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Related Concept Videos

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Telemedicine framework using case-based reasoning with evidences.

A Sene1, B Kamsu-Foguem2, P Rumeau3

  • 1Laboratoire Génie de Production (LGP), EA 1905, ENIT-INPT, Université de Toulouse, 47 avenue d'Azereix, BP 1629, 65016 Tarbes Cedex, France; Laboratoire de Gérontechnologie La Grave, CHU Toulouse/Gérontopôle/UMR 1027 Inserm-Université Toulouse 3, Hôpital La Grave, Place Lange, TSA 60033, 31059 Toulouse Cedex 9, France.

Computer Methods and Programs in Biomedicine
|May 24, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a telemedicine framework using knowledge engineering and ontology modeling to enhance medical decision-making and traceability. The approach integrates evidence-based knowledge into case-based reasoning for improved healthcare delivery.

Keywords:
Case-based reasoningEvidence sourcesMedical taxonomyOncologyTelemedicine

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Knowledge Engineering

Background:

  • Telemedicine facilitates remote healthcare delivery through electronic information exchange.
  • Effective telemedicine requires robust information management and traceability for clinical applications.
  • Integrating evidence-based knowledge is crucial for enhancing medical decision-making processes.

Purpose of the Study:

  • To present a novel telemedicine framework incorporating knowledge engineering and ontology modeling.
  • To improve the accuracy and traceability of medical decision-making in telemedicine.
  • To enhance the integration of evidence-based knowledge within case-based reasoning for healthcare.

Main Methods:

  • Developed a telemedicine framework utilizing knowledge engineering, ontology modeling, and semantic similarity.
  • Enhanced a five-step case-based reasoning process (diagnosis, retrieve treatment, apply evidence, adaptation, retain) with an explicit evidence application step.
  • Employed natural language processing, ontology, and indexing for information processing and case representation.

Main Results:

  • The proposed framework effectively models patient information and treatments using medical ontologies.
  • Demonstrated the framework's utility in oncology, highlighting the role of evidence and expert opinions.
  • The enhanced case-based reasoning process improves traceability and supports evidence-based medical decision-making.

Conclusions:

  • The developed telemedicine framework offers a robust approach for medical decision support and application deployment.
  • Integrating evidence-based knowledge and expert opinions enhances the effectiveness and safety of telemedicine care.
  • This research contributes to advancing telemedicine by improving information management and reasoning processes.