Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Leo Anthony Celi

Leo Anthony Celi

Predicting Hyperkalemia in the ICU and Evaluation of Generalizability and Interpretability


Jan 27, 2021
Gloria Hyunjung Kwak, Christina Chen, Lowell Ling, Erina Ghosh, Leo Anthony Celi, Pan Hui

Figure 1 for Predicting Hyperkalemia in the ICU and Evaluation of Generalizability and Interpretability
Figure 2 for Predicting Hyperkalemia in the ICU and Evaluation of Generalizability and Interpretability
Figure 3 for Predicting Hyperkalemia in the ICU and Evaluation of Generalizability and Interpretability
Figure 4 for Predicting Hyperkalemia in the ICU and Evaluation of Generalizability and Interpretability
* AAAI 2021 Workshop: Trustworthy AI for Healthcare 
 * 6 pages, 3 figures, 3 tables 
  Access Paper or Ask Questions

Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment


Jan 09, 2021
Kristine Zhang, Yuanheng Wang, Jianzhun Du, Brian Chu, Leo Anthony Celi, Ryan Kindle, Finale Doshi-Velez

Figure 1 for Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment
Figure 2 for Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment
Figure 3 for Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment
Figure 4 for Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment
* NeurIPS 2020 Machine Learning for Health (ML4H) Workshop 
  Access Paper or Ask Questions

Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understanding


Jan 06, 2021
Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen, Walter Chang, Leo Anthony Celi

Figure 1 for Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understanding
Figure 2 for Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understanding
Figure 3 for Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understanding
Figure 4 for Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understanding
* Task overview for Acronym Identification and Acronym Disambiguation at Scientific Document Understanding workshop at AAAI 2021 
  Access Paper or Ask Questions

Acronym Identification and Disambiguation shared tasksfor Scientific Document Understanding


Dec 22, 2020
Amir Pouran Ben Veyseh, Franck Dernoncourt, Thien Huu Nguyen, Walter Chang, Leo Anthony Celi

Figure 1 for Acronym Identification and Disambiguation shared tasksfor Scientific Document Understanding
Figure 2 for Acronym Identification and Disambiguation shared tasksfor Scientific Document Understanding
Figure 3 for Acronym Identification and Disambiguation shared tasksfor Scientific Document Understanding
Figure 4 for Acronym Identification and Disambiguation shared tasksfor Scientific Document Understanding
* Task overview for Acronym Identification and Acronym Disambiguation at Scientific Document Understanding workshop at AAAI 2020 
  Access Paper or Ask Questions

Real-time Prediction of COVID-19 related Mortality using Electronic Health Records


Aug 31, 2020
Patrick Schwab, Arash Mehrjou, Sonali Parbhoo, Leo Anthony Celi, Jürgen Hetzel, Markus Hofer, Bernhard Schölkopf, Stefan Bauer

Figure 1 for Real-time Prediction of COVID-19 related Mortality using Electronic Health Records
Figure 2 for Real-time Prediction of COVID-19 related Mortality using Electronic Health Records
Figure 3 for Real-time Prediction of COVID-19 related Mortality using Electronic Health Records
Figure 4 for Real-time Prediction of COVID-19 related Mortality using Electronic Health Records

  Access Paper or Ask Questions

A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients


Mar 06, 2020
Edward T. Moseley, Joy T. Wu, Jonathan Welt, John Foote, Patrick D. Tyler, David W. Grant, Eric T. Carlson, Sebastian Gehrmann, Franck Dernoncourt, Leo Anthony Celi

Figure 1 for A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients
Figure 2 for A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients
Figure 3 for A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients
Figure 4 for A Corpus for Detecting High-Context Medical Conditions in Intensive Care Patient Notes Focusing on Frequently Readmitted Patients
* Accepted at LREC 2020 
  Access Paper or Ask Questions

Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions


Feb 14, 2020
Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Anthony Celi, Emma Brunskill, Finale Doshi-Velez

Figure 1 for Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
Figure 2 for Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
Figure 3 for Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
Figure 4 for Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
* Change: Correction of typo in meta-data author names 
  Access Paper or Ask Questions

Interpretable Machine Learning Model for Early Prediction of Mortality in Elderly Patients with Multiple Organ Dysfunction Syndrome (MODS): a Multicenter Retrospective Study and Cross Validation


Jan 28, 2020
Xiaoli Liu, Pan Hu, Zhi Mao, Po-Chih Kuo, Peiyao Li, Chao Liu, Jie Hu, Deyu Li, Desen Cao, Roger G. Mark, Leo Anthony Celi, Zhengbo Zhang, Feihu Zhou

