Denoising Autoencoders for Learning from Noisy Patient-Reported Data
Harry Rubin-Falcone* (University of Michigan); Joyce Lee (University of Michigan); Jenna Wiens (University of Michigan)
Adaptive Weighted Multi-View Clustering
Shuo Shuo Liu* (Pennsylvania State University); Lin Lin (Duke University)
Token Imbalance Adaptation for Radiology Report Generation
Yuexin Wu* (University of Memphis); I-Chan Huang (St. Jude Children's Research Hospital); Xiaolei Huang (University of Memphis)
Federated Multilingual Models for Medical Transcript Analysis
Andre Manoel* (Microsoft); Mirian Del Carmen Hipolito Garcia (Microsoft); Tal Baumel (Microsoft); Shize Su (Microsoft); Jialei Chen (Microsoft); Robert Sim (Microsoft); Dan Miller (Airbnb); Danny Karmon (Google); Dimitrios Dimitriadis (Amazon)
Rare Life Event Detection via Mobile Sensing Using Multi-Task Learning
Arvind Pillai* (Dartmouth College); Subigya Nepal (Dartmouth College); Andrew Campbell (Dartmouth College)
Virus2Vec: Viral Sequence Classification Using Machine Learning
Sarwan Ali* (Georgia State University); Babatunde Bello (Georgia State University); Prakash Chourasia (Georgia State University); Ria Thazhe Punathil (Georgia State University); Pin-Yu Chen (IBM Research); Imdad Ullah Khan (Lahore University of Management Sciences); Murray Patterson (Georgia State University)
A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular Information
George H. Chen* (Carnegie Mellon University)
Towards the Practical Utility of Federated Learning in the Medical Domain
Hyeonji Hwang* (KAIST); Seongjun Yang (KRAFTON); Daeyoung Kim (KAIST); Radhika Dua (Google Research); Jong-Yeup Kim(Konyang University); Eunho Yang (KAIST) ; Edward Choi (KAIST)
Large-Scale Study of Temporal Shift in Health Insurance Claims
Christina X Ji (MIT CSAIL and IMES); Ahmed Alaa (UC Berkeley and UCSF); David Sontag (MIT CSAIL and IMES)
Semantic match: Debugging feature attribution methods in XAI for healthcare
Giovanni Cinà* (Amsterdam University Medical Center); Tabea E. Röber (University of Amsterdam); Rob Goedhart (University of Amsterdam); Ş. İlker Birbil (University of Amsterdam)
Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models
Siyi Tang* (Stanford University); Jared A. Dunnmon (Stanford University); Liangqiong Qu (University of Hong Kong); Khaled K. Saab (Stanford University); Tina Baykaner (Stanford University); Christopher Lee-Messer (Stanford University); Daniel L. Rubin (Stanford University)
Neural Fine-Gray: Monotonic neural networks for competing risks
Vincent Jeanselme* (University of Cambridge); Chang Ho Yoon (University of Oxford); Brian Tom (University of Cambridge); Jessica Barrett (University of Cambridge)
Multi-modal Pre-training for Medical Vision-language Understanding and Generation: An Empirical Study with A New Benchmark
Li Xu* (Hong Kong Polytechnic University); Bo Liu (Hong Kong Polytechnic University); Ameer Hamza Khan (Hong Kong Polytechnic University); Lu Fan (Hong Kong Polytechnic University); Xiao-Ming Wu (Hong Kong Polytechnic University)
SRDA: Mobile Sensing based Fluid Overload Detection for End Stage Kidney Disease Patients using Sensor Relation Dual Autoencoder
Mingyue Tang (University of Virginia); Jiechao Gao* (University of Virginia); Guimin Dong (Amazon); Carl Yang (Emory University); Brad Campbell (University of Virginia); Brendan Bowman (University of Virginia); Jamie Marie Zoellner (University of Virginia); Emaad Abdel-Rahman (University of Virginia); Mehdi Boukhechba (The Janssen Pharmaceutical Companies of Johnson & Johnson)
Who Controlled the Evidence? Question Answering for Disclosure Information Retrieval
Hardy* (Universitas Mikroskil); Derek Ruths (McGill University); Nicholas B King (McGill University)
Revisiting Machine-Learning based Drug Repurposing: Drug Indications Are Not a Right Prediction Target
Siun Kim* (Seoul National University); Jung-Hyun Won (Seoul National University); David Seung U Lee (Seoul National University); Renqian Luo (Microsoft Research); Lijun Wu (Microsoft Research); Yingce Xia (Microsoft Research); Tao Qin (Microsoft Research); Howard Lee (Seoul National University)
Bayesian Active Questionnaire Design for Cause-of-Death Assignment Using Verbal Autopsies
Toshiya Yoshida* (University of California Santa Cruz); Trinity Shuxian Fan (University of Washington); Tyler McCormick (University of Washington); Zhenke Wu (University of Michigan); Zehang Richard Li (University of California Santa Cruz)
Machine Learning for Arterial Blood Pressure Prediction
Jessica Zheng (MIT); Hanrui Wang* (MIT); Anand Chandrasekhar (MIT); Aaron Aguirre (Massachusetts General Hospital and Harvard Medical School); Song Han (MIT); Hae-Seung Lee (MIT); Charles G. Sodini (MIT)
MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction
Iman Deznabi* (University of Massachusetts, Amherst); Madalina Fiterau (University of Massachusetts, Amherst)
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?
