This research aims to depict the methodological steps and tools about the combined operation of case-based reasoning (CBR) and multi-agent system (MAS) to expose the ontological application in the field of clinical decision support. The multi-agent architecture works for the consideration of the whole cycle of clinical decision-making adaptable to many medical aspects such as the diagnosis, prognosis, treatment, therapeutic monitoring of gastric cancer. In the multi-agent architecture, the ontological agent type employs the domain knowledge to ease the extraction of similar clinical cases and provide treatment suggestions to patients and physicians. Ontological agent is used for the extension of domain hierarchy and the interpretation of input requests. Case-based reasoning memorizes and restores experience data for solving similar problems, with the help of matching approach and defined interfaces of ontologies. A typical case is developed to illustrate the implementation of the knowledge acquisition and restitution of medical experts.
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http://dx.doi.org/10.1016/j.jbi.2015.06.012 | DOI Listing |
BMC Med Educ
January 2025
Kidney Disease Center and Medical Education Department, The First Affiliated Hospital, Zhejiang University School of Medicine, 79 Qingchun Road, Hangzhou, Zhejiang, 310003, China.
Background: In modern clinical settings, interdisciplinary clinical reasoning skills and associated education are pivotal and should be encouraged for residency training.
Methods: An interdisciplinary course on clinical reasoning was developed for residents based on ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. We collected frequently encountered consultation cases as our teaching resources with the methods of scenario case-based learning.
Digit Health
January 2025
School of Computer Science, The University of Sydney, Sydney, NSW, Australia.
Objective: Machine learning (ML) has enabled healthcare discoveries by facilitating efficient modeling, such as for cancer screening. Unlike clinical trials, real-world data used in ML are often gathered for multiple purposes, leading to bias and missing information for a specific classification task. This challenge is especially pronounced in healthcare because of stringent ethical considerations and resource constraints.
View Article and Find Full Text PDFFront Med (Lausanne)
January 2025
Department of Psychoanalysis and Psychotherapy, Medical University of Vienna, Vienna, Austria.
Background: The integration of interdisciplinary clinical reasoning and decision-making into the medical curriculum is imperative. Novel, high-quality e-learning environments, encompassing virtual clinical and hands-on training, are essential. Consequently, we evaluated the efficacy of a case-based e-learning approach.
View Article and Find Full Text PDFFront Med (Lausanne)
January 2025
Department of Anesthesiology, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, China.
Objective: To evaluate the effectiveness of integrating GASMAN anesthesia simulation software with case-based learning (IGC) compared to traditional lecture-based learning (LBL) in teaching inhalation anesthesia to undergraduate anesthesiology students.
Methods: Fourth-year students from two academic years (2022, = 110; 2023, = 131) enrolled in a five-year anesthesiology program were assigned to either traditional lecture-based learning (LBL) or IGC groups. The LBL group received traditional lectures using PowerPoint slides, while the IGC group engaged with GASMAN anesthesia simulation software (a tool designed for anesthesia simulation and gas monitoring) combined with case-based learning.
JMIR Med Educ
January 2025
Department of Orthopedics, First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Background: Teaching severe pelvic trauma poses a significant challenge in orthopedic surgery education due to the necessity of both clinical reasoning and procedural operational skills for mastery. Traditional methods of instruction, including theoretical teaching and mannequin practice, face limitations due to the complexity, the unpredictability of treatment scenarios, the scarcity of typical cases, and the abstract nature of traditional teaching, all of which impede students' knowledge acquisition.
Objective: This study aims to introduce a novel experimental teaching methodology for severe pelvic trauma, integrating virtual reality (VR) technology as a potent adjunct to existing teaching practices.
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