Prediction of pediatric injury by bayesian approach: A proposed framework for pre-diagnosis by automate system

2011 ◽  
Author(s):  
Sakda Arj-Ong ◽  
Poonphon Suesaowaluk
Keyword(s):  
Bayesian Approach ◽  
Automate System ◽  
Pediatric Injury

Evidence-Based Assessment of Pediatric Injury and Injury Risk

2013 ◽  
Author(s):  
Bryan T. Karazsia ◽  
Keri J. Brown Kirschman
Keyword(s):  
Injury Risk ◽  
Evidence Based ◽  
Pediatric Injury

STUDY OF PEDIATRIC INJURIES: PATTERNS AND OUTCOME

2019 ◽  
Vol 3 (8) ◽  
Author(s):  
Yasir. B. Elshambaty
Keyword(s):  
School Level ◽  
Common Mechanism ◽  
Injury Patterns ◽  
Severe Injuries ◽  
Injured Child ◽  
Cross Sectional ◽  
Pediatric Injuries ◽  
Pediatric Injury ◽  
The Common ◽  
Structured Questionnaire

Purpose this study aims to show the patterns and outcome of pediatric injury among those living in Albaha region in Saudi Arabia Methods this is a cross-sectional descriptive household-based study, included children between 0-17 years old both male and female. The data were collected with structured questionnaire between 20 Nov – 20 Dec 2018 and  analyzed with SPSS version 25 Results the total of participants was 257 injured child. 199(77.4%) are male and 58(22.6%) are female. About 44%of them were injured at pre-school level and 56% were traumatized at school age. The least incidence of injury occurred in those less than 2 yrs and higher incidence in those between 3-10 yrs old. The most common mechanism of injury was falling from height. The most affected group age by RTA accidents was 11-17 yrs old. Approximately 83% of the injured children required hospital management. Only one third of the injuries were  associated complications. The most common injured anatomic part was the upper limb and the least affected part was the spine. Only 5% of the injuries were associated with a disability and the common was loss of organ or part of it. Paralysis occurred in less than 1% and head injury resulted in disabilities more than 1%. Conclusion the vast majority of the injuries in our participants are not serious. The severe injuries were associated with RTA-related trauma. Most of injuries due to falling from height are not serious. We recommend not to allow the children to drive cars. Keywords: pediatric injuries; injury patterns; household.

Verbal statistical learning: Prior knowledge impacts word identification but not metacognition

2020 ◽  
Author(s):  
Laetitia Zmuda ◽  
Charlotte Baey ◽  
Paolo Mairano ◽  
Anahita Basirat
Keyword(s):  
Task Performance ◽  
Prior Knowledge ◽  
Statistical Learning ◽  
Cognitive Processes ◽  
Bayesian Approach ◽  
Word Identification ◽  
Artificial Language ◽  
Artificial Languages ◽  
Transitional Probabilities ◽  
Fully Bayesian

It is well-known that individuals can identify novel words in a stream of an artificial language using statistical dependencies. While underlying computations are thought to be similar from one stream to another (e.g. transitional probabilities between syllables), performance are not similar. According to the “linguistic entrenchment” hypothesis, this would be due to the fact that individuals have some prior knowledge regarding co-occurrences of elements in speech which intervene during verbal statistical learning. The focus of previous studies was on task performance. The goal of the current study is to examine the extent to which prior knowledge impacts metacognition (i.e. ability to evaluate one’s own cognitive processes). Participants were exposed to two different artificial languages. Using a fully Bayesian approach, we estimated an unbiased measure of metacognitive efficiency and compared the two languages in terms of task performance and metacognition. While task performance was higher in one of the languages, the metacognitive efficiency was similar in both languages. In addition, a model assuming no correlation between the two languages better accounted for our results compared to a model where correlations were introduced. We discuss the implications of our findings regarding the computations which underlie the interaction between input and prior knowledge during verbal statistical learning.

Sign in / Sign up

Export Citation Format

Share Document