Erik Drysdale
Erik Drysdale
Machine Learning Specialist, The Hospital for Sick Children
Geverifieerd e-mailadres voor sickkids.ca - Homepage
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Mammary molecular portraits reveal lineage-specific features and progenitor cell vulnerabilities
AE Casey, A Sinha, R Singhania, J Livingstone, P Waterhouse, ...
Journal of Cell Biology 217 (8), 2951-2974, 2018
202018
A population-based study of the treatment effect of first-line ipilimumab for metastatic or unresectable melanoma
E Drysdale, Y Peng, P Nguyen, T Baetz, TP Hanna
Melanoma research 29 (6), 635, 2019
62019
The origins and consequences of localized and global somatic hypermutation
F Yousif, SD Prokopec, RX Sun, F Fan, CM Lalansingh, E Drysdale, ...
BioRxiv, 287839, 2018
62018
Accurate Classification of Pediatric Colonic Inflammatory Bowel Disease Subtype Using a Random Forest Machine Learning Classifier
J Dhaliwal, L Erdman, E Drysdale, F Rinawi, J Muir, TD Walters, I Siddiqui, ...
Journal of Pediatric Gastroenterology and Nutrition 72 (2), 262-269, 2021
42021
Trends and relevance in the bladder and bowel dysfunction literature: PlumX metrics contrasted with fragility indicators
M Rickard, DT Keefe, E Drysdale, L Erdman, JH Hannick, K Milford, ...
Journal of Pediatric Urology 16 (4), 477. e1-477. e7, 2020
22020
The false positive control lasso
E Drysdale, Y Peng, TP Hanna, P Nguyen, A Goldenberg
arXiv preprint arXiv:1903.12584, 2019
22019
Dear Watch, Should I Get a COVID-19 Test? Designing deployable machine learning for wearables
B Nestor, J Hunter, R Kainkaryam, E Drysdale, JB Inglis, A Shapiro, ...
medRxiv, 2021
12021
Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds
E Drysdale, D Singh, A Goldenberg
arXiv preprint arXiv:2011.06058, 2020
12020
From Clinic to Computer and Back Again: Practical Considerations When Designing and Implementing Machine Learning Solutions for Pediatrics
DS Sujay Nagaraj, Vinyas Harish, Liam G. McCoy, Felipe Morgado MSc, Ian ...
Current Treatment Options in Pediatrics, 2020
12020
Implementing AI in healthcare
E Drysdale, E Dolatabadi, C Chivers, V Liu, S Saria, M Sendak, J Wiens, ...
12019
MP44-13 A SECOND-LOOK AT REPORTED STATISTICS: CHALLENGES IN REPLICATING REPORTED P-VALUES IN PEDIATRIC UROLOGY LITERATURE
E Drysdale, L Erdman, M Rickard, M Skreta, JK Kim, DT Keefe, JD Santos, ...
The Journal of Urology 206 (Supplement 3), e797-e798, 2021
2021
Dear Watch, Should I get a COVID Test? Designing deployable machine learning for wearables
A Goldenberg, B Nestor, J Hunter, R Kainkaryam, E Drysdale, J Inglis, ...
2021
Automatically disambiguating medical acronyms with ontology-aware deep learning
DSMB Marta Skreta, Aryan Arbabi, Jixuan Wang, Erik Drysdale, Jacob Kelly
Nature Communications 12, 2021
2021
The Telomere Length Landscape of Localized Prostate Cancer
J Livingstone, YJ Shiah, TN Yamaguchi, LE Heisler, V Huang, R Lesurf, ...
bioRxiv, 2021
2021
MP71-09 COMPARISON OF FRAGILITY METRICS BETWEEN 2 COMMON PEDIATRIC UROLOGY BODIES OF LITERATURE
M Rickard, J Hannick*, DT Keefe, E Drysdale, L Erdman, JD Santos, ...
The Journal of Urology 203 (Supplement 4), e1067-e1067, 2020
2020
Achieving Clinical Automation in Emergency Medicine with Machine Learning Medical Directives
D Singh, S Nagaraj, P Mashouri, E Drysdale, J Fischer, A Goldenberg, ...
Available at SSRN 3857668, 0
Adjusting survival curves with inverse probability weights
E Drysdale
Hypothesis: We hypothesize that sepsis can be predicted in children 0-18 years of age presenting to Emergency Departments (ED) at triage using modern machine learning (ML …
MD Devin Singh, C McLean, L Radebe, L Erdman, E Drysdale
Machine Learning Based Medical Directives at Triage in Pediatric Emergency Medicine: The First Step to Automated Pathways for Healthcare Delivery
MD Devin Singh, C McLean, L Erdman, L Radebe, E Drysdale, J Fischer
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Artikelen 1–19