Jeffrey Jopling
Jeffrey Jopling
Postdoctoral Fellow, Clinical Excellence Research Center, Stanford University
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Reference ranges for hematocrit and blood hemoglobin concentration during the neonatal period: data from a multihospital health care system
J Jopling, E Henry, SE Wiedmeier, RD Christensen
Pediatrics 123 (2), e333-e337, 2009
The CBC: reference ranges for neonates
RD Christensen, E Henry, J Jopling, SE Wiedmeier
Seminars in perinatology 33 (1), 3-11, 2009
Expected ranges for blood neutrophil concentrations of neonates: the Manroe and Mouzinho charts revisited
N Schmutz, E Henry, J Jopling, RD Christensen
Journal of Perinatology 28 (4), 275-281, 2008
The erythrocyte indices of neonates, defined using data from over 12 000 patients in a multihospital health care system
RD Christensen, J Jopling, E Henry, SE Wiedmeier
Journal of Perinatology 28 (1), 24-28, 2008
Tool detection and operative skill assessment in surgical videos using region-based convolutional neural networks
A Jin, S Yeung, J Jopling, J Krause, D Azagury, A Milstein, L Fei-Fei
2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 691-699, 2018
Identifying Distinct Subgroups of Intensive Care Unit Patients: a Machine Learning Approach
KC Vranas, JK Jopling, TE Sweeney, MC Ramsey, AS Milstein, ...
Critical care medicine 45 (10), 1607, 2017
Towards vision-based smart hospitals: A system for tracking and monitoring hand hygiene compliance
A Haque, M Guo, A Alahi, S Yeung, Z Luo, A Rege, J Jopling, L Downing, ...
arXiv preprint arXiv:1708.00163, 2017
A computer vision system for deep learning-based detection of patient mobilization activities in the ICU
S Yeung, F Rinaldo, J Jopling, B Liu, R Mehra, NL Downing, M Guo, ...
NPJ digital medicine 2 (1), 1-5, 2019
Priority queuing models for hospital intensive care units and impacts to severe case patients
MS Hagen, JK Jopling, TG Buchman, EK Lee
AMIA Annual Symposium Proceedings 2013, 841, 2013
Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks. arXiv: 180208774 Cs
A Jin, S Yeung, J Jopling, J Krause, D Azagury, A Milstein, L Fei-Fei
February, 2018
Sepsis through the Eyes of an Engineer− Why Treatments Have Succeeded and Failed
J Jopling, TG Buchman
Critical Reviews™ in Biomedical Engineering 40 (4), 2012
To cut is to cure: the surgeon's role in improving value
JK Jopling, CC Sheckter, BC James
Annals of Surgery 267 (5), 817-819, 2018
3d point cloud-based visual prediction of icu mobility care activities
B Liu, M Guo, E Chou, R Mehra, S Yeung, NL Downing, F Salipur, ...
Machine Learning for Healthcare Conference, 17-29, 2018
The Association of ICU with Outcomes of Patients at Low Risk of Dying
KC Vranas, JK Jopling, JY Scott, O Badawi, MO Harhay, CG Slatore, ...
Critical care medicine 46 (3), 347, 2018
Déjà Vu: Introducing Operations Research to Health Care
TH Wagner, JK Jopling
Medical Decision Making 37 (8), 847-848, 2017
Detecting organisational innovations leading to improved ICU outcomes: a protocol for a double-blinded national positive deviance study of critical care delivery
H Chiou, JK Jopling, JY Scott, M Ramsey, K Vranas, TH Wagner, ...
BMJ open 7 (6), e015930, 2017
BURNED: towards efficient and accurate burn prognosis using deep learning
O Despo, S Yeung, J Jopling, B Pridgen, C Sheckter, S Silberstein, ...
Vision-based prediction of ICU mobility care activities using recurrent neural networks
G Bianconi, R Mehra, S Yeung, F Salipur, J Jopling, L Downing, A Haque, ...
NIPS workshop on Machine Learning for Health, 2017
Estimating The Proportion Of Patients" too Well" To Benefit From Critical Care: A Machine Learning Approach
K Vranas, J Jopling, M Ramsey, TE Sweeney, GJ Escobar, V Liu
Performance improvement in surgery.
RF Alban, EC Anania, TN Cohen, PJ Fabri, BL Gewertz, M Jain, ...
Current problems in surgery 56 (6), 204-208, 2019
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