Papers


We have accepted 81 short papers for poster presentation at the workshop. This year we have also established a new category and have selected 86 short papers for digital acceptances.

These are listed below, with links to the paper on arXiv if provided by the authors.

The poster acceptances will appear at two possible poster sessions on Sat. Dec. 8 in Palais des Congres de Montreal.

Digital acceptances will be featured on the website and during breaks at the workshop.

Presenters: Remember to follow the poster and slide instructions: Portrait format. Recommended size: 24 inches wide and 32 inches tall. Digital acceptance slides can be submitted here.

Poster Session 1 (11:30-12:30)

Natural language understanding for task oriented dialog in the biomedical domain in a low ressources context

Antoine Neuraz, Leonardo Campillos Llanos, Anita Burgun and Sophie Rosset

Clinical Concept Extraction with Contextual Word Embedding

Henghui Zhu, Ioannis Paschalidis and Amir Tahmasebi

Disease phenotyping using deep learning: A diabetes case study

Sina Rashidian, Janos Hajagos, Richard Moffitt, Fusheng Wang, Xinyu Dong, Kayley Abell-Hart, Kimberly Noel, Rajarsi Gupta, Mathew Tharakan, Veena Lingam, Joel Saltz and Mary Saltz

Model-Based Reinforcement Learning for Sepsis Treatment

Aniruddh Raghu, Matthieu Komorowski and Sumeetpal Singh

PatchNet: Context-Restricted Architectures to Provide Visual Features for Image Classification

Adityanarayanan Radhakrishnan, Charles Durham, Ali Soylemezoglu and Caroline Uhler

Group induced graphical lasso allows for discovery of molecular pathways-pathways interactions

Veronica Tozzo, Federico Tomasi, Margherita Squillario and Annalisa Barla

A Framework for Implementing Machine Learning on Omics Data

Geoffroy Dubourg-Felonneau, Timothy Cannings, Fergal Cotter, Hannah Thompson, Nirmesh Patel, John W Cassidy and Harry W Clifford

Population-aware Hierarchical Bayesian Domain Adaptation

Vishwali Mhasawade, Nabeel Abdur Rehman and Rumi Chunara

Modeling Irregularly Sampled Clinical Time Series

Satya Narayan Shukla and Benjamin Marlin

Deep Self-Organization: Interpretable Discrete Representation Learning on Medical Time Series

Vincent Fortuin, Matthias Hüser, Francesco Locatello, Heiko Strathmann and Gunnar Rätsch

Interpretable Graph Convolutional Neural Networks for Inference on Noisy Knowledge Graphs

Daniel Neil, Joss Briody, Alix Lacoste, Aaron Sim, Paidi Creed and Amir Saffariazar

Semi-supervised Rare Disease Detection Using Generative Adversarial Network

Wenyuan Li, Yunlong Wang, Yong Cai, Corey Arnold, Emily Zhao and Yilian Yuan

Privacy-Preserving Action Recognition for Smart Hospitals using Low-Resolution Depth Images

Edward Chou, Matthew Tan, Cherry Zou, Michelle Guo, Albert Haque, Arnold Milstein and Li Fei-Fei

Active Transfer Learning and Natural Language Processing For Deep Learning Liver Volumetry Using Surrogate Metrics

Brett Marinelli, Martin Kang, Michael Martini, John Zech, Joseph Titano, Samuel Cho, Anthony Costa and Eric Oermann

Registration of Sparse Clinical Images

Kathleen Lewis, Guha Balakrishnan, John Guttag and Adrian Dalca

Improving Clinical Predictions through Unsupervised Time Series Representation Learning

Xinrui Lyu, Matthias Hüser, Stephanie Hyland, George Zerveas and Gunnar Rätsch

A Hybrid Instance-based Transfer Learning Method

Azin Asgarian, Parinaz Sobhani, Ji Chao Zhang, Madalin Mihailescu, Ariel Sibilia, Ahmed Bilal Ashref and Babak Taati

