Fetching the paper…
Reading the bibliography…
Medical applications of machine learning (ML) have experienced a surge in popularity in recent years.
Benchmarking machine learning models on multi-centre eICU critical care dataset
Seyedmostafa Sheikhalishahi, Vevake Balaraman, and Venet Osmani · 1932
Earlier work this paper cites.
Predicting Inpatient Acute Kidney Injury over Different Time Horizons: How Early and Accurate?
Peng Cheng, Lemuel R. Waitman, Yong Hu, and Mei Liu · 1942
Earlier work this paper cites.
Long Short-Term Memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
Earlier work this paper cites.
2001 SCCM/ESICM/ACCP/ATS/SIS International Sepsis Definitions Conference
Mitchell M. Levy, Mitchell P. Fink, John C. Marshall, Edward Abraham, Derek Angus, Deborah Cook, Jonathan Cohen, Steven M. Opal, Jean-Louis Vincent, Graham Ramsay, and International Sepsis Definitions Conference · 2003
Earlier work this paper cites.
SLURM: Simple Linux Utility for Resource Management
Andy B. Yoo, Morris A. Jette, and Mark Grondona · 2003
Earlier work this paper cites.
Building Classifiers with Independency Constraints
Toon Calders, Faisal Kamiran, and Mykola Pechenizkiy · 2009
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay · 2011
Earlier work this paper cites.
Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury Work Group: KDIGO clinical practice guideline for acute kidney injury
KDIGO · 2012
Earlier work this paper cites.
Predicting In-Hospital Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge 2012
Ikaro Silva, George Moody, Daniel J. Scott, Leo A. Celi, and Roger G. Mark · 2012
Earlier work this paper cites.
Practical Bayesian Optimization of Machine Learning Algorithms, August 2012
Jasper Snoek, Hugo Larochelle, and Ryan P. Adams · 2012
Earlier work this paper cites.
On the Properties of Neural Machine Translation: Encoder-Decoder Approaches, October 2014
Kyunghyun Cho, Bart van Merrienboer, Dzmitry Bahdanau, and Yoshua Bengio · 2014
Earlier work this paper cites.
Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis
Adler Perotte, Rajesh Ranganath, Jamie S Hirsch, David Blei, and Noémie Elhadad · 2015
Earlier work this paper cites.
Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): A population-based study
Romain Pirracchio, Maya L Petersen, Marco Carone, Matthieu Resche Rigon, Sylvie Chevret, and Mark J van der Laan · 2015
Earlier work this paper cites.
U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
TensorFlow: A system for large-scale machine learning
Martin Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G. Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, and Xiaoqiang Zheng · 2016
Earlier work this paper cites.
A computational approach to early sepsis detection
Jacob S. Calvert, Daniel A. Price, Uli K. Chettipally, Christopher W. Barton, Mitchell D. Feldman, Jana L. Hoffman, Melissa Jay, and Ritankar Das · 2016
Earlier work this paper cites.
Predicting Disease Progression with a Model for Multivariate Longitudinal Clinical Data
Joseph Futoma, Mark Sendak, Blake Cameron, and Katherine Heller · 2016
Earlier work this paper cites.
Equality of Opportunity in Supervised Learning
Moritz Hardt, Eric Price, Eric Price, and Nati Srebro · 2016
Earlier work this paper cites.
MIMIC-III, a freely accessible critical care database
Alistair E.W. 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.
Assessment of Clinical Criteria for Sepsis: For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)
Christopher W. Seymour, Vincent X. Liu, Theodore J. Iwashyna, Frank M. Brunkhorst, Thomas D. Rea, André Scherag, Gordon Rubenfeld, Jeremy M. Kahn, Manu Shankar-Hari, Mervyn Singer, Clifford S. Deutschman, Gabriel J. Escobar, and Derek C. Angus · 2016
Earlier work this paper cites.
The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)
Mervyn Singer, Clifford S. Deutschman, Christopher Warren Seymour, Manu Shankar-Hari, Djillali Annane, Michael Bauer, Rinaldo Bellomo, Gordon R. Bernard, Jean-Daniel Chiche, Craig M. Coopersmith, Richard S. Hotchkiss, Mitchell M. Levy, John C. Marshall, Greg S. Martin, Steven M. Opal, Gordon D. Rubenfeld, Tom van der Poll, Jean-Louis Vincent, and Derek C. Angus · 2016
Earlier work this paper cites.
Apache Parquet
Deepak Vohra · 2016
Earlier work this paper cites.
Reproducibility in critical care: A mortality prediction case study
Alistair E. W. Johnson, Tom J. Pollard, and Roger G. Mark · 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.
Mortality Prediction of ICU patients using Machine Leaning: A survey
Alok Sharma, Anupam Shukla, Ritu Tiwari, and Apoorva Mishra · 2017
Earlier work this paper cites.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling, April 2018
Shaojie Bai, J. Zico Kolter, and Vladlen Koltun · 2018
Earlier work this paper cites.
