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ORCIDMLHC 2020: Virtual Event, Durham, NC, USA
- Finale Doshi-Velez, Jim Fackler, Ken Jung, David C. Kale, Rajesh Ranganath, Byron C. Wallace, Jenna Wiens:
Proceedings of the Machine Learning for Healthcare Conference, MLHC 2020, 7-8 August 2020, Virtual Event, Durham, NC, USA. Proceedings of Machine Learning Research 126, PMLR 2020 - Uri Shaham, Tom Zahavy, Cesar Caraballo, Shiwani Mahajan, Daisy Massey, Harlan M. Krumholz:
Learning to Ask Medical Questions using Reinforcement Learning. 2-26 - Yuan Luo, Chengsheng Mao:
ScanMap: Supervised Confounding Aware Non-negative Matrix Factorization for Polygenic Risk Modeling. 27-45 - Stefan Hegselmann, Thomas Volkert, Hendrik Ohlenburg, Antje Gottschalk, Martin Dugas, Christian Ertmer:
An Evaluation of the Doctor-Interpretability of Generalized Additive Models with Interactions. 46-79 - Diyuan Lu, Sebastian Bauer, Valentin Neubert, Lara Sophie Costard, Felix Rosenow, Jochen Triesch:
Towards Early Diagnosis of Epilepsy from EEG Data. 80-96 - Lida Zhang, Nathan C. Hurley, Bassem Ibrahim, Erica S. Spatz, Harlan M. Krumholz, Roozbeh Jafari, Bobak Jack Mortazavi:
Developing Personalized Models of Blood Pressure Estimation from Wearable Sensors Data Using Minimally-trained Domain Adversarial Neural Networks. 97-120 - Hari Bandi, Dimitris Bertsimas:
Optimizing Influenza Vaccine Composition: From Predictions to Prescriptions. 121-142 - Aakash Kaku, Avinash Parnandi, Anita Venkatesan, Natasha Pandit, Heidi M. Schambra, Carlos Fernandez-Granda:
Towards data-driven stroke rehabilitation via wearable sensors and deep learning. 143-171 - Andrew C. Miller, Nicholas J. Foti, Emily B. Fox:
Learning Insulin-Glucose Dynamics in the Wild. 172-197 - James Mullenbach, Jordan Swartz, T. Greg McKelvey, Hui Dai, David A. Sontag:
Knowledge Base Completion for Constructing Problem-Oriented Medical Records. 198-222 - Matthew Engelhard, Samuel Berchuck, Joshua D'Arcy, Ricardo Henao:
Neural Conditional Event Time Models. 223-244 - Justin R. Lovelace, Nathan C. Hurley, Adrian D. Haimovich, Bobak J. Mortazavi:
Dynamically Extracting Outcome-Specific Problem Lists from Clinical Notes with Guided Multi-Headed Attention. 245-270 - Lovedeep Gondara, Ke Wang:
Differentially Private Survival Function Estimation. 271-291 - Mijung Kim, Ho-min Park, Jae Yoon Kim, Seong Hwan Kim, Sofie Hoeke, Wesley De Neve:
MRI-based Diagnosis of Rotator Cuff Tears using Deep Learning and Weighted Linear Combinations. 292-308 - Kristen A. Severson, Lana M. Chahine, Luba Smolensky, Kenney Ng, Jianying Hu, Soumya Ghosh:
Personalized Input-Output Hidden Markov Models for Disease Progression Modeling. 309-330 - Asif Rahman, Yale Chang, Bryan Conroy, Minnan Xu-Wilson:
Phenotyping with Prior Knowledge using Patient Similarity. 331-351 - Srikesh Arunajadai, Lulu Lee, Tom Haskell:
Addressing Sample Size Challenges in Linked Data Through Data Fusion. 352-375 - Riddhiman Adib, Paul M. Griffin, Sheikh Iqbal Ahamed, Mohammad Adibuzzaman:
A Causally Formulated Hazard Ratio Estimation through Backdoor Adjustment on Structural Causal Model. 376-396 - Athanasios Demetri Pananos, Daniel J. Lizotte:
Comparisons Between Hamiltonian Monte Carlo and Maximum A Posteriori For A Bayesian Model For Apixaban Induction Dose & Dose Personalization. 397-417 - Renyu Zhang, Christopher Weber, Robert L. Grossman, Aly A. Khan:
Evaluating and interpreting caption prediction for histopathology images. 418-435 - Shijing Si, Rui Wang, Jedrek Wosik, Hao Zhang, David Dov, Guoyin Wang, Lawrence Carin:
Students Need More Attention: BERT-based Attention Model for Small Data with Application to Automatic Patient Message Triage. 436-456 - Samaneh Nasiri, Gari D. Clifford:
