurately and efficiently in the context of
Unani Medicines principles. By leveraging the power of technology, the proposed
Clinical Decision Support System has the potential to ease access to healthcare
services and information, reduce cost, boost practitioner and patient
satisfaction, improve speed and accuracy of the diagnostic process, and provide
effective treatments remotely. The application will be useful for Unani
Medicines Practitioners, Patients, Government Drug Regulators, Software
Developers, and Medical Researchers.Comment: 59 pages, 11 figures, Computer Science Bachelor's Thesis on use of
Artificial Intelligence in Clinical Decision Support System for Unani
Medicine
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