March 31, 2020 UPDATE: ACM CHIL 2020 will be held on July 23-25, 2020 at the Sheraton Centre Toronto Hotel in Toronto, Ontario, Canada.

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July 23, Day 1: Opening & Tutorials

Time (EST)EventLocation
10am – 11:30amDoctoral SymposiumBirchwood Ballroom
11:30 – 1:00pmLunch (on own)See options
1:00pm – 1:30pmOpening RemarksBirchwood Ballroom
1:30pm – 2:20pmKeynote: Yoshua BengioBirchwood Ballroom
2:30pm – 4:00pmConcurrent Tutorials
A Tour of Survival Analysis, from Classical to Modern
George H. Chen, Jeremy C. Weiss (CMU)
Chestnut West
Public Health Datasets for Deep Learning: Challenges and Opportunities
Josh Risley, Katie Lin, Sam Ching
Chestnut East
State of the Art Deep Learning in Medical Imaging
Joseph P. Cohen (MILA)
**Maple West
4:00pm – 4:20pmBreak with coffeeBirchwood Foyer
4:20pm – 5:50pmConcurrent Tutorials
Population and public health: challenges and opportunities
Vishwali Mhasawade, Yuan Zhao, Rumi Chunara (NYU)
Chestnut West
Analyzing critical care data, from speculation to publication, starring MIMIC-IV
Alistair Johnson (MIT)
Chestnut East
Tutorial TBD
5:50pm – 6:00pmClosing RemarksBirchwood Ballroom
6:00pm – 8:00pmSocialSheraton Centre Toronto Hotel

