MLHC 2019

37 papers

SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules

Irfan Al-Hussaini, Cao Xiao, M. Brandon Westover, Jimeng Sun

Learning from Few Subjects with Large Amounts of Voice Monitoring Data

Jose Javier Gonzalez Ortiz, Daryush D. Mehta, Jarrad H. Van Stan, Robert Hillman, John V. Guttag

Measuring the Sympathetic Response to Intense Exercise in a Practical Setting

Shiva Kaul, Anthony Falco, Karianne Anthes

Embryo Staging with Weakly-Supervised Region Selection and Dynamically-Decoded Predictions

Tingfung Lau, Nathan Ng, Julian Gingold, Nina Desai, Julian McAuley

Automated Estimation of Food Type from Body-worn Audio and Motion Sensors in Free-Living Environments

Mark Mirtchouk, Dana L. McGuire, Andrea L. Deierlein, Samantha Kleinberg

Simultaneous Measurement Imputation and Outcome Prediction for Achilles Tendon Rupture Rehabilitation

Charles Hamesse, Ruibo Tu, Paul Ackermann, Hedvig Kjellström, Cheng Zhang

Enhancing high-content imaging for studying microtubule networks at large-scale

Hao-Chih Lee, Sarah T. Cherng, Riccardo Miotto, Joel T. Dudley

Multi-view Multi-task Learning for Improving Autonomous Mammogram Diagnosis

Trent Kyono, Fiona J. Gilbert, Mihaela Schaar

Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images

David Dov, Shahar Z. Kovalsky, Jonathan Cohen, Danielle Elliott Range, Ricardo Henao

Few-Shot Learning for Dermatological Disease Diagnosis

Viraj Prabhu, Anitha Kannan, Murali Ravuri, Manish Chaplain, David Sontag

EEGtoText: Learning to Write Medical Reports from EEG Recordings

Siddharth Biswal, Cao Xiao, M. Brandon Westover, Jimeng Sun

The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records

Linying Zhang, Yixin Wang, Anna Ostropolets, Jami J. Mulgrave, David M. Blei

Using Domain Knowledge to Overcome Latent Variables in Causal Inference from Time Series

Min Zheng, Samantha Kleinberg

ASAC: Active Sensing using Actor-Critic models

Jinsung Yoon, James Jordon, Mihaela Schaar

A Calibration Metric for Risk Scores with Survival Data

Steve Yadlowsky, Sanjay Basu, Lu Tian

Are Online Reviews of Physicians Biased Against Female Providers?

Avijit Thawani, Michael J. Paul, Urmimala Sarkar, Byron C. Wallace

Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks

Bret Nestor, Matthew B. A. McDermott, Willie Boag, Gabriela Berner, Tristan Naumann

What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use

Sana Tonekaboni, Shalmali Joshi, Melissa D. McCradden, Anna Goldenberg

Counterfactual Reasoning for Fair Clinical Risk Prediction

Stephen R. Pfohl, Tony Duan, Daisy Yi Ding, Nigam H. Shah

Phenotype Inference with Semi-Supervised Mixed Membership Models

Victor A. Rodriguez, Adler Perotte

Predicting Phase 3 Clinical Trial Results by Modeling Phase 2 Clinical Trial Subject Level Data Using Deep Learning

Youran Qi, Qi Tang

Predicting Sick Patient Volume in a Pediatric Outpatient Setting using Time Series Analysis

Grace Guan, Barbara E. Engelhardt

A Neural Model for Predicting Dementia from Language

Weirui Kong, Hyeju Jang, Giuseppe Carenini, Thalia Field

Clinically Accurate Chest X-Ray Report Generation

Guanxiong Liu, Tzu-Ming Harry Hsu, Matthew McDermott, Willie Boag, Wei-Hung Weng

Self-Attention Based Molecule Representation for Predicting Drug-Target Interaction

Bonggun Shin, Sungsoo Park, Keunsoo Kang, Joyce C. Ho

Clinical Judgement Study using Question Answering from Electronic Health Records

Bhanu Pratap Singh Rawat, Fe Li, Hong Yu

Multimodal Machine Learning for Automated ICD Coding

Keyang Xu, Mike Lam, Jingzhi Pang, Xin Gao, Charlotte Band

Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes

Ognjen (Oggi) Rudovic, Yuria Utsumi, Ricardo Guerrero, Kelly Peterson, Daniel Rueckert

Temporal Graph Convolutional Networks for Automatic Seizure Detection

Ian C. Covert, Balu Krishnan, Imad Najm, Jiening Zhan, Matthew Shore

A Spatiotemporal Approach to Predicting Glaucoma Progression Using a CT-HMM

Supriya Nagesh, Alexander Moreno, Hiroshi Ishikawa, Gadi Wollstein, Joel S. Shuman

Multiple Instance Learning for ECG Risk Stratification

Divya Shanmugam, Davis Blalock, John Guttag

Dynamically Personalized Detection of Hemorrhage

Chirag Nagpal, Xinyu Li, Michael R. Pinsky, Artur Dubrawski

Using Contextual Information to Improve Blood Glucose Prediction

Mohammad Akbari, Rumi Chunara

Multi-Task Gaussian Processes and Dilated Convolutional Networks for Reconstruction of Reproductive Hormonal Dynamics

Inigo Urteaga, Tristan Bertin, Theresa M. Hardy, David J. Albers, Noemie Elhadad

FLARe: Forecasting by Learning Anticipated Representations

Surya Teja Devarakonda, Joie Yeahuay Wu, Yi Ren Fung, Madalina Fiterau

Relaxed Parameter Sharing: Effectively Modeling Time-Varying Relationships in Clinical Time-Series

Jeeheh Oh, Jiaxuan Wang, Shengpu Tang, Michael W. Sjoding, Jenna Wiens

Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping

Michael Moor, Max Horn, Bastian Rieck, Damian Roqueiro, Karsten Borgwardt