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ORCIDML4H@NeurIPS 2022: New Orleans, LA, USA
- Antonio Parziale, Monica Agrawal, Shalmali Joshi, Irene Y. Chen, Shengpu Tang, Luis Oala, Adarsh Subbaswamy:
Machine Learning for Health, ML4H 2022, 28 November 2022, New Orleans, Lousiana, USA & Virtual. Proceedings of Machine Learning Research 193, PMLR 2022 - Antonio Parziale, Monica Agrawal, Shengpu Tang, Kristen Severson, Luis Oala, Adarsh Subbaswamy, Sayantan Kumar, Elora D. M. Schörverth, Stefan Hegselmann, Helen Zhou, Ghada Zamzmi, Purity Mugambi, Elena Sizikova, Girmaw Abebe Tadesse, Yuyin Zhou, Taylor W. Killian, Haoran Zhang, Fahad Kamran, Andrea Hobby, Mars Huang, Ahmed M. Alaa, Harvineet Singh, Irene Y. Chen, Shalmali Joshi:
Machine Learning for Health (ML4H) 2022. 1-11 - Vincent Jeanselme, Maria De-Arteaga, Zhe Zhang, Jessica K. Barrett, Brian D. M. Tom:
Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness. 12-34 - Mihir Kulkarni, Satvik Golechha, Rishi Raj, Jithin K. Sreedharan, Ankit Bhardwaj
, Santanu Rathod, Bhavin Vadera, Jayakrishna Kurada, Sanjay Mattoo, Rajendra Joshi, Kirankumar Rade, Alpan Raval:
Predicting Treatment Adherence of Tuberculosis Patients at Scale. 35-61 - Shu Hu, George H. Chen:
Distributionally Robust Survival Analysis: A Novel Fairness Loss Without Demographics. 62-87 - Charles Gadd, Krishnarajah Nirantharakumar, Christopher Yau:
mmVAE: multimorbidity clustering using Relaxed Bernoulli β-Variational Autoencoders. 88-102 - Woojung Kim, Paul A. Jenkins, Christopher Yau:
Feature Allocation Approach for Multimorbidity Trajectory Modelling. 103-119 - Haiyi Mao, Hongfu Liu, Jason Xiaotian Dou, Panayiotis V. Benos:
Towards Cross-Modal Causal Structure and Representation Learning. 120-140 - Peniel N. Argaw, Elizabeth Healey, Isaac S. Kohane:
Identifying Heterogeneous Treatment Effects in Multiple Outcomes using Joint Confidence Intervals. 141-170 - Jay Jojo Cheng, Jared D. Huling, Guanhua Chen:
Meta-analysis of individualized treatment rules via sign-coherency. 171-198 - Iva Bojic, Qi Chwen Ong, Megh Thakkar, Esha Kamran, Irving Yu Le Shua, Jaime Rei Ern Pang, Jessica Chen, Vaaruni Nayak, Shafiq R. Joty, Josip Car:
SleepQA: A Health Coaching Dataset on Sleep for Extractive Question Answering. 199-217 - Joel Stremmel, Brian L. Hill, Jeffrey Hertzberg, Jaime Murillo, Llewelyn Allotey, Eran Halperin:
Extend and Explain: Interpreting Very Long Language Models. 218-258 - Ran Xu, Yue Yu, Chao Zhang, Mohammed K. Ali, Joyce C. Ho, Carl Yang:
Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHR. 259-278 - Dániel Unyi, Bálint Gyires-Tóth:
Neurodevelopmental Phenotype Prediction: A State-of-the-Art Deep Learning Model. 279-289 - Margherita Rosnati, Fabio De Sousa Ribeiro, Miguel Monteiro, Daniel Coelho de Castro, Ben Glocker:
Analysing the effectiveness of a generative model for semi-supervised medical image segmentation. 290-310 - Mehak Gupta, Brennan Gallamoza, Nicolas Cutrona, Pranjal Dhakal, Raphael Poulain, Rahmatollah Beheshti:
An Extensive Data Processing Pipeline for MIMIC-IV. 311-325 - Hamed Fayyaz, Thao-Ly T. Phan, H. Timothy Bunnell, Rahmatollah Beheshti:
Predicting attrition patterns from pediatric weight management programs. 326-342 - Tao Tu, Eric Loreaux, Emma Chesley, Ádám D. Lelkes, Paul Gamble, Mathias Bellaiche, Martin Seneviratne, Ming-Jun Chen:
Automated LOINC Standardization Using Pre-trained Large Language Models. 343-355 - Zhuohong He, Ali Mottaghi, Aidean Sharghi, Muhammad Abdullah Jamal, Omid Mohareri:
An Empirical Study on Activity Recognition in Long Surgical Videos. 356-372 - Raymond Li, Ilya Valmianski, Li Deng, Xavier Amatriain, Anitha Kannan:
OSLAT: Open Set Label Attention Transformer for Medical Entity Retrieval and Span Extraction. 373-390 - Yuhui Zhang, Shih-Cheng Huang, Zhengping Zhou, Matthew P. Lungren, Serena Yeung:
Adapting Pre-trained Vision Transformers from 2D to 3D through Weight Inflation Improves Medical Image Segmentation. 391-404 - Hakim Benkirane, Maria Vakalopoulou, Stergios Christodoulidis, Ingrid-Judith Garberis, Stefan Michiels, Paul-Henry Cournède:
Hyper-AdaC: Adaptive clustering-based hypergraph representation of whole slide images for survival analysis. 405-418 - Rastko Ciric, Armin W. Thomas, Oscar Esteban, Russell A. Poldrack:
Differentiable programming for functional connectomics. 419-455 - Vignav Ramesh, Nathan Andrew Chi, Pranav Rajpurkar:
Improving Radiology Report Generation Systems by Removing Hallucinated References to Non-existent Priors. 456-473 - Jie Wang, Ronald Moore, Yao Xie, Rishikesan Kamaleswaran:
Improving Sepsis Prediction Model Generalization With Optimal Transport. 474-488 - Michael S. Yao, Michael S. Hansen:
A Path Towards Clinical Adaptation of Accelerated MRI. 489-511 - Danliang Ho, Mehul Motani:
Machine and Deep Learning Methods for Predicting Immune Checkpoint Blockade Response. 512-529 - Vasiliki Tassopoulou, Fanyang Yu, Christos Davatzikos:
Deep Kernel Learning with Temporal Gaussian Processes for Clinical Variable Prediction in Alzheimer's Disease. 539-551 - Daniel Lopez Martinez, Alex Yakubovich, Martin Seneviratne, Ádám D. Lelkes, Akshit Tyagi, Jonas Kemp, Ethan Steinberg, N. Lance Downing, Ron C. Li, Keith E. Morse, Nigam H. Shah, Ming-Jun Chen:
Instability in clinical risk stratification models using deep learning. 552-565 - Ivan Ezhov, Marcel Rosier, Lucas Zimmer, Florian Kofler, Suprosanna Shit, Johannes C. Paetzold, Kevin Scibilia, Felix Steinbauer, Leon Mächler, Katharina Franitza, Tamaz Amiranashvili, Martin J. Menten, Marie Metz, Sailesh Conjeti, Benedikt Wiestler, Bjoern H. Menze:
A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling. 566-577
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