MLHC 2020

41 papers

Preparing a Clinical Support Model for Silent Mode in General Internal Medicine

Bret Nestor, Liam G. McCoy, Amol Verma, Chloé Pou-Prom, Joshua Murray

Robust Benchmarking for Machine Learning of Clinical Entity Extraction

Monica Agrawal, Chloe O’Connell, Yasmin Fatemi, Ariel Levy, David Sontag

CheXpert++: Approximating the CheXpert Labeler for Speed, Differentiability, and Probabilistic Output

Matthew B.A. McDermott, Tzu Ming Harry Hsu, Wei-Hung Weng, Marzyeh Ghassemi, Peter Szolovits

UPSTAGE: Unsupervised Context Augmentation for Utterance Classification in Patient-Provider Communication

Do June Min, Veronica Perez-Rosas, Shihchen Kuo, William H. Herman, Rada Mihalcea

Comparing Machine Learning Techniques for Blood Glucose Forecasting Using Free-living and Patient Generated Data

Hadia Hameed, Samantha Kleinberg

Fast, Structured Clinical Documentation via Contextual Autocomplete

Divya Gopinath, Monica Agrawal, Luke Murray, Steven Horng, David Karger

Non-Invasive Classification of Alzheimer’s Disease Using Eye Tracking and Language

Oswald Barral, Hyeju Jang, Sally Newton-Mason, Sheetal Shajan, Thomas Soroski

Clinical Collabsheets: 53 Questions to Guide a Clinical Collaboration

Shems Saleh, William Boag, Lauren Erdman, Tristan Naumann

Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts

Sarah Jabbour, David Fouhey, Ella Kazerooni, Michael W. Sjoding, Jenna Wiens

Self-Supervised Pretraining with DICOM metadata in Ultrasound Imaging

Szu-Yen Hu, Shuhang Wang, Wei-Hung Weng, Jingchao Wang, XiaoHong Wang

Hidden Risks of Machine Learning Applied to Healthcare: Unintended Feedback Loops Between Models and Future Data Causing Model Degradation

George Alexandru Adam, Chun-Hao Kingsley Chang, Benjamin Haibe-Kains, Anna Goldenberg

Using deep networks for scientific discovery in physiological signals

Tom Beer, Bar Eini-Porat, Sebastian Goodfellow, Danny Eytan, Uri Shalit

Predicting Drug Sensitivity of Cancer Cell Lines via Collaborative Filtering with Contextual Attention

Yifeng Tao, Shuangxia Ren, Michael Q. Ding, Russell Schwartz, Xinghua Lu

Query-Focused EHR Summarization to Aid Imaging Diagnosis

Denis Jered McInerney, Borna Dabiri, Anne-Sophie Touret, Geoffrey Young, Jan-Willem Meent

Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations

Benjamin Schloss, Sandeep Konam

Transfer Learning from Well-Curated to Less-Resourced Populations with HIV

Sonali Parbhoo, Mario Wieser, Volker Roth, Finale Doshi-velez

Time-Aware Transformer-based Network for Clinical Notes Series Prediction

Dongyu Zhang, Jidapa Thadajarassiri, Cansu Sen, Elke Rundensteiner

Deep Kernel Survival Analysis and Subject-Specific Survival Time Prediction Intervals

George H. Chen

Deep Reinforcement Learning for Closed-Loop Blood Glucose Control

Ian Fox, Joyce Lee, Rodica Pop-Busui, Jenna Wiens

Attention-Based Network for Weak Labels in Neonatal Seizure Detection

Dmitry Yu. Isaev, Dmitry Tchapyjnikov, C. Michael Cotten, David Tanaka, Natalia Martinez

Attentive Adversarial Network for Large-Scale Sleep Staging

Samaneh Nasiri, Gari D. Clifford

Students Need More Attention: BERT-based Attention Model for Small Data with Application to Automatic Patient Message Triage

Shijing Si, Rui Wang, Jedrek Wosik, Hao Zhang, David Dov

Evaluating and interpreting caption prediction for histopathology images

Renyu Zhang, Christopher Weber, Robert Grossman, Aly A. Khan

Comparisons Between Hamiltonian Monte Carlo and Maximum A Posteriori For A Bayesian Model For Apixaban Induction Dose & Dose Personalization

A. Demetri Pananos, Daniel J. Lizotte

A Causally Formulated Hazard Ratio Estimation through Backdoor Adjustment on Structural Causal Model

Riddhiman Adib, Paul Griffin, Sheikh Iqbal Ahamed, Mohammad Adibuzzaman

Addressing Sample Size Challenges in Linked Data Through Data Fusion

Srikesh Arunajadai, Lulu Lee, Tom Haskell

Phenotyping with Prior Knowledge using Patient Similarity

Asif Rahman, Yale Chang, Bryan Conroy, Minnan Xu-Wilson

Personalized Input-Output Hidden Markov Models for Disease Progression Modeling

Kristen A. Severson, Lana M. Chahine, Luba Smolensky, Kenney Ng, Jianying Hu

MRI-based Diagnosis of Rotator Cuff Tears using Deep Learning and Weighted Linear Combinations

Mijung Kim, Ho-min Park, Jae Yoon Kim, Seong Hwan Kim, Sofie Hoeke

Differentially Private Survival Function Estimation

Lovedeep Gondara, Ke Wang

Dynamically Extracting Outcome-Specific Problem Lists from Clinical Notes with Guided Multi-Headed Attention

Justin Lovelace, Nathan C. Hurley, Adrian D. Haimovich, Bobak J. Mortazavi

Neural Conditional Event Time Models

Matthew Engelhard, Samuel Berchuck, Joshua D’Arcy, Ricardo Henao

Knowledge Base Completion for Constructing Problem-Oriented Medical Records

James Mullenbach, Jordan Swartz, T. Greg McKelvey, Hui Dai, David Sontag

Learning Insulin-Glucose Dynamics in the Wild

Andrew C. Miller, Nicholas J. Foti, Emily Fox

Towards data-driven stroke rehabilitation via wearable sensors and deep learning

Aakash Kaku, Avinash Parnandi, Anita Venkatesan, Natasha Pandit, Heidi Schambra

Optimizing Influenza Vaccine Composition: From Predictions to Prescriptions

Hari Bandi, Dimitris Bertsimas

Developing Personalized Models of Blood Pressure Estimation from Wearable Sensors Data Using Minimally-trained Domain Adversarial Neural Networks

Lida Zhang, Nathan C. Hurley, Bassem Ibrahim, Erica Spatz, Harlan M. Krumholz

Towards Early Diagnosis of Epilepsy from EEG Data

Diyuan Lu, Sebastian Bauer, Valentin Neubert, Laura Sophie Costard, Felix Rosenow

An Evaluation of the Doctor-Interpretability of Generalized Additive Models with Interactions

Stefan Hegselmann, Thomas Volkert, Hendrik Ohlenburg, Antje Gottschalk, Martin Dugas

ScanMap: Supervised Confounding Aware Non-negative Matrix Factorization for Polygenic Risk Modeling

Yuan Luo, Chengsheng Mao

Learning to Ask Medical Questions using Reinforcement Learning

Uri Shaham, Tom Zahavy, Cesar Caraballo, Shiwani Mahajan, Daisy Massey