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Clinical trials are indispensable in developing new treatments, but they face obstacles in patient recruitment and retention, hindering the enrollment of necessary participants.
Long short-term memory
Hochreiter S, Schmidhuber J · 1997
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Recruitment to randomised trials: strategies for trial enrolment and participation study. The STEPS study
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EliXR: an approach to eligibility criteria extraction and representation
Weng C, Wu X, Luo Z, Boland MR, Theodoratos D, Johnson SB · 2011
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Fairness through awareness
Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R · 2012
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Memory networks
Weston J, Chopra S, Bordes A · 2014
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Convolutional neural network architectures for matching natural language sentences
Hu B, Lu Z, Li H, Chen Q · 2014
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Highway networks
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Unsupervised entity and relation extraction from clinical records in Italian
Alicante A, Corazza A, Isgro F, Silvestri S · 2016
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Equality of opportunity in supervised learning
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MIMIC-III, a freely accessible critical care database
Johnson AE, Pollard TJ, Shen L, Lehman LwH, Feng M, Ghassemi M, et al · 2016
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Sharing and reuse of individual participant data from clinical trials: principles and recommendations
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EliIE: An open-source information extraction system for clinical trial eligibility criteria
Kang T, Zhang S, Tang Y, Hruby GW, Rusanov A, Elhadad N, et al · 2017
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Counterfactual fairness
Kusner MJ, Loftus J, Russell C, Silva R · 2017
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Mitigating unwanted biases with adversarial learning
Zhang BH, Lemoine B, Mitchell M · 2018
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Learning adversarially fair and transferable representations
Madras D, Creager E, Pitassi T, Zemel R · 2018
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Fairness in machine learning: Lessons from political philosophy
Binns R · 2018
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Fairness definitions explained
Verma S, Rubin J · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin J, Chang MW, Lee K, Toutanova K · 2018
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Roberta: A robustly optimized bert pretraining approach
Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, et al · 2019
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Criteria2Query: a natural language interface to clinical databases for cohort definition
Yuan C, Ryan PB, Ta C, Guo Y, Li Z, Hardin J, et al · 2019
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Publicly available clinical BERT embeddings
Alsentzer E, Murphy JR, Boag W, Weng WH, Jin D, Naumann T, et al · 2019
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DeepEnroll: patient-trial matching with deep embedding and entailment prediction
Zhang X, Xiao C, Glass LM, Sun J · 2020
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COMPOSE: cross-modal pseudo-siamese network for patient trial matching
Gao J, Xiao C, Glass LM, Sun J · 2020
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Wasserstein fair classification
Jiang R, Pacchiano A, Stepleton T, Jiang H, Chiappa S · 2020
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You Q, Zhang Z, Luo J · 2018
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Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer Z, Powers B, Vogeli C, Mullainathan S · 2019
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Putting fairness principles into practice: Challenges, metrics, and improvements
Beutel A, Chen J, Doshi T, Qian H, Woodruff A, Luu C, et al · 2019
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Fairness in recommendation ranking through pairwise comparisons
Beutel A, Chen J, Doshi T, Qian H, Wei L, Wu Y, et al · 2019
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50 years of test (un) fairness: Lessons for machine learning
Hutchinson B, Mitchell M · 2019
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Learning from failure: De-biasing classifier from biased classifier
Nam J, Cha H, Ahn S, Lee J, Shin J · 2020
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Reducing sentiment polarity for demographic attributes in word embeddings using adversarial learning
Sweeney C, Najafian M · 2020
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Generative adversarial networks
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al · 2020
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A survey on bias and fairness in machine learning
Mehrabi N, Morstatter F, Saxena N, Lerman K, Galstyan A · 2021
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Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning
Ding S, Tang R, Zha D, Zou N, Zhang K, Jiang X, et al · 2023
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