Fetching the paper…
Reading the bibliography…
While the general machine learning (ML) community has benefited from public datasets, tasks, and models, the progress of ML in healthcare has been hampered by a lack of such shared assets.
The need for open source software in machine learning
Soren Sonnenburg, Mikio L Braun, Cheng Soon Ong, Samy Bengio, Leon Bottou, Geoffrey Holmes, Yann LeCunn, Klaus-Robert Muller, Fernando Pereira, Carl Edward Rasmussen, et al · 2007
Earlier work this paper cites.
The bigchaos solution to the netflix grand prize
Andreas Töscher, Michael Jahrer, and Robert M Bell · 2009
Earlier work this paper cites.
Data reuse and the open data citation advantage
Heather A Piwowar and Todd J Vision · 2013
Earlier work this paper cites.
Data resource profile: clinical practice research datalink (cprd)
Emily Herrett, Arlene M Gallagher, Krishnan Bhaskaran, Harriet Forbes, Rohini Mathur, Tjeerd Van Staa, and Liam Smeeth · 2015
Earlier work this paper cites.
Observational health data sciences and informatics (ohdsi): opportunities for observational researchers
George Hripcsak, Jon D Duke, Nigam H Shah, Christian G Reich, Vojtech Huser, Martijn J Schuemie, Marc A Suchard, Rae Woong Park, Ian Chi Kei Wong, Peter R Rijnbeek, et al · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
Earlier work this paper cites.
Mimic-iii, a freely accessible critical care database
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
Earlier work this paper cites.
Gram: graph-based attention model for healthcare representation learning
Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F Stewart, and Jimeng Sun · 2017
Earlier work this paper cites.
Lightgbm: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
Earlier work this paper cites.
The uk biobank resource with deep phenotyping and genomic data
Clare Bycroft, Colin Freeman, Desislava Petkova, Gavin Band, Lloyd T Elliott, Kevin Sharp, Allan Motyer, Damjan Vukcevic, Olivier Delaneau, Jared O’Connell, et al · 2018
Earlier work this paper cites.
Potential biases in machine learning algorithms using electronic health record data
Milena A Gianfrancesco, Suzanne Tamang, Jinoos Yazdany, and Gabriela Schmajuk · 2018
Earlier work this paper cites.
The eicu collaborative research database, a freely available multi-center database for critical care research
Tom J Pollard, Alistair EW Johnson, Jesse D Raffa, Leo A Celi, Roger G Mark, and Omar Badawi · 2018
Earlier work this paper cites.
Benchmarking deep learning models on large healthcare datasets
Sanjay Purushotham, Chuizheng Meng, Zhengping Che, and Yan Liu · 2018
Earlier work this paper cites.
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, et al · 2018
Earlier work this paper cites.
Multitask learning and benchmarking with clinical time series data
Hrayr Harutyunyan, Hrant Khachatrian, David C Kale, Greg Ver Steeg, and Aram Galstyan · 2019
Earlier work this paper cites.
Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, et al · 2019
Earlier work this paper cites.
A new paradigm for accelerating clinical data science at stanford medicine
Somalee Datta, Jose Posada, Garrick Olson, Wencheng Li, Ciaran O’Reilly, Deepa Balraj, Joseph Mesterhazy, Joseph Pallas, Priyamvada Desai, and Nigam Shah · 2020
Earlier work this paper cites.
Hirid, a high time-resolution icu dataset (version 1.0). physionet. 2020
M Faltys, M Zimmermann, X Lyu, M Hüser, S Hyland, G Rätsch, and T Merz · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Benchmarking machine learning models on multi-centre eicu critical care dataset
Seyedmostafa Sheikhalishahi, Vevake Balaraman, and Venet Osmani · 2020
Cited alongside, same era.
Deep learning for electronic health records: A comparative review of multiple deep neural architectures
Jose Roberto Ayala Solares, Francesca Elisa Diletta Raimondi, Yajie Zhu, Fatemeh Rahimian, Dexter Canoy, Jenny Tran, Ana Catarina Pinho Gomes, Amir H Payberah, Mariagrazia Zottoli, Milad Nazarzadeh, et al · 2020
Cited alongside, same era.
Clinical text data in machine learning: systematic review
Irena Spasic, Goran Nenadic, et al · 2020
Cited alongside, same era.
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
Later among the works it cites.
Roformer: Enhanced transformer with rotary position embedding
Jianlin Su, Yu Lu, Shengfeng Pan, Ahmed Murtadha, Bo Wen, and Yunfeng Liu · 2021
Later among the works it cites.
Sharing icu patient data responsibly under the society of critical care medicine/european society of intensive care medicine joint data science collaboration: the amsterdam university medical centers database (amsterdamumcdb) example
Patrick J Thoral, Jan M Peppink, Ronald H Driessen, Eric JG Sijbrands, Erwin JO Kompanje, Lewis Kaplan, Heatherlee Bailey, Jozef Kesecioglu, Maurizio Cecconi, Matthew Churpek, et al · 2021
Later among the works it cites.
