재정 지원 요구사항을 통해 공개된 자료 - Jenna Wiens자세히 알아보기
제공된 곳이 있음: 63
Machine Learning for Healthcare: On the Verge of a Major Shift in Healthcare Epidemiology
J Wiens, ES Shenoy
Clinical Infectious Diseases, 2017
재정 지원 요구사항 정책: US National Science Foundation, US National Institutes of Health
A framework for effective application of machine learning to microbiome-based classification problems
BD Topçuoğlu, NA Lesniak, MT Ruffin IV, J Wiens, PD Schloss
MBio 11 (3), 10.1128/mbio. 00434-20, 2020
재정 지원 요구사항 정책: US National Institutes of Health
A Generalizable, Data-Driven Approach to Predict Daily Risk of Clostridium difficile Infection at Two Large Academic Health Centers
J Oh, M Makar, C Fusco, R McCaffrey, K Rao, EE Ryan, L Washer, ...
Infection Control and Hospital Epidemiology 39 (4), 425-433, 2018
재정 지원 요구사항 정책: US National Science Foundation, US National Institutes of Health
Evaluating a widely implemented proprietary deterioration index model among hospitalized patients with COVID-19
K Singh, TS Valley, S Tang, BY Li, F Kamran, MW Sjoding, J Wiens, ...
Annals of the American Thoracic Society 18 (7), 1129-1137, 2021
재정 지원 요구사항 정책: US National Institutes of Health
Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories
I Fox, L Ang, M Jaiswal, R Pop-Busui, J Wiens
KDD'18 Proceedings of the 24th ACM SIGKDD International Conference on …, 2018
재정 지원 요구사항 정책: US National Science Foundation, US National Institutes of Health
Deep reinforcement learning for closed-loop blood glucose control
I Fox, J Lee, R Pop-Busui, J Wiens
Machine Learning for Healthcare Conference, 508-536, 2020
재정 지원 요구사항 정책: Juvenile Diabetes Research Foundation
Machine learning for patient risk stratification for acute respiratory distress syndrome
D Zeiberg, T Prahlad, BK Nallamothu, TJ Iwashyna, J Wiens, MW Sjoding
PloS one 14 (3), e0214465, 2019
재정 지원 요구사항 정책: US National Institutes of Health, US Department of Veterans Affairs
Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data
S Tang, P Davarmanesh, Y Song, D Koutra, MW Sjoding, J Wiens
Journal of the American Medical Informatics Association 27 (12), 1921-1934, 2020
재정 지원 요구사항 정책: US National Science Foundation, US National Institutes of Health
Model selection for offline reinforcement learning: Practical considerations for healthcare settings
S Tang, J Wiens
Machine Learning for Healthcare Conference, 2-35, 2021
재정 지원 요구사항 정책: US National Science Foundation, US National Institutes of Health
Heart sound classification based on temporal alignment techniques
JJG Ortiz, CP Phoo, J Wiens
2016 computing in cardiology conference (CinC), 589-592, 2016
재정 지원 요구사항 정책: US National Science Foundation
Using Machine Learning and the Electronic Health Record to Predict Complicated Clostridium difficile Infection
BY Li, J Oh, VB Young, K Rao, J Wiens
Open forum infectious diseases 6 (5), ofz186, 2019
재정 지원 요구사항 정책: US National Institutes of Health
Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks
J Oh, J Wang, J Wiens
Proceedings of the 3rd Machine Learning for Health Care (MLHC), 2018
재정 지원 요구사항 정책: US National Science Foundation, US National Institutes of Health
The number needed to benefit: estimating the value of predictive analytics in healthcare
VX Liu, DW Bates, J Wiens, NH Shah
Journal of the American Medical Informatics Association 26 (12), 1655-1659, 2019
재정 지원 요구사항 정책: US National Institutes of Health
Deep Residual Time-Series Forecasting: Application to Blood Glucose Prediction.
H Rubin-Falcone, I Fox, J Wiens
KDH@ ECAI 20, 105-109, 2020
재정 지원 요구사항 정책: Juvenile Diabetes Research Foundation
Deep learning applied to chest X-rays: exploiting and preventing shortcuts
S Jabbour, D Fouhey, E Kazerooni, MW Sjoding, J Wiens
Machine Learning for Healthcare Conference, 750-782, 2020
재정 지원 요구사항 정책: US National Science Foundation, US National Institutes of Health
Early identification of patients admitted to hospital for covid-19 at risk of clinical deterioration: model development and multisite external validation study
F Kamran, S Tang, E Otles, DS McEvoy, SN Saleh, J Gong, BY Li, S Dutta, ...
The British Medical Journal (BMJ) 376, 2022
재정 지원 요구사항 정책: US National Science Foundation, US National Institutes of Health
Learning Credible Models
J Wang, J Oh, J Wiens
KDD'18 The 24th ACM SIGKDD International Conference on Knowledge Discovery …, 2018
재정 지원 요구사항 정책: US National Science Foundation, US National Institutes of Health
Characterizing heterogeneity in the progression of Alzheimer's disease using longitudinal clinical and neuroimaging biomarkers
D Goyal, D Tjandra, RQ Migrino, B Giordani, Z Syed, J Wiens, ...
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring 10, 629-637, 2018
재정 지원 요구사항 정책: US National Science Foundation, US Department of Defense, US National …
Automatically evaluating balance: a machine learning approach
T Bao, BN Klatt, SL Whitney, KH Sienko, J Wiens
IEEE transactions on neural systems and rehabilitation engineering 27 (2 …, 2019
재정 지원 요구사항 정책: US National Science Foundation, US National Institutes of Health
mikropml: user-friendly R package for supervised machine learning pipelines
BD Topçuoğlu, Z Lapp, KL Sovacool, E Snitkin, J Wiens, PD Schloss
Journal of open source software 6 (61), 2021
재정 지원 요구사항 정책: US National Science Foundation, US National Institutes of Health
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