July 24, Day 2: Single-Track Proceedings Sessions

Time (EST)EventLocation
8:30am – 9:00amBreakfast & RegistrationBirchwood Foyer
9:00am – 9:10amOpening RemarksBirchwood Ballroom
9:10am – 10:00amKeynote: Nigam ShahBirchwood Ballroom
10:00am – 10:50amResearch TalksBirchwood Ballroom
#1, Defining Admissible Rewards for High-Confidence Policy Evaluation in Batch Reinforcement Learning
Niranjani Prasad, Barbara Engelhardt (Princeton University); Finale Doshi-Velez (Harvard SEAS)
#2, Variational Learning of Individual Survival Distributions
Zidi Xiu, Chenyang Tao, Ricardo Henao (Duke University)
#3, Interpretable Subgroup Discovery in Treatment Effect Estimation with Application to Opioid Prescribing Guidelines
Chirag Nagpal (Carnegie Mellon University); Dennis Wei, Bhanukiran Vinzamuri (IBM Research); Monica Shekhar (IBM Global Business Services); Sara E. Berger, Subhro Das, Kush R. Varshney (IBM Research)
#4, Adverse Drug Reaction Discovery from Electronic Health Records with Deep Neural Networks
Wei Zhang (University of Wisconsin, Madison); Zhaobin Kuang (Stanford University); Peggy Peissig (Marshfield Clinic Research Institute); David Page (Duke University)
#5, CaliForest: Calibrated Random Forest for Health Data
Yubin Park, Joyce C Ho (Emory University)
10:50am – 11:10amBreak with coffeeBirchwood Foyer
11:10am – noonResearch TalksBirchwood Ballroom
#6, BMM-Net: Automatic Segmentation of Edema in Optical Coherence Tomography Based on Boundary Detection and Multi-Scale Network
Ruru Zhang, Jiawen He, Shenda SHI, Haihong E, Zhonghong Ou, Meina Song (Beijing University of Posts and Telecommunications)
#7, Survival Cluster Analysis
Paidamoyo Chapfuwa (Duke University); Chunyuan Li (Microsoft Research); Nikhil Mehta, Lawrence Carin, Ricardo Henao (Duke University)
#8, An Adversarial Approach for the Robust Classification of Pneumonia from Chest Radiographs
Joseph D. Janizek, Gabriel Erion, Alex J. DeGrave, Su-In Lee (University of Washington)
#9, Explaining an increase in predicted risk for clinical alerts
Michaela Hardt (Amazon); Alvin Rajkomar (Google, UCSF); Gerardo Flores, Andrew Dai, Michael Howell, Greg Corrado, Claire Cui (Google); Moritz Hardt (UC Berkeley, Twitter)
#10, Fast Learning-based Registration of Sparse 3D Clinical Images
Kathleen Lewis (MIT); Natalia S. Rost (Harvard Medical School, MGH); John Guttag (MIT); Adrian V. Dalca (MIT, Harvard Medical School, MGH)
noon – 1:10pmLunch (on own)See options
1:10pm – 2:00pmResearch TalksBirchwood Ballroom
#11, Multiple Instance Learning for Predicting Necrotizing Enterocolitis in Premature Infants Using Microbiome Data
Thomas Hooven (University of Pittsburgh); Ansaf Salleb-Aouissi, Yun Chao Lin (Columbia University)
#12, Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings
Haoran Zhang, Amy X. Lu, Mohamed Abdalla (University of Toronto; Vector Institute for Artificial Intelligence); Matthew McDermott (MIT); Marzyeh Ghassemi (University of Toronto; Vector Institute for Artificial Intelligence)
#13, Disease State Prediction From Single-Cell Data Using Graph Attention Networks
Neal Ravindra, Arijit Sehanobish, David Van Dijk (Yale University)
#14, Using SNOMED to Automate Clinical Concept Mapping
Shaun Gupta, Frederik Dieleman, Patrick Long, Orla Doyle, Nadejda Leavitt (IQVIA)
#15, MMiDaS-AE: Multi-modal Missing Data aware Stacked Autoencoder for Biomedical Abstract Screening
Eric W. Lee (Emory University); Byron C. Wallace (Northeastern University); Karla I. Galaviz, Joyce C. Ho (Emory University)
2:00pm – 2:20pmBreak with coffeeBirchwood Foyer
2:20pm – 3:10pmResearch TalksBirchwood Ballroom
#16, Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging
Luke Oakden-Rayner (Australian Institute for Machine Learning, University of Adelaide); Jared Dunnmon (Stanford University); Gustavo Carneiro (Australian Institute for Machine Learning, University of Adelaide); Christopher Re (Department of Computer Science, Stanford University)
#17, Interactive Hybrid Approach to Combine Human and Machine Intelligence for Personalized Rehabilitation Assessment
Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic (Carnegie Mellon University); Alexandre Bernardino (Instituto Superior Técnico); Sergi Bermúdez i Badia (Madeira Interactive Technology Institute)
#18, Extracting Medical Entities from Social Media
Sanja Scepanovic, Enrique Martin-Lopez, Daniele Quercia, Khan Baykaner (Nokia Bell Labs)
#19, Population-aware Hierarchical Bayesian Domain Adaptation via Multi-component Invariant Learning
Vishwali Mhasawade, Nabeel Abdur Rehman, Rumi Chunara (New York University)
#20, TASTE: Temporal and Static Tensor Factorization for Phenotyping Electronic Health Records
Ardavan Afshar (Georgia Institute of Technology); Ioakeim Perros (HEALTH[at]SCALE); Haesun Park (Georgia Institute of Technology); Christopher deFilippi (INOVA); Sherry Yan (Sutter Health); Walter Stewart (unaffiliated); Joyce Ho (Emory University); Jimeng Sun (Georgia Institute of Technology)
3:10pm – 3:30pmBreakBirchwood Foyer
3:30pm – 4:00pmResearch TalksBirchwood Ballroom
#21, Analyzing the Role of Model Uncertainty for Electronic Health Records
Michael W. Dusenberry, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon (Google); Ghassen Jerfel (Google, Duke University); Katherine Heller, Andrew M. Dai (Google)
#22, Deidentification of free-text medical records using pre-trained bidirectional transformers
Alistair Johnson, Lucas Bulgarelli, Tom Pollard (MIT)
#23, MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-III
Shirly Wang (University of Toronto, Layer 6 AI); Matthew B. A. McDermott, Geeticka Chauhan (Massachusetts Institute of Technology); Marzyeh Ghassemi (University of Toronto; Vector Institute for Artificial Intelligence); Michael C. Hughes (Tufts University); Tristan Naumann (Microsoft Research)
4:10pm – 5:00pmKeynote: Elaine NsoesieBirchwood Ballroom
5:00pm – 6:30pmPoster Session I (Proceedings)Birchwood Foyer

