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Generative, pre-trained transformers (GPTs, a.k.a.
Data Structures for Statistical Computing in Python
Wes McKinney · 2010
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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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Time series feature extraction on basis of scalable hypothesis tests (tsfresh – a python package)
Maximilian Christ, Nils Braun, Julius Neuffer, and Andreas W. Kempa-Liehr · 2018
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PyTorch Lightning, March 2019
William Falcon and The PyTorch Lightning team · 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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The “all of us” research program
AURP Investigators · 2019
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Experiment tracking with weights and biases, 2020
Lukas Biewald · 2020
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Neural temporal point processes for modelling electronic health records
Joseph Enguehard, Dan Busbridge, Adam Bozson, Claire Woodcock, and Nils Hammerla · 2020
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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
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Intensity-free learning of temporal point processes
Oleksandr Shchur, Marin Biloš, and Stephan Günnemann · 2020
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Democratizing EHR analyses with FIDDLE: a flexible data-driven preprocessing pipeline for structured clinical data
Shengpu Tang, Parmida Davarmanesh, Yanmeng Song, Danai Koutra, Michael W Sjoding, and Jenna Wiens · 2020
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Mimic-extract: A data extraction, preprocessing, and representation pipeline for mimic-iii
Shirly Wang, Matthew B. A. McDermott, Geeticka Chauhan, Marzyeh Ghassemi, Michael C. Hughes, and Tristan Naumann · 2020
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Transformer hawkes process
Simiao Zuo, Haoming Jiang, Zichong Li, Tuo Zhao, and Hongyuan Zha · 2020
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Revisiting deep learning models for tabular data
Yury Gorishniy, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko · 2021
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Clairvoyance: A pipeline toolkit for medical time series
Daniel Jarrett, Jinsung Yoon, Ioana Bica, Zhaozhi Qian, Ari Ercole, and Mihaela van der Schaar · 2021
Cited alongside, same era.
Deep contextual clinical prediction with reverse distillation
Rohan Kodialam, Rebecca Boiarsky, Justin Lim, Aditya Sai, Neil Dixit, and David Sontag · 2021
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Medgpt: Medical concept prediction from clinical narratives
Zeljko Kraljevic, Anthony Shek, Daniel Bean, Rebecca Bendayan, James Teo, and Richard Dobson · 2021
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Explainable health risk predictor with transformer-based medicare claim encoder
Chuhong Lahlou, Ancil Crayton, Caroline Trier, and Evan Willett · 2021
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duckdb/duckdb: 0.2.6 preview release "jamaicensis", May 2021
Mark, Hannes Mühleisen, Pedro Holanda, tiagokepe, Diego Gomes Tomé, Josh Wills, Richard Wesley, Kirill Müller, Till Döhmen, tania, Denis Hirn, simonasked, Azim Afroozeh, nantiamak, Patrick Schratz, Aris Koning, André Kohn, Chilarai, Gabor Szarnyas, Arjen P. de Vries, Sreeharsha Ramanavarapu, Andy Teucher, Dominik Moritz, Igor [hyperxor], Uwe L. Korn, travis leith, Aleksei Kashuba, Erwan Le Pennec, Y., and Jian Zhang · 2021
Kats, 3 2022
Xiaodong Jiang, Sudeep Srivastava, Sourav Chatterjee, Yang Yu, Jeffrey Handler, Peiyi Zhang, Rohan Bopardikar, Dawei Li, Yanjun Lin, Uttam Thakore, Michael Brundage, Ginger Holt, Caner Komurlu, Rakshita Nagalla, Zhichao Wang, Hechao Sun, Peng Gao, Wei Cheung, Jun Gao, Qi Wang, Marius Guerard, Morteza Kazemi, Yulin Chen, Chong Zhou, Sean Lee, Nikolay Laptev, Tihamér Levendovszky, Jake Taylor, Huijun Qian, Jian Zhang, Aida Shoydokova, Trisha Singh, Chengjun Zhu, Zeynep Baz, Christoph Bergmeir, Di Yu, Ahmet Koylan, Kun Jiang, Ploy Temiyasathit, and Emre Yurtbay · 2022
Later among the works it cites.
