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Recent works have shown that applying Machine Learning to Electronic Health Records (EHR) can strongly accelerate precision medicine.
Preterm birth: Causes consequences and prevention. committee on understanding premature birth and assuring health outcomes, institute of medicine of the national academies, 2006
Richard Behrman and A Butler · 2006
Earlier work this paper cites.
Accuracy of single progesterone test to predict early pregnancy outcome in women with pain or bleeding: meta-analysis of cohort studies
Jorine Verhaegen, Ioannis D Gallos, Norah M Van Mello, Mohamed Abdel-Aziz, Yemisi Takwoingi, Hoda Harb, Jonathan J Deeks, Ben WJ Mol, and Arri Coomarasamy · 2012
Earlier work this paper cites.
Analyzing risks of adverse pregnancy outcomes
Michael S Kramer, Xun Zhang, and Robert W Platt · 2013
Earlier work this paper cites.
The distribution of clinical phenotypes of preterm birth syndrome: implications for prevention
Fernando C Barros, Aris T Papageorghiou, Cesar G Victora, Julia A Noble, Ruyan Pang, Jay Iams, Leila Cheikh Ismail, Robert L Goldenberg, Ann Lambert, Michael S Kramer, et al · 2015
Earlier work this paper cites.
Federated learning: Strategies for improving communication efficiency
F. X. Yu P. Richtárik A. T. Suresh J. Konecny, H. B. McMahan and D. Bacon · 2016
Earlier work this paper cites.
Retain: An interpretable predictive model for healthcare using reverse time attention mechanism
Edward Choi, Mohammad Taha Bahadori, Jimeng Sun, Joshua Kulas, Andy Schuetz, and Walter Stewart · 2016
Earlier work this paper cites.
Preterm birth prediction: Deriving stable and interpretable rules from high dimensional data
Truyen Tran, Wei Luo, Dinh Phung, Jonathan Morris, Kristen Rickard, and Svetha Venkatesh · 2016
Earlier work this paper cites.
Using kernel methods and model selection for prediction of preterm birth
Ilia Vovsha, Ansaf Salleb-Aouissi, Anita Raja, Thomas Koch, Alex Rybchuk, Axinia Radeva, Ashwath Rajan, Yiwen Huang, Hatim Diab, Ashish Tomar, et al · 2016
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Doctor ai: Predicting clinical events via recurrent neural networks
Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F Stewart, and Jimeng Sun · 2016
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Communication-efficient learning of deep networks from decentralized data
Daniel Ramage Seth Hampson Brendan McMahan, Eider Moore and Blaise Aguera y Arcas · 2017
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Federated multi-task learning
Maziar Sanjabi Virginia Smith, Chao-Kai Chiang and Ameet Talwalkars · 2017
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Cited alongside, same era.
Fadl: Federated-autonomous deep learning for distributed electronic health record
Dianbo Liu, Timothy Miller, Raheel Sayeed, and Kenneth Mandl · 2018
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Alternating loss correction for preterm-birth prediction from ehr data with noisy labels
Sabri Boughorbel, Fethi Jarray, Neethu Venugopal, and Haithum Elhadi · 2018
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Federated machine learning: Concept and applications
Qiang Yang, Yang Liu, Tianjian Chen, and Yongxin Tong · 2019
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Robust and communication-efficient federated learning from non-iid data
Klaus-Robert Müller Felix Sattler, Simon Wiedemann and Wojciech Samek · 2019
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On the convergence of fedavg on non-iid data
Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang · 2019
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Yang Liu, Tianjian Chen, and Qiang Yang · 2018
Cited alongside, same era.
Federated learning with non-iid data
Liangzhen Lai Naveen Suda Damon Civin Vikas Chandra Yue Zhao, Meng Li · 2018
Cited alongside, same era.
Kewei Cheng, Tao Fan, Yilun Jin, Yang Liu, Tianjian Chen, and Qiang Yang · 2019
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Analyzing the role of model uncertainty for electronic health records
Michael W Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine Heller, and Andrew M Dai · 2019
Closest in time.