Table of Contents

• Machine Learning for Health (ML4H) 2022Antonio 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 Alaa, Harvineet Singh, Irene Y. Chen, Shalmali Joshi. 1-11 [doi]
• Imputation Strategies Under Clinical Presence: Impact on Algorithmic FairnessVincent Jeanselme, Maria De-Arteaga, Zhe Zhang, Jessica K. Barrett, Brian D. M. Tom. 12-34 [doi]
• Predicting Treatment Adherence of Tuberculosis Patients at ScaleMihir Kulkarni, Satvik Golechha, Rishi Raj, Jithin K. Sreedharan, Ankit Bhardwaj, Santanu Rathod, Bhavin Vadera, Jayakrishna Kurada, Sanjay Mattoo, Rajendra Joshi, Kirankumar Rade, Alpan Raval. 35-61 [doi]
• Distributionally Robust Survival Analysis: A Novel Fairness Loss Without DemographicsShu Hu, George H. Chen. 62-87 [doi]
• mmVAE: multimorbidity clustering using Relaxed Bernoulli β-Variational AutoencodersCharles Gadd, Krishnarajah Nirantharakumar, Christopher Yau. 88-102 [doi]
• Feature Allocation Approach for Multimorbidity Trajectory ModellingWoojung Kim, Paul A. Jenkins, Christopher Yau. 103-119 [doi]
• Towards Cross-Modal Causal Structure and Representation LearningHaiyi Mao, Hongfu Liu, Jason Xiaotian Dou, Panayiotis V. Benos. 120-140 [doi]
• Identifying Heterogeneous Treatment Effects in Multiple Outcomes using Joint Confidence IntervalsPeniel N. Argaw, Elizabeth Healey, Isaac S. Kohane. 141-170 [doi]
• Meta-analysis of individualized treatment rules via sign-coherencyJay Jojo Cheng, Jared D. Huling, Guanhua Chen. 171-198 [doi]
• SleepQA: A Health Coaching Dataset on Sleep for Extractive Question AnsweringIva 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. 199-217 [doi]
• Extend and Explain: Interpreting Very Long Language ModelsJoel Stremmel, Brian L. Hill, Jeffrey Hertzberg, Jaime Murillo, Llewelyn Allotey, Eran Halperin. 218-258 [doi]
• Counterfactual and Factual Reasoning over Hypergraphs for Interpretable Clinical Predictions on EHRRan Xu, Yue Yu, Chao Zhang, Mohammed K. Ali, Joyce C. Ho, Carl Yang. 259-278 [doi]
• Neurodevelopmental Phenotype Prediction: A State-of-the-Art Deep Learning ModelDániel Unyi, Bálint Gyires-Tóth. 279-289 [doi]
• Analysing the effectiveness of a generative model for semi-supervised medical image segmentationMargherita Rosnati, Fabio De Sousa Ribeiro, Miguel Monteiro, Daniel Coelho de Castro, Ben Glocker. 290-310 [doi]
• An Extensive Data Processing Pipeline for MIMIC-IVMehak Gupta, Brennan Gallamoza, Nicolas Cutrona, Pranjal Dhakal, Raphael Poulain, Rahmatollah Beheshti. 311-325 [doi]
• Predicting attrition patterns from pediatric weight management programsHamed Fayyaz, Thao-Ly T. Phan, H. Timothy Bunnell, Rahmatollah Beheshti. 326-342 [doi]
• Automated LOINC Standardization Using Pre-trained Large Language ModelsTao Tu, Eric Loreaux, Emma Chesley, Ádám D. Lelkes, Paul Gamble, Mathias Bellaiche, Martin Seneviratne, Ming-Jun Chen. 343-355 [doi]
• An Empirical Study on Activity Recognition in Long Surgical VideosZhuohong He, Ali Mottaghi, Aidean Sharghi, Muhammad Abdullah Jamal, Omid Mohareri. 356-372 [doi]
• OSLAT: Open Set Label Attention Transformer for Medical Entity Retrieval and Span ExtractionRaymond Li, Ilya Valmianski, Li Deng, Xavier Amatriain, Anitha Kannan. 373-390 [doi]
• Adapting Pre-trained Vision Transformers from 2D to 3D through Weight Inflation Improves Medical Image SegmentationYuhui Zhang, Shih-Cheng Huang, Zhengping Zhou, Matthew P. Lungren, Serena Yeung. 391-404 [doi]
• Hyper-AdaC: Adaptive clustering-based hypergraph representation of whole slide images for survival analysisHakim Benkirane, Maria Vakalopoulou, Stergios Christodoulidis, Ingrid-Judith Garberis, Stefan Michiels, Paul-Henry Cournède. 405-418 [doi]
• Differentiable programming for functional connectomicsRastko Ciric, Armin W. Thomas, Oscar Esteban, Russell A. Poldrack. 419-455 [doi]
• Improving Radiology Report Generation Systems by Removing Hallucinated References to Non-existent PriorsVignav Ramesh, Nathan Andrew Chi, Pranav Rajpurkar. 456-473 [doi]
• Improving Sepsis Prediction Model Generalization With Optimal TransportJie Wang, Ronald Moore, Yao Xie, Rishikesan Kamaleswaran. 474-488 [doi]
• A Path Towards Clinical Adaptation of Accelerated MRIMichael S. Yao, Michael S. Hansen. 489-511 [doi]
• Machine and Deep Learning Methods for Predicting Immune Checkpoint Blockade ResponseDanliang Ho, Mehul Motani. 512-529 [doi]
• Deep Kernel Learning with Temporal Gaussian Processes for Clinical Variable Prediction in Alzheimer's DiseaseVasiliki Tassopoulou, Fanyang Yu, Christos Davatzikos. 539-551 [doi]
• Instability in clinical risk stratification models using deep learningDaniel 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. 552-565 [doi]
• A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modelingIvan Ezhov, Marcel Rosier, Lucas Zimmer, Florian Kofler, Suprosanna Shit, Johannes C. Paetzold, Kevin Scibilia, Felix Steinbauer, Leon Maechler, Katharina Franitza, Tamaz Amiranashvili, Martin J. Menten, Marie Metz, Sailesh Conjeti, Benedikt Wiestler, Bjoern H. Menze. 566-577 [doi]