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In 2019, The Centers for Medicare and Medicaid Services (CMS) launched an Artificial Intelligence (AI) Health Outcomes Challenge seeking solutions to predict risk in value-based care for incorporation into CMS Innovation Center payment and service delivery models.
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
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SECNLP: A Survey of Embeddings in Clinical Natural Language Processing
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Publicly available clinical BERT embeddings
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Pre-training of Graph Augmented Transformers for Medication Recommendation
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Improved hierarchical patient classification with language model pretraining over clinical notes
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Language models are an effective representation learning technique for electronic health record data
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Rehospitalizations among Patients in the Medicare Fee-for-Service Program
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Yuqi Si, Jingcheng Du, Zhao Li, Xiaoqian Jiang, Timothy Miller, Fei Wang, W. Jim Zheng, and Kirk Roberts. 2020 · 2010
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Predicting risk of hospitalization or death among patients with heart failure in the veterans health administration
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On the Properties of Neural Machine Translation: Encoder-Decoder Approaches
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A Tool for prediction of risk of rehospitalisation and mortality in the hospitalised elderly: Secondary analysis of clinical trial data
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Development, validation and deployment of a real time 30 day hospital readmission risk assessment tool in the Maine healthcare information exchange
Shiying Hao, Yue Wang, Bo Jin, Andrew Young Shin, Chunqing Zhu, Min Huang, Le Zheng, Jin Luo, Zhongkai Hu, Changlin Fu, Dorothy Dai, Yicheng Wang, Devore S. Culver, Shaun T. Alfreds, Todd Rogow, Frank Stearns, Karl G. Sylvester, Eric Widen, and Xuefeng B. Ling. 2015 · 2015
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Do non-clinical factors improve prediction of readmission risk?. Results from the Tele-HF study
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Joint impact of clinical and behavioral variables on the risk of unplanned readmission and death after a heart failure hospitalization
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Unscheduled-return-visits after an emergency department (ED) attendance and clinical link between both visits in patients aged 75 years and over: A prospective observational study
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Predictive models for hospital readmission risk: A systematic review of methods
Arkaitz Artetxe, Andoni Beristain, and Manuel Graña. 2018 · 2018
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Hospital readmission risk prediction based on claims data available at admission: A pilot study in Switzerland
Beat Brüngger and Eva Blozik. 2019 · 2018
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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An Effective Patient Representation Learning for Time-series Prediction Tasks Based on EHRs. In Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 . IEEE, 885–892
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Cited alongside, same era.
Dynamic hierarchical classification for patient risk-of-readmission
Senjuti Basu Roy, Ankur Teredesai, Kiyana Zolfaghar, Rui Liu, David Hazel, Stacey Newman, and Albert Marinez. 2015 · 2015
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Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)
Truyen Tran, Tu Dinh Nguyen, Dinh Phung, and Svetha Venkatesh. 2015 · 2015
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An Absolute Risk Prediction Model to Determine Unplanned Cardiovascular Readmissions for Adults with Chronic Heart Failure
Betihavas V., Frost S.A., Newton P.J., Macdonald P., Stewart S., Carrington M.J., and Chan Y.K. 2015 · 2015
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Predicting readmission risk with institution-specific prediction models
Shipeng Yu, Faisal Farooq, Alexander van Esbroeck, Glenn Fung, Vikram Anand, and Balaji Krishnapuram. 2015 · 2015
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RETAIN: An interpretable predictive model for healthcare using reverse time attention mechanism
Edward Choi, Mohammad Taha Bahadori, Joshua A. Kulas, Andy Schuetz, Walter F. Stewart, and Jimeng Sun. 2016a · 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. 2016b · 2016
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Multi-layer Representation Learning for Medical Concepts
Edward Choi, Mohammad Taha Bahadori, Elizabeth Searles, Catherine Coffey, and Jimeng Sun. 2016c · 2016
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Exploring the predictors of early readmission to psychiatric hospital
A. D. Tulloch, A. S. David, and G. Thornicroft. 2016 · 2016
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Liqi Lei, Yangming Zhou, Jie Zhai, Le Zhang, Zhijia Fang, Ping He, and Ju Gao. 2019 · 2018
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KAME: Knowledge-based attention model for diagnosis prediction in healthcare
Fenglong Ma, Radha Chitta, Quanzeng You, Jing Zhou, Houping Xiao, and Jing Gao. 2018a · 2018
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Health-ATM: A deep architecture for multifaceted patient health record representation and risk prediction
Tengfei Ma, Cao Xiao, and Fei Wang. 2018b · 2018
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Scalable and accurate deep learning with electronic health records
Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M. Dai, Nissan Hajaj, Michaela Hardt, Peter J. Liu, Xiaobing Liu, Jake Marcus, Mimi Sun, Patrik Sundberg, Hector Yee, Kun Zhang, Yi Zhang, Gerardo Flores, Gavin E. Duggan, Jamie Irvine, Quoc Le, Kurt Litsch, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L. Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H. Shah, Atul J. Butte, Michael D. Howell, Claire Cui, Greg S. Corrado, and Jeffrey Dean. 2018 · 2018
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Predicting Hospital Readmission via Cost-Sensitive Deep Learning
H. Wang, Z. Cui, Y. Chen, M. Avidan, A. B. Abdallah, and A. Kronzer. 2018 · 2018
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Readmission prediction via deep contextual embedding of clinical concepts
Cao Xiao, Tengfei Ma, Adji B. Dieng, David M. Blei, and Fei Wang. 2018 · 2018
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RAIM: Recurrent attentive and intensive model of multimodal patient monitoring data
Yanbo Xu, Siddharth Biswal, Shriprasad R. Deshpande, Kevin O. Maher, and Jimeng Sun. 2018 · 2018
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Jinghe Zhang, Kamran Kowsari, James H. Harrison, Jennifer M. Lobo, and Laura E. Barnes. 2018 · 2018
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Jacob Devlin, Ming Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Decoupled Weight Decay Regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
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Predictive Modeling of the Hospital Readmission Risk from Patients’ Claims Data Using Machine Learning: A Case Study on COPD
Xu Min, Bin Yu, and Fei Wang. 2019 · 2019
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Deep Patient Representation of Clinical Notes via Multi-Task Learning for Mortality Prediction
Yuqi Si and Kirk Roberts. 2019 · 2019
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MetaPred: Meta-Learning for Clinical Risk Prediction with Limited Patient Electronic Health Records. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . ACM, New York, NY, USA, 2487–2495
Xi Sheryl Zhang, Fengyi Tang, Hiroko H. Dodge, Jiayu Zhou, and Fei Wang. 2019 · 2019
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BEHRT: Transformer for Electronic Health Records
Yikuan Li, Shishir Rao, José Roberto Ayala Solares, Abdelaali Hassaine, Rema Ramakrishnan, Dexter Canoy, Yajie Zhu, Kazem Rahimi, and Gholamreza Salimi-Khorshidi. 2020 · 2020
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