Multiple Instance Learning for ECG Risk Stratification

Divya Shanmugam, Davis Blalock and John Guttag

Triage and Doctor Effort in Medical Machine Learning Prediction

Maithra Raghu, Jon Kleinberg and Sendhil Mullainathan

Measuring the Stability of EHR- and EKG-based Predictive Models

Andrew Miller, Ziad Obermeyer, and Sendhil Mullainathan

Inferring Multidimensional Rates of Aging from Cross-Sectional Data

Emma Pierson, Pang Wei Koh, Tatsunori Hashimoto, Daphne Koller, Jure Leskovec, Nick Eriksson and Percy Liang

Learning from Few Subjects in the Presence of Large Amounts of Ambulatory Data

Jose Javier Gonzalez Ortiz, John Guttag, Robert E, Hillman, Daryush Mehta, Jarrad Van Stan and Marzyeh Ghassemi

Descriptive Analysis of ICU Patient Mobilization from Depth Videos

Laëtitia Shao, Zaid Nabulsi, Ruchir Rastogi, Bingbin Liu, Francesca Salipur, Serena Yeung, N, Lance Downing, William Beninati, Arnold Milstein and Li Fei-Fei

Learning Global Additive Explanations for Neural Nets Using Model Distillation

Sarah Tan, Rich Caruana, Giles Hooker, Paul Koch and Albert Gordo

Towards generative adversarial networks as a new paradigm for radiology education

Samuel Finlayson, Hyunkwang Lee, Isaac Kohane and Luke Oakden-Rayner

Semi-Supervised Deep Learning for Abnormality Classification in Retinal Images

Bruno Lecouat, Ken Chang, Chuan-Sheng Foo, Balagopal Unnikrishnan, James Brown, Houssam Zenati, Andrew Beers, Vijay Chandrasekhar, Pavitra Krishnaswamy and Jayashree Kalpathy-Cramer

Generative models for clinical imaging genetic analysis

Francesco Paolo Casale, Nicolo Fusi, Jennifer Listgarten and Adrian Dalca

Poster Session 2 (13:30-14:30)

Prototypical Clustering Networks for Dermatological Disease Diagnosis

Viraj Prabhu, Anitha Kannan, Murali Ravuri, Manish Chablani, David Sontag, Xavier Amatriain

Generative Modeling and Inverse Imaging of CardiacTransmembrane Potential

Sandesh Ghimire, Jwala Dhamala, Prashnna Kumar Gyawali and Linwei Wang

Unsupervised learning with contrastive latent variable models

Kristen Severson, Soumya Ghosh and Kenney Ng

Radiotherapy Target Contouring with Convolutional Gated Graph Neural Network

Chun-Hung Chao, Yen-Chi Cheng, Hsien-Tzu Cheng, Chi-Wen Huang, Tsung-Ying Ho, Chen-Kan Tseng, Le Lu and Min Sun

Predicting pregnancy using large-scale data from a women's health tracking mobile application

Bo Liu, Shuyang Shi, Yongshang Wu, Laura Symul, Emma Pierson and Jure Leskovec

DeepSPINE: automated lumbar spinal stenosis grading using deep learning

Jen-Tang Lu, Stefano Pedemonte, Bernardo Bizzo, Sean Doyle, Katherine Andriole, Mark Michalski, R, Gilberto Gonzalez and Stuart Pomerantz

Compensated Integrated Gradients to Reliably Interpret EEG Classification

Kazuki Tachikawa, Yuji Kawai, Jihoon Park and Minoru Asada

Childhood obesity prediction and risk factor analysis from nationwide health records

Hagai Rossman, Smadar Shilo, Nitzan Artzi and Eran Segal

TIFTI: A Framework for Extracting Drug Intervals from Longitudinal Clinic Notes

Monica Agrawal, Griffin Adams, Nathan Nussbaum and Benjamin Birnbaum

Automatic Diagnosis of Short-Duration 12-Lead ECG using Deep Convolutional Network

Antonio H, Ribeiro, Manoel Horta Ribeiro, Gabriela Paixão, Derick Oliveira, Paulo R, Gomes, Jéssica A, Canazart, Milton Pifano, Wagner Meira Jr, Thomas B, Schön and Antonio Luiz Ribeiro

