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Machine Learning for Healthcare

Home
MLHC 2025 Info
2025 Conference
Conference Registration
Travel & Accommodations
Call for Papers
Sponsorship Information
Program Chairs
Past Conferences
2016 Conference
2017 Conference
2018 Conference
2019 Conference
2020 Conference
2020 Accepted Papers
2021 Conference
2021 Recorded Talks
2021 Accepted Papers
2022 Conference
2022 Recorded Talks
2022 Accepted Papers
2023 Conference
2023 Accepted Papers
2024 Conference
2024 Abstracts
  • Past Conferences
  • 2016 Conference
  • 2017 Conference
  • 2018 Conference
  • 2019 Conference
  • 2020 Conference
  • 2020 Accepted Papers
  • 2021 Conference
  • 2021 Recorded Talks
  • 2021 Accepted Papers
  • 2022 Conference
  • 2022 Recorded Talks
  • 2022 Accepted Papers
  • 2023 Conference
  • 2023 Accepted Papers
  • 2024 Conference
  • 2024 Abstracts
  • Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settings

    Shengpu Tang, Jenna Wiens

  • Knowledge Graph-based Question Answering with Electronic Health Records

    Junwoo Park, Youngwoo Cho, Haneol Lee, Jaegul Choo, Edward Choi

  • Uncertainty-Aware Time-to-Event Prediction using Deep Kernel Accelerated Failure Time Models

    Zhiliang Wu, Yinchong Yang, Peter A. Fashing, Volker Tresp

  • Directing Human Attention in Event Localization for Clinical Timeline Creation

    Jason Zhao, Monica Agrawal, Pedram Razavi, David Sontag

  • CheXbreak: Misclassification Identification for Deep Learning Models Interpreting Chest X-rays

    Emma Chen, Andy Kim, Rayan Krishnan, Jin Long, Andrew Y. Ng, Pranav Rajpurkar;

  • Understanding Clinical Collaborations Through Federated Classifier Selection

    Sebastian Caldas, Joo Heung Yoon, Michael R. Pinsky, Gilles Clermont, Artur Dubrawski

  • Deep Generative Analysis for Task-Based Functional MRI Experiments

    Daniela de Albuquerque, Jack Goffinet, Rachael Wright, John Pearson

  • Detecting Atrial Fibrillation in ICU Telemetry data with Weak Labels

    Brian Chen, Golara Javadi, Amoon Jamzad, Alexander Hamilton, Stephanie Sibley, Purang Abolmaesumi, David Maslove, Parvin Mousavi

  • Read, Attend, and Code: Pushing the Limits of Medical Codes Prediction from Clinical Notes by Machines

    Byung-Hak Kim, Varun Ganapathi

  • Power Constrained Bandits

    Jiayu Yao, Emma Brunskill, Weiwei Pan, Susan Murphy, Finale Doshi-Velez

  • EVA: Generating Longitudinal Electronic Health Records Using Conditional Variational Autoencoders

    Siddharth Biswal, Soumya Ghosh, Jon Duke, Bradley Malin, Walter Stewart, Cao Xiao, Jimeng Sun

  • Intraoperative Adverse Event Detection in Laparoscopic Surgery: Stabilized Multi-Stage Temporal Convolutional Network with Focal-Uncertainty Loss

    Haiqi Wei, Frank Rudzicz, David Fleet, Teodor Grantcharov, Babak Taati

  • Model-based metrics: Sample-efficient estimates of predictive model subpopulation performance

    Andrew C. Miller, Leon A. Gatys, Joseph Futoma, Emily Fox

  • An Interpretable Framework for Drug-Target Interaction with Gated Cross Attention

    Yeachan Kim, Bonggun Shin

     

  • Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization

    Bharath Chintagunta, Namit Katariya, Xavier Amatriain, Anitha Kannan

  • Risk score learning for COVID-19 contact tracing apps

    Kevin Murphy, Abhishek Kumar, Stylianos Serghiou

  • MIMIC-SBDH: A Dataset for Social and Behavioral Determinants of Health

    Hiba Ahsan, Emmie Ohnuki, Avijit Mitra, Hong You

  • In-depth Benchmarking of Deep Neural Network Architectures for ECG Diagnosis

    Naoki Nonaka, Jun Seita

  • Hierarchical Information Criterion for Variable Abstraction

    Mark Mirtchouk, Bharat Srikishan, Samantha Kleinberg

  • Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographs

    Nasir Hayat, Hazem Lashen, Farah E. Shamout

  • Incorporating External Information in Tissue Subtyping: A Topic Modeling Approach

    Ardvan Saeedi, Payman Yadollahpour, Sumedha Singla, Brian Pollack, William Wells, Frank Sciurba, Kayhan Batmanghelich

  • Mind the Performance Gap: Examining Dataset Shift During Prospective Validation

    Erkin Otles, Jeeheh Oh, Benjamin Li, Michelle Bochinski, Hyeon Joo, Justin Ortwine, Erica Shenoy, Laraine Washer, Vincent B. Young, Krishna Rao, Jenna Wiens

  • A Generative Modeling Approach to Calibrated Predictions: A Use Case on Menstrual Cycle Length Prediction

    Inigo Urteaga, Kathy Li, Amanda Shea, Virginia J. Vitzthum, Chris H. Wiggins, Noemie Elhadad

  • Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories

    Gian Marco Visani, Alexandra Hope Lee, Cuong Nguyen, David M. Kent, John B. Wong, Joshua T. Cohen, Michael C. Hughes

  • A New Semi-supervised Learning Benchmark for Classifying View and Diagnosing Aortic Stenosis from Echocardiograms

    Zhe Huang, Gary Long, Benjamin Wessler, Michael C. Hughes

  • Dynamic Survival Analysis for EHR Data with Personalized Parametric Distributions

    Preston Putzel, Hyungrok Do, Alex Boyd, Hua Zhong, Padhraic Smyth

  • Deep Cox Mixtures for Survival Regression

    Chirag Nagpal, Steve Yadlowsky, Negar Rostamzadeh, Katherine Heller

  • Stool Image Analysis for Precision Health Monitoring by Smart Toilets

    Jin Zhou, Nick DeCapite, Jackson McNabb, Jose R. Ruiz, Deborah A. Fisher, Sonia Grego, Krishnendu Chakrabarty

  • Back to the basics with inclusion of clinical domain knowledge - A simple, scalable and effective model of Alzheimer’s Disease classification

    Sarah C. Brüningk, Felix Hensel, Louis P. Lukas, Merel Kuijs, Catherine R. Jutzeler, Bastian Rieck

  • MedAug: Contrastive learning leveraging patient metadata improves representations for chest X-ray interpretation

    Yen Nhi Truong Vu, Richard Wang, Niranjan Balachandar, Can Liu, Andrew Y. Ng, Pranav Rajpurkar

  • Point Processes for Competing Observations with Recurrent Networks (POPCORN): A Generative Model of EHR Data

    Shreyas Bhave, Adler Perotte

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