Gin-config
Dan Holtmann-Rice, Sergio Guadarrama, and Nathan Silberman · 2018
Earlier work this paper cites.
The Development of a Machine Learning Inpatient Acute Kidney Injury Prediction Model*:
Jay L. Koyner, Kyle A. Carey, Dana P. Edelson, and Matthew M. Churpek · 2018
Cited alongside, same era.
The eICU Collaborative Research Database, a freely available multi-center database for critical care research
Tom J. Pollard, Alistair E. W. Johnson, Jesse D. Raffa, Leo A. Celi, Roger G. Mark, and Omar Badawi · 2018
Cited alongside, same era.
Benchmarking deep learning models on large healthcare datasets
Sanjay Purushotham, Chuizheng Meng, Zhengping Che, and Yan Liu · 2018
Cited alongside, same era.
Key challenges for delivering clinical impact with artificial intelligence
Christopher J. Kelly, Alan Karthikesalingam, Mustafa Suleyman, Greg Corrado, and Dominic King · 2019
Cited alongside, same era.
Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping
Michael Moor, Max Horn, Bastian Rieck, Damian Roqueiro, and Karsten Borgwardt · 2019
Cited alongside, same era.
Machine Learning–Based Early Warning Systems for Clinical Deterioration: Systematic Scoping Review
Sankavi Muralitharan, Walter Nelson, Shuang Di, Michael McGillion, Pj Devereaux, Neil Grant Barr, and Jeremy Petch · 2021
Later among the works it cites.
Application of Machine Learning in Intensive Care Unit (ICU) Settings Using MIMIC Dataset: Systematic Review
Mahanazuddin Syed, Shorabuddin Syed, Kevin Sexton, Hafsa Bareen Syeda, Maryam Garza, Meredith Zozus, Farhanuddin Syed, Salma Begum, Abdullah Usama Syed, Joseph Sanford, and Fred Prior · 2021
Later among the works it cites.
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation
Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon · 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 J. G. Sijbrands, Erwin J. O. Kompanje, Lewis Kaplan, Heatherlee Bailey, Jozef Kesecioglu, Maurizio Cecconi, Matthew Churpek, Gilles Clermont, Mihaela van der Schaar, Ari Ercole, Armand R. J. Girbes, and Paul W. G. Elbers · 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…
A Self-Correcting Deep Learning Approach to Predict Acute Conditions in Critical Care
Ziyuan Pan, Hao Du, Kee Yuan Ngiam, Fei Wang, Ping Shum, and Mengling Feng · 2019
Cited alongside, same era.
Early Prediction of Sepsis from Clinical Data: The PhysioNet/Computing in Cardiology Challenge 2019
Matthew A Reyna, Chris Josef, Salman Seyedi, Russell Jeter, Supreeth P Shashikumar, M Brandon Westover, Ashish Sharma, Shamim Nemati, and Gari D Clifford · 2019
Cited alongside, same era.
Use of machine learning to analyse routinely collected intensive care unit data: A systematic review
Duncan Shillan, Jonathan A. C. Sterne, Alan Champneys, and Ben Gibbison · 2019
Cited alongside, same era.
A clinically applicable approach to continuous prediction of future acute kidney injury
Nenad Tomašev, Xavier Glorot, Jack W. Rae, Michal Zielinski, Harry Askham, Andre Saraiva, Anne Mottram, Clemens Meyer, Suman Ravuri, Ivan Protsyuk, Alistair Connell, Cían O. Hughes, Alan Karthikesalingam, Julien Cornebise, Hugh Montgomery, Geraint Rees, Chris Laing, Clifton R. Baker, Kelly Peterson, Ruth Reeves, Demis Hassabis, Dominic King, Mustafa Suleyman, Trevor Back, Christopher Nielson, Joseph R. Ledsam, and Shakir Mohamed · 2019
Cited alongside, same era.
Experiment tracking with weights and biases, 2020
Lukas Biewald · 2020
Cited alongside, same era.
A Comparison of Sepsis-2 (Systemic Inflammatory Response Syndrome Based) to Sepsis-3 (Sequential Organ Failure Assessment Based) Definitions—A Multicenter Retrospective Study*
Milo Engoren, Troy Seelhammer, Robert E. Freundlich, Michael D. Maile, Matthew J. G. Sigakis, and Thomas A. Schwann · 2020
Cited alongside, same era.
High-level library to help with training neural networks in PyTorch, 2020
V. Fomin, J. Anmol, S. Desroziers, J. Kriss, and A. Tejani · 2020
Cited alongside, same era.
Tell me something interesting: Clinical utility of machine learning prediction models in the ICU
Bar Eini-Porat, Ofra Amir, Danny Eytan, and Uri Shalit · 2022
Later among the works it cites.
Why do tree-based models still outperform deep learning on typical tabular data?
