Intelligent platform for automatic medical knowledge acquisition: detection and understanding of neural dysfunctions

2003 ◽  
Author(s):  
M. Zorman ◽  
P. Kokol ◽  
M. Lenic ◽  
P. Povalej ◽  
B. Stiglic ◽  
...  
Keyword(s):  
Knowledge Acquisition ◽  
Medical Knowledge ◽  
Neural Dysfunctions

Social media as a primary source of medical knowledge acquisition and dissemination

2016 ◽  
Vol 127 ◽  
pp. A1 ◽  
Author(s):  
Chieh-Chen Wu ◽  
Richard Lu ◽  
Hsuan-Chia Yang ◽  
Yu-Chuan (Jack) Li
Keyword(s):  
Social Media ◽  
Knowledge Acquisition ◽  
Medical Knowledge ◽  
Primary Source

Guideline-Enabled Data Driven Clinical Knowledge Model for the Treatment of Oral Cavity Cancer Acquired Through a Refined Knowledge Acquisition Method

2019 ◽  
Author(s):  
Maqbool Hussain ◽  
Muhammad Afzal ◽  
Khalid M. Malik ◽  
Taqdir Ali ◽  
Wajahat Ali Khan ◽  
...  
Keyword(s):  
Oral Cavity ◽  
Knowledge Acquisition ◽  
Formal Verification ◽  
Medical Knowledge ◽  
Knowledge Model ◽  
Validation Process ◽  
Hybrid Knowledge ◽  
Verification Process ◽  
Acquisition Method

Validation and verification are the critical requirements in the knowledge acquisition method for the clinical decision support system (CDSS). After acquiring the medical knowledge from diverse sources, the rigorous validation and formal verification process are required before creating the final knowledge model. Previously, we have proposed a hybrid knowledge acquisition method for acquiring medical knowledge from clinical practice guidelines (CPGs) and patient data in the Smart CDSS for treatment of oral cavity cancer. The final knowledge model was created by combining knowledge models obtained from CPGs and patient data after passing through a rigorous validation process. However, detailed analysis shows that due to lack of formal verification process, it involves various inconsistencies in knowledge relevant to the formalism of knowledge, conformance to CPGs, quality of knowledge, and complexities of knowledge acquisition artifacts. Therefore, it is required to enhance a hybrid knowledge acquisition method that thwarts the inconsistencies using formal verification. This paper presents the verification process using the Z formal method and its outcome as an enhanced acquisition method – known as the refined knowledge acquisition (ReKA) method. The ReKA method adopted verification method and explored the mechanism of theorem proving using the Z notation. It enables to identify inconsistencies in the validation process used for hybrid knowledge acquisition. Additionally, it refines the hybrid knowledge acquisition method by discovering the missing steps in the current validation process at the acquisition stage. Consequently, ReKA adds a set of nine additional criteria to be used to have a final valid refined clinical knowledge model. The criteria ensure the validity of final knowledge model concerning formalism of knowledge, conformance to GPGs, quality of the knowledge, usage of stringent conditions and treatment plans, and inconsistencies possibly resulting from the complexities. Evaluation, using four medical knowledge acquisition scenarios, shows that newly added knowledge in CDSS due to the addition of criteria by ReKA method always produces a valid knowledge model. The final knowledge model was also evaluated with 1229 oral cavity patient cases, which outperformed with an accuracy of 72.57\% compared to a similar approach with an accuracy of 69.7\%. Furthermore, ReKA method identified a set of decision paths (about 47.8%) in the existing approach, which results in a final knowledge model with low quality, non-conformed from standard CPGs. In conclusion, ReKA is formally proved method which always yields valid knowledge model having high quality, supporting local practices, and influenced from standard guidelines.

Knowledge-Acquisition Tools for Medical Knowledge-Based Systems

1995 ◽  
Vol 34 (01/02) ◽  
pp. 25-39 ◽  
Author(s):  
G. Lanzola ◽  
S. Quaglini ◽  
M. Stefanelli
Keyword(s):  
Knowledge Acquisition ◽  
Medical Knowledge ◽  
Knowledge Based Systems ◽  
Medical Problems ◽  
Computational Framework ◽  
Domain Experts ◽  
Knowledge Based ◽  
Strategic Issue ◽  
Epistemological Model ◽  
Medical Task

Abstract:Knowledge-based systems (KBS) have been proposed to solve a large variety of medical problems. A strategic issue for KBS development and maintenance are the efforts required for both knowledge engineers and domain experts. The proposed solution is building efficient knowledge acquisition (KA) tools. This paper presents a set of KA tools we are developing within a European Project called GAMES II. They have been designed after the formulation of an epistemological model of medical reasoning. The main goal is that of developing a computational framework which allows knowledge engineers and domain experts to interact cooperatively in developing a medical KBS. To this aim, a set of reusable software components is highly recommended. Their design was facilitated by the development of a methodology for KBS construction. It views this process as comprising two activities: the tailoring of the epistemological model to the specific medical task to be executed and the subsequent translation of this model into a computational architecture so that the connections between computational structures and their knowledge level counterparts are maintained. The KA tools we developed are illustrated taking examples from the behavior of a KBS we are building for the management of children with acute myeloid leukemia.

scholarly journals Transitioning From a Noon Conference to an Academic Half-Day Curriculum Model: Effect on Medical Knowledge Acquisition and Learning Satisfaction

2014 ◽  
Vol 6 (1) ◽  
pp. 93-99 ◽  
Author(s):  
Duc Ha ◽  
Michael Faulx ◽  
Carlos Isada ◽  
Michael Kattan ◽  
Changhong Yu ◽  
...  
Keyword(s):  
Internal Medicine ◽  
Knowledge Acquisition ◽  
Cleveland Clinic ◽  
Medical Knowledge ◽  
Learner Satisfaction ◽  
Learning Satisfaction ◽  
Medical Licensing ◽  
Sex Type ◽  
Multivariable Analyses ◽  
Resident Learning

Abstract Background The academic half-day (AHD) curriculum is an alternative to the traditional noon conference in graduate medical education, yet little is known regarding its effect on knowledge acquisition and resident satisfaction. Objective We investigated the association between the 2 approaches for delivering the curriculum and knowledge acquisition, as reflected by the Internal Medicine In-Training Examination (IM-ITE) scores and assessed resident learning satisfaction under both curricula. Methods The Cleveland Clinic Internal Medicine Residency Program transitioned from the noon conference to the AHD curriculum in 2011. Covariates for residents enrolled from 2004 to 2011 were age; sex; type of medical degree; United States Medical Licensing Examination Step 1, 2 Clinical Knowledge; and IM-ITE-1 scores. We performed univariable and multivariable linear regressions to investigate the association between covariates and IM-ITE-2 and IM-ITE-3 scores. Residents also were surveyed about their learning satisfaction in both curricula. Results Of 364 residents, 112 (31%) and 252 (69%) were exposed to the AHD and the noon conference curriculum, respectively. In multivariable analyses, the AHD curriculum was associated with higher IM-ITE-3 (regression coefficient, 4.8; 95% confidence interval 2.9–6.6) scores, and residents in the AHD curriculum had greater learning satisfaction compared with the noon conference cohort (Likert, 3.4 versus 3.0; P  =  .003). Conclusions The AHD curriculum was associated with improvement in resident medical knowledge acquisition and increased learner satisfaction.

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