Hirid-icu-benchmark–a comprehensive machine learning benchmark on high-resolution icu data
Hugo Yèche, Rita Kuznetsova, Marc Zimmermann, Matthias Hüser, Xinrui Lyu, Martin Faltys, and Gunnar Rätsch · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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
Cited alongside, same era.
Mimic-extract: A data extraction, preprocessing, and representation pipeline for mimic-iii
Shirly Wang, Matthew BA McDermott, Geeticka Chauhan, Marzyeh Ghassemi, Michael C Hughes, and Tristan Naumann · 2020
Cited alongside, same era.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ B. Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri S. Chatterji, Annie S. Chen, Kathleen Creel, Jared Quincy Davis, Dorottya Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah D. Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark S. Krass, Ranjay Krishna, Rohith Kuditipudi, and et al · 2021
Cited alongside, same era.
Deep learning for chest x-ray analysis: A survey
Erdi Çallı, Ecem Sogancioglu, Bram van Ginneken, Kicky G van Leeuwen, and Keelin Murphy · 2021
Cited alongside, same era.
Unifying heterogenous electronic health records systems via text-based code embedding
Kyunghoon Hur, Jiyoung Lee, Jungwoo Oh, Wesley Price, Young-Hak Kim, and Edward Choi · 2021
Cited alongside, same era.
Radgraph: Extracting clinical entities and relations from radiology reports
Saahil Jain, Ashwin Agrawal, Adriel Saporta, Steven QH Truong, Du Nguyen Duong, Tan Bui, Pierre Chambon, Yuhao Zhang, Matthew P Lungren, Andrew Y Ng, et al · 2021
Cited alongside, same era.
Cop-e-cat: cleaning and organization pipeline for ehr computational and analytic tasks
Aishwarya Mandyam, Elizabeth C Yoo, Jeff Soules, Krzysztof Laudanski, and Barbara E Engelhardt · 2021
Cited alongside, same era.
An extensive data processing pipeline for mimic-iv
Mehak Gupta, Brennan Gallamoza, Nicolas Cutrona, Pranjal Dhakal, Raphael Poulain, and Rahmatollah Beheshti · 2022
Later among the works it cites.
How open source machine learning software shapes ai
Max Langenkamp and Daniel N Yue · 2022
Later among the works it cites.
Anna Munoz-Farre, Harry Rose, and Sera Aylin Cakiroglu · 2022
Later among the works it cites.
Few-shot learning with semi-supervised transformers for electronic health records
Raphael Poulain, Mehak Gupta, and Rahmatollah Beheshti · 2022
Later among the works it cites.
Compute trends across three eras of machine learning
Jaime Sevilla, Lennart Heim, Anson Ho, Tamay Besiroglu, Marius Hobbhahn, and Pablo Villalobos · 2022
Later among the works it cites.
Benchmarking emergency department prediction models with machine learning and public electronic health records
Feng Xie, Jun Zhou, Jin Wee Lee, Mingrui Tan, Siqi Li, Logasan S/O Rajnthern, Marcel Lucas Chee, Bibhas Chakraborty, An-Kwok Ian Wong, Alon Dagan, et al · 2022
Later among the works it cites.
Ehr foundation models improve robustness in the presence of temporal distribution shift
Lin Lawrence Guo, Ethan Steinberg, Scott Lanyon Fleming, Jose Posada, Joshua Lemmon, Stephen R Pfohl, Nigam Shah, Jason Fries, and Lillian Sung · 2023
Closest in time.
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
Closest in time.
Foundation models for generalist medical artificial intelligence
Michael Moor, Oishi Banerjee, Zahra Shakeri Hossein Abad, Harlan M Krumholz, Jure Leskovec, Eric J Topol, and Pranav Rajpurkar · 2023
Closest in time.
Large ai models in health informatics: Applications, challenges, and the future
Jianing Qiu, Lin Li, Jiankai Sun, Jiachuan Peng, Peilun Shi, Ruiyang Zhang, Yinzhao Dong, Kyle Lam, Frank P-W Lo, Bo Xiao, et al · 2023
Closest in time.
The reproducibility issues that haunt health-care ai
Emily Sohn · 2023
Closest in time.
Yet another icu benchmark: A flexible multi-center framework for clinical ml
Robin van de Water, Hendrik Schmidt, Paul Elbers, Patrick Thoral, Bert Arnrich, and Patrick Rockenschaub · 2023
Closest in time.
Michael Wornow, Yizhe Xu, Rahul Thapa, Birju Patel, Ethan Steinberg, Scott Fleming, Michael A Pfeffer, Jason Fries, and Nigam H Shah · 2023
Closest in time.