July 25, Day 3: Track-Based Workshop  Sessions

Time (EST)EventLocation
8:30am – 9:00amBreakfastBirchwood Foyer
9:00am – 9:10amOpening RemarksBirchwood Ballroom
9:10am – 10:00amKeynote: Sherri RoseBirchwood Ballroom
10:00am – 10:10amOpening Remarks by Track LeadsBirchwood Ballroom
10:10am – 11:00amWorkshop Spotlight TalksBirchwood Ballroom
3 min x 14 talks
Track 1, Differentially Private “Small” Dataset Release Using Random Projections
Lovedeep Gondara, Ke Wang (Simon Fraser University)
Track 1, Calibrated Deep Nonparametric Survival Analysis
Fahad Kamran, Jenna Wiens (University of Michigan)
Track 1, Temporal-Clustering Invariance in Irregular Healthcare Time Series
Mohammad Taha Bahadori, Zachary Lipton (Amazon)
Track 1, Predicting Progression Patterns of Type 2 Diabetes using Multi-sensor Measurements
Ramin Ramazi, Christine Perndorfer, Emily Soriano, Jean-Philippe Laurenceau, Rahmatollah Beheshti (University of Delaware)
Track 1, Learning Representations for Prediction of Next Patient State
Taylor Killian (University of Toronto; Vector Institute); Jayakumar Subramanian (Adobe Research India); Mehdi Fatemi (Microsoft Research); Marzyeh Ghassemi (University of Toronto; Vector Institute for Artificial Intelligence)
Track 1, Deep State-Space Generative Model For Correlated Time-to-Event Predictions
Yuan Xue, Denny Zhou, Nan Du, Andrew M. Dai, Zhen Xu, Kun Zhang, Claire Cui (Google)
Track 1, Differentially Private Survival Function Estimation
Lovedeep Gondara, Ke Wang (Simon Fraser University)
Track 1, MGP-AttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis
Margherita Rosnati (Imperial College London); Vincent Fortuin (ETH Zürich)
Track 1, Improving medical annotation quality to decrease labeling burden using stratified noisy cross-validation
Joy Hsu, Sonia Phene, Akinori Mitani, Jieying Luo, Naama Hammel, Jonathan Krause, Rory Sayres (Google)
Track 1, A Multi-Task Learning Approach to Personalized Progression Modeling
Mohamed Ghalwash, Daby Sow (IBM Research)
Track 1, Improved Patient Classification with Hierarchical Language Model Pretraining over Clinical Notes
Jonas Kemp (Google); Alvin Rajkomar (Google, UCSF); Andrew M. Dai (Google)
Track 1, Health change detection using temporal transductive learning
Abhay Harpale (GE Global Research)
Track 1, Cost-Sensitive Feature Selection Using Bayesian Optimization
Lucca G. Zenobio, Thiago N. C. Cardoso, Andrea Kauffmann, Augusto Antunes (Mineria)
Track 4, A large-scale Twitter dataset for drug safety applications mined from publicly existing resources
Ramya Tekumalla, Juan M Banda (Georgia State University)
11:00am – 12:30pmLunch (on own) See options
12:30pm – 1:20pmWorkshop Spotlight TalksBirchwood Ballroom
3 min x 14 talks
Track 2, ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission
Kexin Huang (Harvard University); Jaan Altosaar (Princeton University); Rajesh Ranganath (NYU)
Track 2, Mining Dynamic Problem Lists from Clinical Notes for the Interpretable Prediction of Adverse Outcomes
Justin Lovelace, Nathan Hurley (Texas A&M University); Adrian Haimovich (Yale University); Bobak Mortazavi (Texas A&M University)
