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Expert Performance (expert + performance)

Distribution by Scientific Domains
Medical Sciences100%

Selected Abstracts

Deliberate Practice and Acquisition of Expert Performance: A General Overview

ACADEMIC EMERGENCY MEDICINE, Issue 11 2008
K. Anders Ericsson PhD
Traditionally, professional expertise has been judged by length of experience, reputation, and perceived mastery of knowledge and skill. Unfortunately, recent research demonstrates only a weak relationship between these indicators of expertise and actual, observed performance. In fact, observed performance does not necessarily correlate with greater professional experience. Expert performance can, however, be traced to active engagement in deliberate practice (DP), where training (often designed and arranged by their teachers and coaches) is focused on improving particular tasks. DP also involves the provision of immediate feedback, time for problem-solving and evaluation, and opportunities for repeated performance to refine behavior. In this article, we draw upon the principles of DP established in other domains, such as chess, music, typing, and sports to provide insight into developing expert performance in medicine. [source]

Paraconsistent Artificial Neural Network as Auxiliary in Cephalometric Diagnosis

ARTIFICIAL ORGANS, Issue 7 2010
Mauricio C. Mario
Abstract This work presents an application of the paraconsistent artificial neural network (PANN) in the analysis of cephalometric variables and provides an orthodontic diagnosis. An expert's analysis is subject to the inherent imprecision of measurements, registers, and individual variability of physician visual analysis. Patient input cephalometric values are compared with means drawn from individuals considered normal in the cephalometric point of view by the PANN. This reference is constituted by individuals from 6 to 18 years old, both genders. The applied cephalometric analysis was targeted to measure skeletal and dental discrepancies and established a cephalometric diagnosis. The analysis results in degrees of skeletal, anteroposterior, and dental discrepancy, pertinent to upper and lower incisors. A sample of 120 orthodontic patients was processed by the proposed model and three orthodontic experts. Comparisons between the model and the human expert's performance provided kappa indexes that varied from moderate to almost perfect agreement. The agreement between the model and specialist's performance was equivalent. In addition, the model pointed out contradictions presented in the data that were not noticed by the orthodontists, which highlight the contribution that this kind of system could carry out in the orthodontics decision support. [source]