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)
Measuring the Severity of Depressive Symptoms from Spoken Language and 3D Facial Expressions
Albert Haque, Michelle Guo, Adam Miner and Li Fei-Fei
What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems
Owen Lahav, Nicholas Mastronarde and Mihaela van der Schaar
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
MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks
Daniel Jarrett, Jinsung Yoon and Mihaela van der Schaar
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
Glottal Closure Instants Detection From Pathological Acoustic Speech Signal Using Deep Learning
Gurunath Reddy M, Tanumay Mandal and K. Sreenivasa Rao
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
Large-scale Generative Modeling to Improve Automated Veterinary Disease Coding
Yuhui Zhang, Allen Nie and James Zou
Estimating Causal Effects With Partial Covariates For Clinical Interpretability
Sonali Parbhoo, Mario Wieser and Volker Roth
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
An Analytics Approach to Randomized Controlled Trial Design for Hypertension Management
Anthony Bonifonte and Turgay Ayer
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
Machine Learning for Survival Analysis: Empirical Risk Minimization for Censored Distribution-Free Regression with Applications
Guillaume Ausset, Stéphan Clémençon and François Portier
Semi-supervised Rare Disease Detection Using Generative Adversarial Network
Wenyuan Li, Yunlong Wang, Yong Cai, Corey Arnold, Emily Zhao and Yilian Yuan
Inferring Causality by Answering Naranjo Questionnaire using Electronic Health Records
Bhanu Pratap Singh Rawat, Fei Li and Hong Yu
Dynamic Measurement Scheduling for Adverse Event Forecasting using Deep RL
Chun-Hao Chang, Mingjie Mai and Anna Goldenberg
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
Feature Selection Based on Unique Relevant Information for Health Data
Shiyu Liu and Mehul Motani
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, Jen J. Gong, 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
Unsupervised Medical Image Imputation via Variational Inference of Deep Subspaces
Adrian Dalca, John Guttag and Mert Sabuncu
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)
Automatic Documentation of ICD Codes with Far-Field Speech Recognition
Albert Haque and Corinna Fukushima
Distinguishing correlation from causation using genome-wide association studies
Luke O'Connor and Alkes Price
Prototypical Clustering Networks for Dermatological Disease Diagnosis
Viraj Prabhu, Anitha Kannan, Murali Ravuri, Manish Chablani, David Sontag, Xavier Amatriain
Disease Detection in Weakly Annotated Volumetric Medical Images using a Convolutional LSTM Network
Nathaniel Braman, David Beymer and Ehsan Dehghan
Cluster-Based Learning from Weakly Labeled Bags in Digital Pathology
Shazia Akbar and Anne Martel
Decorrelating the Brain Dynamics with Recurrent Neural Network for Schizophrenia Classification
Byung-Hoon Kim and Jong Chul Ye
Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning
Changhee Lee, Nicholas Mastronarde and Mihaela van der Schaar
Generative Modeling and Inverse Imaging of Cardiac Transmembrane 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
Isolating Cost Drivers in Interstitial Lung Disease Treatment Using Nonparametric Bayesian Methods
Seth Stafford and Christoph Kurz
Deep Ensemble Tensor Factorization for Longitudinal Patient Trajectories Classification
Edward De Brouwer, Jaak Simm, Adam Arany and Yves Moreau
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
Advancing PICO Element Detection in Medical Text via Deep Neural Networks
Di Jin and Peter Szolovits
Time Aggregation and Model Interpretation for Deep Multivariate Longitudinal Patient Outcome Forecasting Systems in Chronic Ambulatory Care
Beau Norgeot, Dmytro Lituiev, Benjamin Glicksberg and Atul Butte
ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks
Xiaodan Hu, Audrey G, Chung, Paul Fieguth and Alexander Wong
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
Unsupervised Deep Neural Networks Harmonize Multiple Data Sources and Explain Inherent Biases
Nelson Johansen and Gerald Quon
Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records
Xiangan Liu, Keyang Xu, Pengtao Xie and Eric Xing
Digital Acceptances
Robust Active Learning for Electrocardiographic Signal Classification
Xu Chen and Saratendu Sethi
Identification of Predictive Subpopulations in Heterogeneous Samples
Bryan He, James Zou and Matt Thomson
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
Class Augmented Semi-Supervised Learning for Practical Clinical Analytics on Physiological Signals
Arijit Ukil, Soma Bandyopadhyay, Chetanya Puri and Arpan Pal
Cross-domain Transfer Learning for Cardiovascular Diseases
Girmaw Abebe Tadesse, Tingting Zhu and David Clifton
Reliable uncertainty estimate for antibiotic resistance classification with Stochastic Gradient Langevin Dynamics
Md-Nafiz Hamid and Iddo Friedberg
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
Learning Optimal Personalized Treatment Rules Using Robust Regression Informed K-NN
