4 research outputs found
Classification of Cognitive Load and Expertise for Adaptive Simulation using Deep Multitask Learning
Simulations are a pedagogical means of enabling a risk-free way for
healthcare practitioners to learn, maintain, or enhance their knowledge and
skills. Such simulations should provide an optimum amount of cognitive load to
the learner and be tailored to their levels of expertise. However, most current
simulations are a one-type-fits-all tool used to train different learners
regardless of their existing skills, expertise, and ability to handle cognitive
load. To address this problem, we propose an end-to-end framework for a trauma
simulation that actively classifies a participant's level of cognitive load and
expertise for the development of a dynamically adaptive simulation. To
facilitate this solution, trauma simulations were developed for the collection
of electrocardiogram (ECG) signals of both novice and expert practitioners. A
multitask deep neural network was developed to utilize this data and classify
high and low cognitive load, as well as expert and novice participants. A
leave-one-subject-out (LOSO) validation was used to evaluate the effectiveness
of our model, achieving an accuracy of 89.4% and 96.6% for classification of
cognitive load and expertise, respectively.Comment: 2019 IEEE. Personal use of this material is permitted. Permission
from IEEE must be obtained for all other uses, in any current or future
media, including reprinting/republishing this material for advertising or
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this work in other work
Using research to prepare for outbreaks of severe acute respiratory infection
Abstract
Severe acute respiratory infections (SARI) remain one of the leading causes of mortality around the world in all age groups. There is large global variation in epidemiology, clinical management and outcomes, including mortality. We performed a short period observational data collection in critical care units distributed globally during regional peak SARI seasons from 1 January 2016 until 31 August 2017, using standardised data collection tools. Data were collected for 1 week on all admitted patients who met the inclusion criteria for SARI, with follow-up to hospital discharge. Proportions of patients across regions were compared for microbiology, management strategies and outcomes. Regions were divided geographically and economically according to World Bank definitions. Data were collected for 682 patients from 95 hospitals and 23 countries. The overall mortality was 9.5%. Of the patients, 21.7% were children, with case fatality proportions of 1% for those less than 5 years. The highest mortality was in those above 60 years, at 18.6%. Case fatality varied by region: East Asia and Pacific 10.2% (21 of 206), Sub-Saharan Africa 4.3% (8 of 188), South Asia 0% (0 of 35), North America 13.6% (25 of 184), and Europe and Central Asia 14.3% (9 of 63). Mortality in low-income and low-middle-income countries combined was 4% as compared with 14% in high-income countries. Organ dysfunction scores calculated on presentation in 560 patients where full data were available revealed Sequential Organ Failure Assessment (SOFA) scores on presentation were significantly associated with mortality and hospital length of stay. Patients in East Asia and Pacific (48%) and North America (24%) had the highest SOFA scores of >12. Multivariable analysis demonstrated that initial SOFA score and age were independent predictors of hospital survival. There was variability across regions and income groupings for the critical care management and outcomes of SARI. Intensive care unit-specific factors, geography and management features were less reliable than baseline severity for predicting ultimate outcome. These findings may help in planning future outbreak severity assessments, but more globally representative data are required
Using research to prepare for outbreaks of severe acute respiratory infection
Severe acute respiratory infections (SARI) remain one of the leading
causes of mortality around the world in all age groups. There is large
global variation in epidemiology, clinical management and outcomes,
including mortality. We performed a short period observational data
collection in critical care units distributed globally during regional
peak SARI seasons from 1 January 2016 until 31 August 2017, using
standardised data collection tools. Data were collected for 1 week on
all admitted patients who met the inclusion criteria for SARI, with
follow-up to hospital discharge. Proportions of patients across regions
were compared for microbiology, management strategies and outcomes.
Regions were divided geographically and economically according to World
Bank definitions. Data were collected for 682 patients from 95 hospitals
and 23 countries. The overall mortality was 9.5%. Of the patients,
21.7% were children, with case fatality proportions of 1% for those
less than 5 years. The highest mortality was in those above 60 years, at
18.6%. Case fatality varied by region: East Asia and Pacific 10.2% (21
of 206), Sub-Saharan Africa 4.3% (8 of 188), South Asia 0% (0 of 35),
North America 13.6% (25 of 184), and Europe and Central Asia 14.3% (9
of 63). Mortality in low-income and low-middle-income countries combined
was 4% as compared with 14% in high-income countries. Organ
dysfunction scores calculated on presentation in 560 patients where full
data were available revealed Sequential Organ Failure Assessment (SOFA)
scores on presentation were significantly associated with mortality and
hospital length of stay. Patients in East Asia and Pacific (48%) and
North America (24%) had the highest SOFA scores of >12. Multivariable
analysis demonstrated that initial SOFA score and age were independent
predictors of hospital survival. There was variability across regions
and income groupings for the critical care management and outcomes of
SARI. Intensive care unit-specific factors, geography and management
features were less reliable than baseline severity for predicting
ultimate outcome. These findings may help in planning future outbreak
severity assessments, but more globally representative data are
required