A Bayesian model of acquisition and clearance of bacterial colonization

Marko Järvenpää, Mohamad Sater, Georgia Lagoudas, Paul Blainey, Loren Miller, James McKinnell, Susan Huang, Yonatan Grad and Pekka Marttinen

A Probabilistic Model of Cardiac Physiology and Electrocardiograms

Andrew Miller, Ziad Obermeyer, David Blei, John Cunningham, and Sendhil Mullainathan

Curriculum Learning for Training Neural Networks on Medical Data

Rasheed El-Bouri, David Clifton and Tingting Zhu

Automated Radiation Therapy Treatment Planning using 3-D Generative Adversarial Networks

Aaron Babier, Rafid Mahmood, Andrea McNiven, Adam Diamant and Timothy Chan

Imputation of Clinical Covariates in Time Series

Dimitris Bertsimas, Agni Orfanoudaki and Colin Pawlowski

Predicting Language Recovery after Stroke with Convolutional Networks on Stitched MRI

Yusuf Roohani, Noor Sajid, Pranava Madhyastha, Thomas Hope and Cathy Price

The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech

Aparna Balagopalan, Jekaterina Novikova, Frank Rudzicz and Marzyeh Ghassemi

Corresponding Projections for Orphan Screening

Sven Giesselbach, Katrin Ullrich, Michael Kamp, Daniel Paurat and Thomas Gärtner

Deep Learning with Attention to Predict Gestational Age of the Fetal Brain

Liyue Shen, Edward Lee, Katie Shpanskaya and Kristen Yeom

Leveraging Routine Pre-Operative Blood Draws to Predict Hemorrhagic Shock During Surgery

Xinyu Li, Michael R, Pinsky, Gilles Clermont and Artur Dubrawski

Rank Projection Trees for Multilevel Neural Network Interpretation

Jonathan Warrell, Hussein Mohsen and Mark Gerstein

Using permutations to assess confounding in machine learning applications for digital health

Elias Chaibub Neto, Abhishek Pratap, Thanneer Perumal, Meghasyam Tummalacherla, Brian Bot, Lara Mangravite and Larsson Omberg

Robustness against the channel effect in pathological voice detection

Yi-Te Hsu, Zining Zhu, Chi-Te Wang, Shih-Hau Fang, Frank Rudzicz and Yu Tsao

Learning to Unlearn: Building Immunity to Dataset Bias in Medical Imaging Studies

Ahmed Ashraf, Shehroz Khan, Nikhil Bhagwat, Mallar Chakravarty and Babak Taati

Modeling the Biological Pathology Continuum with HSIC-regularized Wasserstein Auto-encoders

Denny Wu, Hirofumi Kobayashi, Charles Ding, Cheng Lei, Keisuke Goda and Marzyeh Ghassemi

Unsupervised Multimodal Representation Learning across Medical Images and Reports

Tzu-Ming Harry Hsu, Wei-Hung Weng, Willie Boag, Matthew McDermott and Peter Szolovits

Deep Sequence Modeling for Hemorrhage Diagnosis

Fabian Falck, Michael Pinsky and Artur Dubrawski

Digital Acceptances

Can We Estimate the Health-Related Quality of Life of Twitter Users Using Tweets? A Feasibility Study

Karthik Sarma, Brennan M, R, Spiegel, Mark W, Reid, Shawn Chen, Raina M, Merchant, Emily Seltzer and Corey W, Arnold

Cross-domain Transfer Learning for Cardiovascular Diseases

Girmaw Abebe Tadesse, Tingting Zhu and David Clifton

Adjusting for Confounding in Unsupervised Latent Representations of Images

Craig A Glastonbury, Michael Ferlaino, Christoffer Nellaker and Cecilia Lindgren

Generative Adversarial Frameworkfor Learning Multiple Clinical Tasks

Mina Rezaei, Haojin Yang and Christoph Meinel

An adaptive treatment recommendation and outcome prediction model for metastatic melanoma

Xue Teng, Fuad Gwadry, Haley McConkey, Scott Ernst and Femida Gwadry-Sridhar

Feature Selection for Survival Analysis with Competing Risks using Deep Learning

Carl Rietschel, Jinsung Yoon and Mihaela van der Schaar

Application of Clinical Concept Embeddings for Heart Failure Prediction in UK EHR data