Zhi Chen* (Duke University); Sarah Tan (Cornell University); Urszula Chajewska (Microsoft Research); Cynthia Rudin (Duke University); Rich Caruana (Microsoft Research)
Explaining a machine learning decision to physicians via counterfactuals
Supriya Nagesh* (Amazon); Nina Mishra (Amazon); Yonatan Naamad (Amazon); James M Rehg (Georgia Institute of Technology); Mehul A Shah (Aryn); Alexei Wagner (Harvard University)
Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise
Donna Tjandra* (University of Michigan); Jenna Wiens (University of Michigan)
Fair Admission Risk Prediction with Proportional Multicalibration
William La Cava* (Boston Children's Hospital and Harvard Medical School); Elle Lett (Boston Children's Hospital and Harvard Medical School); Guangya Wan (Boston Children's Hospital and Harvard Medical School)
Collecting data when missingness is unknown: a method for improving model performance given under-reporting in patient populations
Kevin Wu* (Stanford University and Optum Labs); Dominik Dahlem (Optum Labs); Christopher Hane (Optum Labs); Eran Halperin (Optum Labs); James Zou (Stanford University)
Self-Supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets
Mike A Merrill* (University of Washington); Tim Althoff (University of Washington)
Clinical Relevance Score for Guided Trauma Injury Pattern Discovery with Weakly Supervised β-VAE
Qixuan Jin* (Massachusetts Institute of Technology); Jacobien Oosterhoff (Delft University of Technology); Yepeng Huang (Harvard School of Public Health); Marzyeh Ghassemi (Massachusetts Institute of Technology); Gabriel A. Brat (Beth Israel Deaconess Medical Center and Harvard Medical School)
Evaluating Model Performance in Medical Datasets Over Time
Helen Zhou* (Carnegie Mellon University); Yuwen Chen (Carnegie Mellon University); Zachary Chase Lipton (Carnegie Mellon University)
Rediscovery of CNN's Versatility for Text-based Encoding of Raw Electronic Health Records
Eunbyeol Cho* (KAIST); Min Jae Lee (KAIST); Kyunghoon Hur (KAIST); Jiyoun Kim (KAIST); Jinsung Yoon (Google Cloud AI Research); Edward Choi (KAIST)
Homekit2020: A Benchmark for Time Series Classification on a Large Mobile Sensing Dataset with Laboratory Tested Ground Truth of Influenza Infections
Mike A Merrill (University of Washington); Esteban Safranchik* (University of Washington); Arinbjörn Kolbeinsson (Evidation Health); Piyusha Gade (Evidation Health); Ernesto Ramirez (Evidation Health); Ludwig Schmidt (University of Washington); Luca Foschini (Sage Bionetworks); Tim Althoff (University of Washington)
PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis
Yi Yang* (Emory University); Hejie Cui (Emory University); Carl Yang (Emory University)
Do We Still Need Clinical Language Models?
Eric Lehman* (MIT and Xyla); Evan Hernandez (MIT and Xyla); Diwakar Mahajan (IBM Research); Jonas Wulff (Xyla); Micah J. Smith (Xyla); Zachary Ziegler (Xyla); Daniel Nadler (Xyla); Peter Szolovits (MIT); Alistair Johnson (The Hospital for Sick Children); Emily Alsentzer (Brigham and Women's Hospital and Harvard Medical School)
Contrastive Learning of Electrodermal Activity Representations for Stress Detection
Katie Matton* (MIT CSAIL and MIT Media Lab); Robert A Lewis* (MIT Media Lab); John Guttag (MIT CSAIL); Rosalind Picard (MIT Media Lab)
Understanding and Predicting the Effect of Environmental Factors on People with Type 2 Diabetes
Kailas Vodrahalli* (Stanford University); Gregory D. Lyng (Optum AI Labs); Brian L. Hill (Optum AI Labs); Kimmo Karkkainen (Optum AI Labs); Jeffrey Hertzberg (Optum AI Labs); James Zou (Stanford University); Eran Halperin (Optum AI Labs)