Leo Grinsztajn, Edouard Oyallon, and Gael Varoquaux · 2022
Later among the works it cites.
An Extensive Data Processing Pipeline for MIMIC-IV, September 2022
Mehak Gupta, Brennan Gallamoza, Nicolas Cutrona, Pranjal Dhakal, Raphael Poulain, and Rahmatollah Beheshti · 2022
Later among the works it cites.
HyperImpute: Generalized Iterative Imputation with Automatic Model Selection, June 2022
Daniel Jarrett, Bogdan Cebere, Tennison Liu, Alicia Curth, and Mihaela van der Schaar · 2022
Later among the works it cites.
Machine Learning–Based Short-Term Mortality Prediction Models for Patients With Cancer Using Electronic Health Record Data: Systematic Review and Critical Appraisal
Sheng-Chieh Lu, Cai Xu, Chandler H. Nguyen, Yimin Geng, André Pfob, and Chris Sidey-Gibbons · 2022
Later among the works it cites.
TorchMetrics - measuring reproducibility in PyTorch, February 2022
Nicki Skafte Detlefsen, Jiri Borovec, Justus Schock, Ananya Harsh, Teddy Koker, Luca Di Liello, Daniel Stancl, Changsheng Quan, Maxim Grechkin, and William Falcon · 2022
Later among the works it cites.
Developing a supervised machine learning model for predicting perioperative acute kidney injury in arthroplasty patients
Okke Nikkinen, Timo Kolehmainen, Toni Aaltonen, Elias Jämsä, Seppo Alahuhta, and Merja Vakkala · 2022
Later among the works it cites.
Integrating Physiological Time Series and Clinical Notes with Transformer for Early Prediction of Sepsis, March 2022
Yuqing Wang, Yun Zhao, Rachael Callcut, and Linda Petzold · 2022
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 · 2022
Later among the works it cites.
Ricu: R’s interface to intensive care data
Nicolas Bennett, Drago Plecko, Ida-Fong Ukor, Nicolai Meinshausen, and Peter Bühlmann · 2023
Closest in time.
PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series, May 2023
Wenjie Du · 2023
Closest in time.
PyTorch lightning
William Falcon and The PyTorch Lightning team · 2023
Closest in time.
Introducing the BlendedICU dataset, the first harmonized, international intensive care dataset
Matthieu Oliver, Jérôme Allyn, Rémi Carencotte, Nicolas Allou, and Cyril Ferdynus · 2023
Closest in time.
The Salzburg Intensive Care database (SICdb): An openly available critical care dataset
Niklas Rodemund, Bernhard Wernly, Christian Jung, Crispiana Cozowicz, and Andreas Koköfer · 2023
Closest in time.
The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges
Tabinda Sarwar, Sattar Seifollahi, Jeffrey Chan, Xiuzhen Zhang, Vural Aksakalli, Irene Hudson, Karin Verspoor, and Lawrence Cavedon · 2023
Closest in time.
TemporAI: Facilitating Machine Learning Innovation in Time Domain Tasks for Medicine, January 2023
Evgeny S. Saveliev and Mihaela van der Schaar · 2023
Closest in time.
Closing Gaps: An Imputation Analysis of ICU Vital Signs
Robin van de Water and Bert Arnrich · 2023
Closest in time.
PyHealth: A Deep Learning Toolkit for Healthcare Applications
Chaoqi Yang, Zhenbang Wu, Patrick Jiang, Zhen Lin, Junyi Gao, Benjamin P. Danek, and Jimeng Sun · 2023
Closest in time.
Continuous and automatic mortality risk prediction using vital signs in the intensive care unit: A hybrid neural network approach
Stephanie Baker, Wei Xiang, and Ian Atkinson · 2045
Closest in time.
Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk
Sebastiano Barbieri, James Kemp, Oscar Perez-Concha, Sradha Kotwal, Martin Gallagher, Angus Ritchie, and Louisa Jorm · 2045
Closest in time.
Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review
Goran Medic, Melodi Kosaner Kließ, Louis Atallah, Jochen Weichert, Saswat Panda, Maarten Postma, and Amer El-Kerdi · 2046
Closest in time.
Multitask learning and benchmarking with clinical time series data
Hrayr Harutyunyan, Hrant Khachatrian, David C. Kale, Greg Ver Steeg, and Aram Galstyan · 2052
Closest in time.
Establishment of a Chinese critical care database from electronic healthcare records in a tertiary care medical center
Senjun Jin, Lin Chen, Kun Chen, Chaozhou Hu, Sheng’an Hu, and Zhongheng Zhang · 2052
Closest in time.
MIMIC-IV, a freely accessible electronic health record dataset
Alistair E. W. Johnson, Lucas Bulgarelli, Lu Shen, Alvin Gayles, Ayad Shammout, Steven Horng, Tom J. Pollard, Benjamin Moody, Brian Gow, Li-wei H. Lehman, Leo A. Celi, and Roger G. Mark · 2052
Closest in time.