Track 2, Deep Transfer Learning for Physiological Signals
Hugh Chen (University of Washington); Scott Lundberg (Microsoft Research); Gabe Erion, Jerry H. Kim, Su-In Lee (University of Washington)
Track 2, Open Set Medical Diagnosis
Viraj Prabhu (Georgia Institute of Technology); Anitha Kannan, Geoffrey J. Tso, Namit Katariya, Manish Chablani (Curai); David Sontag (MIT); Xavier Amatriain (Curai)
Track 2, Assessing Robustness of Deep Learning Methods in Dermatological Workflow
Sourav Mishra, Subhajit Chaudhury (The University of Tokyo); Hideaki Imaizumi (exMedio Inc.); Toshihiko Yamasaki (The University of Tokyo)
Track 2, 3D Image Based Craniosynostosis Triaging System
Devin Singh (Staff Physician, Hospital for Sick Children, University of Toronto); Pouria Mashouri, John Phillips, Anna Goldenberg, Michael Brudno (Hospital for Sick Children, University of Toronto)
Track 2, Automatic Detection and Classification of Cognitive Distortions in Mental Health Text
Benjamin Shickel, Scott Siegel, Martin Heesacker (University of Florida); Sherry Benton (TAO Connect, Inc.); Parisa Rashidi (University of Florida)
Track 2, Automated Emotional Valence Prediction in Mental Health Text via Deep Transfer Learning
Benjamin Shickel, Martin Heesacker (University of Florida); Sherry Benton (TAO Connect, Inc.); Parisa Rashidi (University of Florida)
Track 2, Generation of Differentially Private Heterogeneous Synthetic Electronic Health Records using GANs
Kieran Chin-Cheong, Thomas M. Sutter, Julia E. Vogt (ETH Zurich)
Track 2, Integrating Physiological Time Series and Clinical Notes with Deep Learning for Improved ICU Mortality Prediction
Satya Narayan Shukla, Benjamin Marlin (University of Massachusetts Amherst)
Track 2, CheXpedition: Investigating Generalization Challenges for Translation of Chest X-Ray Algorithms to the Clinical Setting
Pranav Rajpurkar, Anirudh Joshi, Phil Chen, Anuj Pareek, Amir Kiani (Stanford University); Matthew Lungren (Stanford University School of Medicine); Andrew Ng, Jeremy Irvin (Stanford University)
Track 2, Automated Medical Coding using BERT: Benchmarking Deep Learning in the Face of Subjective Labels
Mehmet Seflek, Wesam Elshamy, Abboud Chaballout, Ali Madani (Diagnoss Inc.)
Track 2, A Comprehensive Evaluation of Multitask Representation Learning on EHR Data
Matthew McDermott (MIT); Bret Nestor (University of Toronto); Wancong Zhang (New York University); Peter Szolovits (MIT); Anna Goldenberg (University of Toronto; Vector Institute for Artificial Intelligence; SickKids); Marzyeh Ghassemi (University of Toronto; Vector Institute for Artificial Intelligence)
Track 3, Dataset Bias in Diagnostic AI systems: Guidelines for Dataset Collection and Usage
Julie R Vaughn, Avital Baral, Mayukha Vadari, William Boag (MIT)
1:20pm – 1:40pmBreak with coffeeBirchwood Foyer
1:40pm – 3:00pmPoster Session II (Workshops)Birchwood Foyer
3:00pm – 3:50pmKeynote: Ruslan SalakhutdinovBirchwood Ballroom
3:50pm – 4:00pmClosing RemarksBirchwood Ballroom

Note: some details are tentative and subject to change.