Spiros Denaxas, Pontus Stenetorp, Sebastian Riedel, Maria Pikoula, Richard Dobson and Harry Hemingway

Sreenivasa Rao

Gurunath Reddy M, Tanumay Mandal and K

Machine Learning on Electronic Health Records: Models and Features Usages to predict Medication Non-Adherence

Thomas Janssoone, Pierre Rinder, Pierre Hornus, Clémence Bic and Dora Kanoun

Digital Breast Tomosynthesis Reconstruction using Deep Learning

Nikita Moriakov, Koen Michielsen, Jonas Adler, Ritse Mann, Ioannis Sechopoulos and Jonas Teuwen

FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record

Dianbo Liu, Tim Miller, Raheel Sayeed and Kenneth Mandl

Explainable Genetic Inheritance Pattern Prediction

Edmond Cunningham, Dana Schlegel and Andrew DeOrio

In-silico Risk Analysis of Personalized Artificial Pancreas Controllers via Rare-event Simulation

Matthew O'Kelly, Aman Sinha, Justin Norden and Hongseok Namkoong

EEG Seizure Detection via Deep Neural Networks: Application and Interpretation

Jiening Zhan, Hector Yee, Ian Covert, Jiang Wu, Albee Ling, Matthew Shore, Eric Teasley, Rebecca Davies, Tiffany Kung, Justin Tansuwan, John Hixson and Ming Jack Po

General-to-Detailed GAN for Infrequent Class Medical Images

Tatsuki Koga, Naoki Nonaka, Jun Sakuma and Jun Seita

Stochastic Optimal Control of Epidemic Processes in Networks

Lars Lorch, Abir De, Samir Bhatt, William Trouleau, Utkarsh Upadhyay and Manuel Gomez Rodriguez

Vision-Based Gait Analysis for Senior Care

Evan Darke, Anin Sayana, Kelly Shen, David Xue, Jun-Ting Hsieh, Zelun Luo, Li-Jia Li, N, Lance Downing, Arnold Milstein and Li Fei-Fei

Alternating Loss Correction for Preterm-Birth Prediction from EHR Data with Noisy Labels

Sabri Boughorbel, Fethi Jarray, Neethu Venugopal and Haithum Elhadi

Multimodal Medical Image Retrieval based on Latent Topic Modeling

Mandikal Vikram, Aditya Anantharaman, Suhas B S and Sowmya Kamath S

Towards Continuous Domain adaptation for Healthcare

Rahul V, Hariharan Ravishankar and Saihareesh Anamandra

Structure-Based Networks for Drug Validation

Cătălina Cangea, Arturas Grauslys, Francesco Falciani and Pietro Liò

Personalizing Intervention Probabilities By Pooling

Sabina Tomkins, Susan Murphy and Predrag Klasnja

Semi-unsupervised Learning of Human Activity using Deep Generative Models

Matthew Willetts, Aiden Doherty, Chris Holmes and Stephen Roberts

Interlacing Personal and Reference Genomes for Machine Learning Disease-Variant Detection

Luke R Harries, Suyi Zhang, Geoffroy Dubourg-Felonneau, James H R Farmery, Jonathan Sinai, Belle Taylor, Nirmesh Patel, John W Cassidy, John Shawe-Taylor and Harry W Clifford

Interpretable Clustering via Optimal Trees

Dimitris Bertsimas, Agni Orfanoudaki and Holly Wiberg

Modeling Treatment Delays for Patients Using Feature Label Pairs in a Time Series

Weiyu Huang, Yunlong Wang, Li Zhou, Emily Zhao, Yilian Yuan and Alejandro Ribero

Real-Time Sleep Staging using Deep Learning on a Smartphone for a Wearable EEG

Abhay Koushik, Judith Amores Fernandez and Pattie Maes

Semantically-aware population health risk analyses

Alexander New, Sabbir Rashid, John Erickson, Deborah McGuinness and Kristin Bennett

Modeling disease progression in longitudinal EMR data using continuous-time hidden Markov models

Aman Verma, Guido Powell, Yu Luo, David Stephens and David Buckeridge

Unsupervised Phenotype Identification from Clinical Notes for Association Studies in Cancer