Hi-behrt: Hierarchical transformer-based model for accurate prediction of clinical events using multimodal longitudinal electronic health records
Yikuan Li, Mohammad Mamouei, Gholamreza Salimi-Khorshidi, Shishir Rao, Abdelaali Hassaine, Dexter Canoy, Thomas Lukasiewicz, and Kazem Rahimi · 2022
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Unsupervised pre-training of graph transformers on patient population graphs
Chantal Pellegrini, Nassir Navab, and Anees Kazi · 2022
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Targeted-behrt: Deep learning for observational causal inference on longitudinal electronic health records
Shishir Rao, Mohammad Mamouei, Gholamreza Salimi-Khorshidi, Yikuan Li, Rema Ramakrishnan, Abdelaali Hassaine, Dexter Canoy, and Kazem Rahimi · 2022
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Cited alongside, same era.
A comprehensive ehr timeseries pre-training benchmark
Matthew McDermott, Bret Nestor, Evan Kim, Wancong Zhang, Anna Goldenberg, Peter Szolovits, and Marzyeh Ghassemi · 2021
Cited alongside, same era.
Structure inducing pre-training
Matthew McDermott, Brendan Yap, Peter Szolovits, and Marinka Zitnik · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Med-bert: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction
Laila Rasmy, Yang Xiang, Ziqian Xie, Cui Tao, and Degui Zhi · 2021
Cited alongside, same era.
Rapt: Pre-training of time-aware transformer for learning robust healthcare representation
Houxing Ren, Jingyuan Wang, Wayne Xin Zhao, and Ning Wu · 2021
Cited alongside, same era.
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, and Rob Fergus · 2021
Cited alongside, same era.
Generalized and transferable patient language representation for phenotyping with limited data
Yuqi Si, Elmer V Bernstam, and Kirk Roberts · 2021
Cited alongside, same era.
Generative adversarial networks enhanced pre-training for insufficient electronic health records modeling
Houxing Ren, Jingyuan Wang, and Wayne Xin Zhao · 2022
Later among the works it cites.
Metacare++: Meta-learning with hierarchical subtyping for cold-start diagnosis prediction in healthcare data
Yanchao Tan, Carl Yang, Xiangyu Wei, Chaochao Chen, Weiming Liu, Longfei Li, Jun Zhou, and Xiaolin Zheng · 2022
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Point-bert: Pre-training 3d point cloud transformers with masked point modeling
Xumin Yu, Lulu Tang, Yongming Rao, Tiejun Huang, Jie Zhou, and Jiwen Lu · 2022
Later among the works it cites.
Mimic-iv, a freely accessible electronic health record dataset
Alistair EW Johnson, Lucas Bulgarelli, Lu Shen, Alvin Gayles, Ayad Shammout, Steven Horng, Tom J Pollard, Benjamin Moody, Brian Gow, Li-wei H Lehman, et al · 2023
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Clinical decision transformer: Intended treatment recommendation through goal prompting
Seunghyun Lee, Da Young Lee, Sujeong Im, Nan Hee Kim, and Sung-Min Park · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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pandas-dev/pandas: Pandas, April 2023
The pandas development team · 2023
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Temporai: Facilitating machine learning innovation in time domain tasks for medicine
Evgeny S Saveliev and Mihaela van der Schaar · 2023
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pola-rs/polars: Python polars 0.16.12, March 2023
Ritchie Vink, Stijn de Gooijer, Alexander Beedie, J van Zundert, Gert Hulselmans, Cory Grinstead, Marco Edward Gorelli, Matteo Santamaria, Daniël Heres, ibENPC, Jorge Leitao, Marc van Heerden, Colin Jermain, Ryan Russell, Chris Pryer, Adrián Gallego Castellanos, Jeremy Goh, Moritz Wilksch, illumination k, Max Conradt, Liam Brannigan, Joshua Peek, Yu Ri Tan, elbaro, Nicolas Stalder, Søren Havelund Welling, Adam Gregory, paq, and Jakob Keller · 2023
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