Attentive Adversarial Network for Large-Scale Sleep Staging. 457-478 - Dmitry Yu. Isaev, Dmitry Tchapyjnikov, C. Michael Cotten, David Tanaka, Natalia Martínez, Martín Bertrán, Guillermo Sapiro, David E. Carlson:
Attention-Based Network for Weak Labels in Neonatal Seizure Detection. 479-507 - Ian Fox, Joyce M. Lee, Rodica Pop-Busui, Jenna Wiens:
Deep Reinforcement Learning for Closed-Loop Blood Glucose Control. 508-536 - George H. Chen:
Deep Kernel Survival Analysis and Subject-Specific Survival Time Prediction Intervals. 537-565 - Dongyu Zhang
, Jidapa Thadajarassiri, Cansu Sen, Elke A. Rundensteiner:
Time-Aware Transformer-based Network for Clinical Notes Series Prediction. 566-588 - Sonali Parbhoo, Mario Wieser, Volker Roth, Finale Doshi-Velez:
Transfer Learning from Well-Curated to Less-Resourced Populations with HIV. 589-609 - Benjamin Schloss, Sandeep Konam:
Towards an Automated SOAP Note: Classifying Utterances from Medical Conversations. 610-631 - Denis Jered McInerney, Borna Dabiri, Anne-Sophie Touret, Geoffrey Young, Jan-Willem van de Meent, Byron C. Wallace:
Query-Focused EHR Summarization to Aid Imaging Diagnosis. 632-659 - Yifeng Tao, Shuangxia Ren, Michael Q. Ding, Russell Schwartz, Xinghua Lu:
Predicting Drug Sensitivity of Cancer Cell Lines via Collaborative Filtering with Contextual Attention. 660-684 - Tom Beer, Bar Eini-Porat, Sebastian Goodfellow, Danny Eytan, Uri Shalit:
Using deep networks for scientific discovery in physiological signals. 685-709 - George-Alexandru Adam, Chun-Hao Kingsley Chang, Benjamin Haibe-Kains, Anna Goldenberg:
Hidden Risks of Machine Learning Applied to Healthcare: Unintended Feedback Loops Between Models and Future Data Causing Model Degradation. 710-731 - Szu-Yeu Hu, Shuhang Wang, Wei-Hung Weng, Jingchao Wang, Xiaohong Wang, Arinc Ozturk, Quan Li, Viksit Kumar, Anthony E. Samir:
Self-Supervised Pretraining with DICOM metadata in Ultrasound Imaging. 732-749 - Sarah Jabbour, David Fouhey, Ella Kazerooni, Michael W. Sjoding, Jenna Wiens:
Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts. 750-782 - Shems Saleh, William Boag, Lauren Erdman, Tristan Naumann:
Clinical Collabsheets: 53 Questions to Guide a Clinical Collaboration. 783-812 - Oswald Barral, Hyeju Jang, Sally Newton-Mason, Sheetal Shajan, Thomas Soroski, Giuseppe Carenini, Cristina Conati, Thalia Shoshana Field:
Non-Invasive Classification of Alzheimer's Disease Using Eye Tracking and Language. 813-841 - Divya Gopinath, Monica Agrawal, Luke S. Murray, Steven Horng, David R. Karger, David A. Sontag:
Fast, Structured Clinical Documentation via Contextual Autocomplete. 842-870 - Hadia Hameed, Samantha Kleinberg:
Comparing Machine Learning Techniques for Blood Glucose Forecasting Using Free-living and Patient Generated Data. 871-894 - Do June Min, Verónica Pérez-Rosas, Shihchen Kuo, William H. Herman, Rada Mihalcea:
UPSTAGE: Unsupervised Context Augmentation for Utterance Classification in Patient-Provider Communication. 895-912 - Matthew B. A. McDermott, Tzu-Ming Harry Hsu, Wei-Hung Weng, Marzyeh Ghassemi, Peter Szolovits:
CheXpert++: Approximating the CheXpert Labeler for Speed, Differentiability, and Probabilistic Output. 913-927 - Monica Agrawal, Chloe O'Connell, Yasmin Fatemi, Ariel Levy, David A. Sontag:
Robust Benchmarking for Machine Learning of Clinical Entity Extraction. 928-949 - Bret Nestor, Liam G. McCoy, Amol Verma, Chloé Pou-Prom, Joshua Murray, Sebnem Kuzulugil, David Dai, Muhammad Mamdani, Anna Goldenberg, Marzyeh Ghassemi:
Preparing a Clinical Support Model for Silent Mode in General Internal Medicine. 950-972
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