Ruidi Chen and Ioannis Paschalidis
Unsupervised learning with GLRM feature selection reveals novel traumatic brain injury phenotypes
Aaron Masino and Kaitlin Folweiler
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
A Deep Latent-Variable Model application to Select Treatment Intensity in Survival Analysis
Cedric Beaulac, Jeffrey S, Rosenthal and David Hodgson
FADL:Federated-Autonomous Deep Learning for Distributed Electronic Health Record
Dianbo Liu, Tim Miller, Raheel Sayeed and Kenneth Mandl
Early Stratification of Patients at Risk for Postoperative Complications after Elective Colectomy
Wen Wang, Rema Padman and Nirav Shah
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
Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detection
Jwala Dhamala, Emmanuel Azuh, Abdullah Al-Dujaili, Jonathan Rubin and Una-May O'Reilly
Predicting Diabetes Disease Evolution Using Financial Records and Recurrent Neural Networks
Rafael Sousa, Anderson Soares and Lucas Pereira
A Distillation Approach to Data Efficient Individual Treatment Effect Estimation
Maggie Makar, Adith Swaminathan and Emre Kiciman
Integrating Reinforcement Learning to Self Training for Pulmonary Nodule Segmentation in Chest X-rays
Sejin Park, Woochan Hwang and Kyu-Hwan Jung
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
Knowledge-driven generative subspaces for modeling multi-view dependencies in medical data
Parvathy Sudhir Pillai and Tze Yun Leong
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
Computational EEG in Personalized Medicine: A study in Parkinson’s Disease
Sebastian Mathias Keller, Maxim Samarin, Antonia Meyer, Vitalii Kozak, Ute Gschwandtner, Peter Fuhr, Volker Roth
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
HYPE: A High Performing NLP System for Automatically Detecting Hypoglycemia Events from Electronic Health Record Notes
Yonghao Jin, Fei Li and Hong Yu
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
Medical Concept Normalization in Social Media Posts with Recurrent Neural Networks
Elena Tutubalina and Zulfat Miftakhutdinov
Patient Subtyping with Disease Progression and Irregular Observation Trajectories
Nikhil Galagali and Minnan Xu-Wilson
Bayesian deep neural networks for low-cost neurophysiological markers of Alzheimer’s disease severity
Wolfgang Fruehwirt, Adam Cobb, Stephen Roberts and Georg Dorffner
Integrating omics and MRI data with kernel-based tests and CNNs to identify rare genetic markers for Alzheimer's disease
Stefan Konigorski, Shahryar Khorasani and Christoph Lippert
Semantically-aware population health risk analyses
Alexander New, Sabbir Rashid, John Erickson, Deborah McGuinness and Kristin Bennett
Hierarchical Deep Learning Classification of Unstructured Pathology Reports to Automate ICD-O Morphology Grading
Waheeda Saib and Tapiwa Chiwewe
Examining the measurement of quality in healthcare using artificial intelligence methods: a study of quality in long-term care
Pouria Mashouri, Andrea Iaboni and Babak Taati
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
Partial Least Squares Regression in Alzheimer's disease: finding latent shared structures between biomarkers and imaging features
Adrià Casamitjana and Veronica Vilaplana
Advance Prediction of Ventricular Tachyarrhythmias using Patient Metadata and Multi-Task Networks
Marek Rei, Josh Oppenheimer and Marek Sirendi
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
Boosting pathology detection in infants by deep transfer learning from adult speech
Charles Onu, Gautam Bhattacharya and Doina Precup
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
A New NMT Model for Translating Clinical Texts from English to Spanish
Rumeng Li, Xun Wang and Hong Yu
Cross-Modal Medical Embedding Alignment Without Human Annotations
James Mullenbach, Ioakeim Perros, Xiaoqian Jiang and Jimeng Sun
Dual Objective Approach Using A Convolutional Neural Network for Magnetic Resonance Elastography
Ligin Solamen, Yipeng Shi and Justice Amoh
A Sequence of Two Studies to Learn & Test Heterogeneous Treatment Sub-groups: Effects of Cost Exposure on Use of Outpatient Care
Rahul Ladhania, Amelia Haviland, Neeraj Sood and Ateev Mehrotra
Discovering heterogeneous subpopulations for fine-grained analysis of opioid use and opioid use disorders
Jen Gong, Abigail Jacobs, Toby Stuart and Mathijs de Vaan
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
Practical Window Setting Optimization for Medical Image Deep Learning
Hyunkwang Lee, Myeongchan Kim and Synho Do
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
METCC: METric learning for Confounder Control Making distance matter in high dimensional biological analysis
Kabir Manghnani, Adam Drake, Nathan Wan and Imran Haque
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
Phenotype inference with Semi-Supervised Mixed Membership Models
Victor Rodriguez and Adler Perotte
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
Towards Robust Lung Segmentation in Chest Radiographs with Deep Learning
Jyoti Islam and Yanqing Zhang
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