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Clinical predictive models often rely on patients' electronic health records (EHR), but integrating medical knowledge to enhance predictions and decision-making is challenging.
Pre-training of graph augmented transformers for medication recommendation
Junyuan Shang, Tengfei Ma, Cao Xiao, and Jimeng Sun · 1906
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Olivier Bodenreider · 2004
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Bio2rdf: towards a mashup to build bioinformatics knowledge systems
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Mathieu Bastian, Sebastien Heymann, and Mathieu Jacomy · 2009
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Guangtao Wang, Rex Ying, Jing Huang, and Jure Leskovec · 2009
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Language models are open knowledge graphs
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
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Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio · 2014
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Diederik P Kingma and Jimmy Ba · 2014
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Locating relevant patient information in electronic health record data using representations of clinical concepts and database structures
Xuequn Pan and James J Cimino · 2014
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Real-time prediction of mortality, readmission, and length of stay using electronic health record data
Xiongcai Cai, Oscar Perez-Concha, Enrico Coiera, Fernando Martin-Sanchez, Richard Day, David Roffe, and Blanca Gallego · 2015
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Palmer L Elixhauser A, Steiner C · 2016
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Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark · 2016
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Deep patient: an unsupervised representation to predict the future of patients from the electronic health records
Riccardo Miotto, Li Li, Brian A Kidd, and Joel T Dudley · 2016
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Deepr: a convolutional net for medical records
Phuoc Nguyen, Truyen Tran, Nilmini Wickramasinghe, and Svetha Venkatesh · 2016
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Gram: graph-based attention model for healthcare representation learning
Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F Stewart, and Jimeng Sun · 2017
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Peipei Ping, Karol Watson, Jiawei Han, and Alex Bui · 2017
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Learning a health knowledge graph from electronic medical records
Maya Rotmensch, Yoni Halpern, Abdulhakim Tlimat, Steven Horng, and David Sontag · 2017
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Deep ehr: a survey of recent advances in deep learning techniques for electronic health record (ehr) analysis
Benjamin Shickel, Patrick James Tighe, Azra Bihorac, and Parisa Rashidi · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Mime: Multilevel medical embedding of electronic health records for predictive healthcare
Edward Choi, Cao Xiao, Walter Stewart, and Jimeng Sun · 2018
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Amelie Gyrard, Manas Gaur, Saeedeh Shekarpour, Krishnaprasad Thirunarayan, and Amit Sheth · 2018
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John Boaz Lee, Ryan Rossi, and Xiangnan Kong · 2018
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Kame: Knowledge-based attention model for diagnosis prediction in healthcare
Fenglong Ma, Quanzeng You, Houping Xiao, Radha Chitta, Jing Zhou, and Jing Gao · 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
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Gaan: Gated attention networks for learning on large and spatiotemporal graphs
Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, and Dit-Yan Yeung · 2018
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Towards controllable biases in language generation
Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng · 2020
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Gate: graph-attention augmented temporal neural network for medication recommendation
Chenhao Su, Sheng Gao, and Si Li · 2020
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Diagnostic prediction with sequence-of-sets representation learning for clinical events
Tianran Zhang, Muhao Chen, and Alex AT Bui · 2020
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Personalizing medication recommendation with a graph-based approach
Suman Bhoi, Mong Li Lee, Wynne Hsu, Hao Sen Andrew Fang, and Ngiap Chuan Tan · 2021
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Rethinking graph transformers with spectral attention
Devin Kreuzer, Dominique Beaini, Will Hamilton, Vincent Létourneau, and Prudencio Tossou · 2021
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Awais Ashfaq, Anita Sant’Anna, Markus Lingman, and Sławomir Nowaczyk · 2019
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Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population-based registry study
Mathias Carl Blom, Awais Ashfaq, Anita Sant’Anna, Philip D Anderson, and Markus Lingman · 2019
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Robustly extracting medical knowledge from ehrs: a case study of learning a health knowledge graph
Irene Y Chen, Monica Agrawal, Steven Horng, and David Sontag · 2019
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Electronic health record mortality prediction model for targeted palliative care among hospitalized medical patients: a pilot quasi-experimental study
Katherine R Courtright, Corey Chivers, Michael Becker, Susan H Regli, Linnea C Pepper, Michael E Draugelis, and Nina R O’Connor · 2019
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Fast graph representation learning with pytorch geometric
Matthias Fey and Jan Eric Lenssen · 2019
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Multitask learning and benchmarking with clinical time series data
Hrayr Harutyunyan, Hrant Khachatrian, David C Kale, Greg Ver Steeg, and Aram Galstyan · 2019
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Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2019
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Real-world integration of genomic data into the electronic health record: the pennchart genomics initiative
Kelsey S Lau-Min, Stephanie Byers Asher, Jessica Chen, Susan M Domchek, Michael Feldman, Steven Joffe, Jeffrey Landgraf, Virginia Speare, Lisa A Varughese, Sony Tuteja, et al · 2021
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Machine-learning-based hospital discharge predictions can support multidisciplinary rounds and decrease hospital length-of-stay
Scott Levin, Sean Barnes, Matthew Toerper, Arnaud Debraine, Anthony DeAngelo, Eric Hamrock, Jeremiah Hinson, Erik Hoyer, Trushar Dungarani, and Eric Howell · 2021
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Applying personal knowledge graphs to health
Sola Shirai, Oshani Seneviratne, and Deborah L McGuinness · 2021
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Ethical and social risks of harm from language models
Laura Weidinger, John Mellor, Maribeth Rauh, Conor Griffin, Jonathan Uesato, Po-Sen Huang, Myra Cheng, Mia Glaese, Borja Balle, Atoosa Kasirzadeh, et al · 2021
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Variationally regularized graph-based representation learning for electronic health records
Weicheng Zhu and Narges Razavian · 2021
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A review on language models as knowledge bases, 2022
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Knowledge is flat: A seq2seq generative framework for various knowledge graph completion
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Palm: Scaling language modeling with pathways
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Medml: Fusing medical knowledge and machine learning models for early pediatric covid-19 hospitalization and severity prediction
Junyi Gao, Chaoqi Yang, Joerg Heintz, Scott Barrows, Elise Albers, Mary Stapel, Sara Warfield, Adam Cross, Jimeng Sun, et al · 2022
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Global self-attention as a replacement for graph convolution
Md Shamim Hussain, Mohammed J. Zaki, and Dharmashankar Subramanian · 2022
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Graph representation learning in biomedicine and healthcare
Michelle M Li, Kexin Huang, and Marinka Zitnik · 2022
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A framework for adapting pre-trained language models to knowledge graph completion
Justin Lovelace and Carolyn Rose · 2022
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BioGPT: generative pre-trained transformer for biomedical text generation and mining
Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon, and Tie-Yan Liu · 2022
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Do pre-trained models benefit knowledge graph completion? a reliable evaluation and a reasonable approach
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Recipe for a general, powerful, scalable graph transformer
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Chen Chen, Yufei Wang, Aixin Sun, Bing Li, and Kwok-Yan Lam · 2023
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