Stefan Stark, Stephanie L Hyland, Julia Vogt and Gunnar Rätsch

Probabilistic modelling of gait for remote passive monitoring applications

Yordan Raykov, Luc Evers, Reham Badawy, Marjan Faber, Bastiaan Bloem and Max Little

Confounding-Robust Policy Improvement

Nathan Kallus and Angela Zhou

Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning

Mengqi Jin, Mohammad Taha Bahadori, Aaron Colak, Parminder Bhatia, Busra Celikkaya, Ram Bhakta, Selvan Senthivel, Mohammed Khalilia, Daniel Navarro, Borui Zhang, Tiberiu Doman, Arun Ravi, Matthieu Liger, Taha Kass-hout

Predicting Electroencephalogram Impressions using Deep Neural Networks

Siddharth Biswal, Michael Brandon Westover and Jimeng Sun

Prediction of New Onset Diabetes after Liver Transplant

Angeline Yasodhara, Mamatha Bhat and Anna Goldenberg

Direct Uncertainty Prediction for Medical Second Opinions

Maithra Raghu, Katy Blumer, Rory Sayres, Ziad Obermeyer, Sendhil Mullainathan and Jon Kleinberg

Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation

Bret Nestor, Matthew McDermott, Geeticka Chauhan, Tristan Naumann, Michael Hughes, Anna Goldenberg and Marzyeh Ghassemi

Web Applicable Computer-aided Diagnosis of Glaucoma Using Deep Learning

Mijung Kim, Ho-Min Park, Jasper Zuallaert, Olivier Janssens, Sofie Van Hoecke and Wesley De Neve

Cross-Modal Medical Embedding Alignment Without Human Annotations

James Mullenbach, Ioakeim Perros, Xiaoqian Jiang and Jimeng Sun

Privacy-Preserving Distributed Deep Learning for Clinical Data

Brett K. Beaulieu-Jones, William Yuan, Samuel G. Finlayson, Zhiwei Steven Wu

A Comparison of Methods for Progression Endotype Detection in Amyotrophic Lateral Sclerosis

Hamish Tomlinson, Romain Studer, Poojitha Ojamies, Joanna Holbrook and Paidi Creed

Learning the progression and clinical subtypes of Alzheimer’s disease from longitudinal clinical data

Vipul Satone, Rachneet Kaur, Faraz Faghri, Mike A Nalls, Andrew B Singleton, Roy H Campbell

Using Multitask Learning to Improve 12-Lead Electrocardiogram Classification

John Hughes, Taylor Sittler, Anthony Joseph, Jeffrey Olgin, Joseph Gonzalez and Geoffrey Tison

Atlas Construction and Improved Registration of Medical Images with CNN Frameworks

Adrian Dalca, Guha Balakrishnan, John Guttag and Mert Sabuncu

Uncertainty-Aware LSTM with Attention Early Warning System

Farah Shamout, Tingting Zhu, Peter Watkinson and David Clifton

Deep Learning approach for predicting 30 day readmissions after Coronary Artery Bypass Graft Surgery

Ramesh Manyam, Yanqing Zhang, William Keeling, Jose Binongo, Michael Kayatta and Seth Carter

Leveraging Deep Stein's based Risk Estimator for Unsupervised X-ray Denoising

Fahad Shamshad, Muhammad Awais, Muhammad Asim, Zain Ul Aabidin Lodhi, Muhammad Umair, Ali Ahmed

Predicting Blood Pressure Response to Fluid Bolus Therapy Using Attention-Based Neural Networks

Uma M, Girkar, Ryo Uchimido, Li-Wei H, Lehman, Peter Szolovits, Leo A Celi and Wei-Hung Weng,

Generalizability of predictive models for intensive care unit patients

Alistair Johnson, Tom Pollard and Tristan Naumann

Relation Networks for Optic Disc and Fovea Localization in Retinal Images

Sudharshan Chandra Babu, Shishira R Maiya and Sivasankar Elango

Learning Individualized Cardiovascular Responses from Large-scale Wearable Sensors Data

Haraldur Hallgrimsson, Filip Jankovic, Tim Althoff and Luca Foschini