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We present a self-supervised, time-to-event (TTE) foundation model called MOTOR (Many Outcome Time Oriented Representations) which is pretrained on timestamped sequences of events in electronic health records (EHR) and health insurance claims.
Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
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Behrt: Transformer for electronic health records, 2019
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Unified methods for censored longitudinal data and causality , volume 5
Mark J Van der Laan and James M Robins · 2003
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Time-to-event predictive modeling for chronic conditions using electronic health records
Yu-Kai Lin, Hsinchun Chen, Randall A Brown, Shu-Hsing Li, and Hung-Jen Yang · 2014
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Wush Chi-Hsuan Wu, Mi-Yen Yeh, and Ming-Syan Chen · 2015
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Recurrent neural networks for multivariate time series with missing values, 2016
Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, and Yan Liu · 2016
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Edward Choi, Mohammad Taha Bahadori, Joshua A. Kulas, Andy Schuetz, Walter F. Stewart, and Jimeng Sun · 2016
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Gaussian processes for survival analysis
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R. Miotto, L. Li, B. A. Kidd, and J. T. Dudley · 2016
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Cehr-bert: Incorporating temporal information from structured ehr data to improve prediction tasks
Chao Pang, Xinzhuo Jiang, Krishna S. Kalluri, Matthew Spotnitz, RuiJun Chen, Adler Perotte, and Karthik Natarajan · 2021
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Covrnn—a recurrent neural network model for predicting outcomes of covid-19 patients: model development and validation using ehr data
Laila Rasmy, Masayuki Nigo, Bijun Sai Kannadath, Ziqian Xie, Bingyu Mao, Khush Patel, Yujia Zhou, Wanheng Zhang, Angela Ross, Hua Xu, and Degui Zhi · 2021
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Chapter 5 standardized vocabularies, Jan 2021
Observational Health Data Sciences and Informatics · 2021
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Language models are an effective representation learning technique for electronic health record data
Ethan Steinberg, Ken Jung, Jason A Fries, Conor K Corbin, Stephen R Pfohl, and Nigam H Shah · 2021
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Time-to-event modeling for hospital length of stay prediction for COVID-19 patients
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