Figure 1 for Interpretable Machine Learning Model for Early Prediction of Mortality in Elderly Patients with Multiple Organ Dysfunction Syndrome (MODS): a Multicenter Retrospective Study and Cross Validation
Figure 2 for Interpretable Machine Learning Model for Early Prediction of Mortality in Elderly Patients with Multiple Organ Dysfunction Syndrome (MODS): a Multicenter Retrospective Study and Cross Validation
Figure 3 for Interpretable Machine Learning Model for Early Prediction of Mortality in Elderly Patients with Multiple Organ Dysfunction Syndrome (MODS): a Multicenter Retrospective Study and Cross Validation
Figure 4 for Interpretable Machine Learning Model for Early Prediction of Mortality in Elderly Patients with Multiple Organ Dysfunction Syndrome (MODS): a Multicenter Retrospective Study and Cross Validation
* 33 pages, 14 figures, 14 tables, article, Co-author: Xiaoli Liu and Pan Hu, Co-correspondence: Feihu Zhou and Zhengbo Zhang 
  Access Paper or Ask Questions

Ensembles of Locally Independent Prediction Models


Nov 27, 2019
Andrew Slavin Ross, Weiwei Pan, Leo Anthony Celi, Finale Doshi-Velez

Figure 1 for Ensembles of Locally Independent Prediction Models
Figure 2 for Ensembles of Locally Independent Prediction Models
Figure 3 for Ensembles of Locally Independent Prediction Models
Figure 4 for Ensembles of Locally Independent Prediction Models
* This is an expansion of arXiv:1806.08716 with different applications and focus, accepted to AAAI 2020 
  Access Paper or Ask Questions

Withholding aggressive treatments may not accelerate time to death among dying ICU patients


Aug 04, 2018
Daniele Ramazzotti, Peter Clardy, Leo Anthony Celi, David J. Stone, Robert S. Rudin

Figure 1 for Withholding aggressive treatments may not accelerate time to death among dying ICU patients
Figure 2 for Withholding aggressive treatments may not accelerate time to death among dying ICU patients
Figure 3 for Withholding aggressive treatments may not accelerate time to death among dying ICU patients
Figure 4 for Withholding aggressive treatments may not accelerate time to death among dying ICU patients

  Access Paper or Ask Questions

Modeling Mistrust in End-of-Life Care


Jun 30, 2018
Willie Boag, Harini Suresh, Leo Anthony Celi, Peter Szolovits, Marzyeh Ghassemi

Figure 1 for Modeling Mistrust in End-of-Life Care
Figure 2 for Modeling Mistrust in End-of-Life Care
Figure 3 for Modeling Mistrust in End-of-Life Care
Figure 4 for Modeling Mistrust in End-of-Life Care

  Access Paper or Ask Questions

Evaluating Reinforcement Learning Algorithms in Observational Health Settings


May 31, 2018
Omer Gottesman, Fredrik Johansson, Joshua Meier, Jack Dent, Donghun Lee, Srivatsan Srinivasan, Linying Zhang, Yi Ding, David Wihl, Xuefeng Peng, Jiayu Yao, Isaac Lage, Christopher Mosch, Li-wei H. Lehman, Matthieu Komorowski, Matthieu Komorowski, Aldo Faisal, Leo Anthony Celi, David Sontag, Finale Doshi-Velez

Figure 1 for Evaluating Reinforcement Learning Algorithms in Observational Health Settings
Figure 2 for Evaluating Reinforcement Learning Algorithms in Observational Health Settings
Figure 3 for Evaluating Reinforcement Learning Algorithms in Observational Health Settings
Figure 4 for Evaluating Reinforcement Learning Algorithms in Observational Health Settings

  Access Paper or Ask Questions

Clinical Intervention Prediction and Understanding using Deep Networks


May 23, 2017
Harini Suresh, Nathan Hunt, Alistair Johnson, Leo Anthony Celi, Peter Szolovits, Marzyeh Ghassemi

Figure 1 for Clinical Intervention Prediction and Understanding using Deep Networks
Figure 2 for Clinical Intervention Prediction and Understanding using Deep Networks
Figure 3 for Clinical Intervention Prediction and Understanding using Deep Networks
Figure 4 for Clinical Intervention Prediction and Understanding using Deep Networks

  Access Paper or Ask Questions

Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach


May 23, 2017
Aniruddh Raghu, Matthieu Komorowski, Leo Anthony Celi, Peter Szolovits, Marzyeh Ghassemi

Figure 1 for Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach
Figure 2 for Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach
Figure 3 for Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach
Figure 4 for Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach

  Access Paper or Ask Questions

Comparing Rule-Based and Deep Learning Models for Patient Phenotyping


Mar 25, 2017
Sebastian Gehrmann, Franck Dernoncourt, Yeran Li, Eric T. Carlson, Joy T. Wu, Jonathan Welt, John Foote Jr., Edward T. Moseley, David W. Grant, Patrick D. Tyler, Leo Anthony Celi

Figure 1 for Comparing Rule-Based and Deep Learning Models for Patient Phenotyping
Figure 2 for Comparing Rule-Based and Deep Learning Models for Patient Phenotyping
Figure 3 for Comparing Rule-Based and Deep Learning Models for Patient Phenotyping
Figure 4 for Comparing Rule-Based and Deep Learning Models for Patient Phenotyping

  Access Paper or Ask Questions