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ORCIDML4H@NeurIPS 2023: New Orleans, LA, USA
- Stefan Hegselmann, Antonio Parziale, Divya Shanmugam, Shengpu Tang, Mercy Nyamewaa Asiedu, Serina Chang, Tom Hartvigsen, Harvineet Singh:
Machine Learning for Health, ML4H@NeurIPS 2023, 10 December 2023, New Orleans, Louisiana, USA. Proceedings of Machine Learning Research 225, PMLR 2023 - Stefan Hegselmann, Antonio Parziale, Divya Shanmugam, Shengpu Tang, Kristen Severson, Mercy Nyamewaa Asiedu, Serina Chang, Bonaventure F. P. Dossou, Qian Huang, Fahad Kamran, Haoran Zhang, Sujay Nagaraj, Luis Oala, Shan Xu, Chinasa T. Okolo, Helen Zhou, Jessica Dafflon, Caleb Ellington, Sarah Jabbour, Hyewon Jeong, Harry Reyes Nieva
, Yuzhe Yang, Ghada Zamzmi, Vishwali Mhasawade, Van Truong, Payal Chandak, Matthew Lee, Peniel Argaw, Kyle Heuton, Harvineet Singh, Thomas Hartvigsen:
Machine Learning for Health (ML4H) 2023. 1-12 - Muhammad Muneeb Afzal, Muhammad Osama Khan, Shujaat Mirza:
Towards Equitable Kidney Tumor Segmentation: Bias Evaluation and Mitigation. 13-26 - Shobhit Agarwal, Yevgeniy R. Semenov, William Lotter:
Representing visual classification as a linear combination of words. 27-38 - Ali Behrouz, Farnoosh Hashemi:
Learning Temporal Higher-order Patterns to Detect Anomalous Brain Activity. 39-51 - Manuel Burger, Gunnar Rätsch, Rita Kuznetsova:
Multi-modal Graph Learning over UMLS Knowledge Graphs. 52-81 - Akshay Goel, Almog Gueta, Omry Gilon, Chang Liu, Sofia Erell, Lan Huong Nguyen, Xiaohong Hao, Bolous Jaber, Shashir Reddy, Rupesh Kartha, Jean Steiner, Itay Laish, Amir Feder:
LLMs Accelerate Annotation for Medical Information Extraction. 82-100 - Fabian Gröger, Simone Lionetti, Philippe Gottfrois, Álvaro González-Jiménez, Matthew Groh, Roxana Daneshjou, Labelling Consortium, Alexander A. Navarini, Marc Pouly:
Towards Reliable Dermatology Evaluation Benchmarks. 101-128 - Ethan Harvey, Wansu Chen, David M. Kent, Michael C. Hughes:
A Probabilistic Method to Predict Classifier Accuracy on Larger Datasets given Small Pilot Data. 129-144 - Mohammad Reza Hosseinzadeh Taher, Masaki Ikuta, Ravi Soni:
Curriculum Self-Supervised Learning for 3D CT Cardiac Image Segmentation. 145-156 - Chang Hu, Krishnakant V. Saboo, Ahmad H. Ali, Brian D. Juran, Konstantinos N. Lazaridis, Ravishankar K. Iyer:
REMEDI: REinforcement learning-driven adaptive MEtabolism modeling of primary sclerosing cholangitis DIsease progression. 157-189 - Nivetha Jayakumar, Jiarui Xing, Tonmoy Hossain, Frederick H. Epstein, Kenneth C. Bilchick, Miaomiao Zhang:
Activation From Sparse 2D Cardiac MRIs. 190-200 - Prajwal Kailas, Max Homilius, Rahul C. Deo, Calum A. MacRae:
NoteContrast: Contrastive Language-Diagnostic Pretraining for Medical Text. 201-216 - Muhammad Osama Khan, Muhammad Muneeb Afzal, Shujaat Mirza, Yi Fang:
How Fair are Medical Imaging Foundation Models? 217-231 - Sameer Tajdin Khanna, Daniel Michael, Marinka Zitnik, Pranav Rajpurkar:
Learning Generalized Medical Image Representations Through Image-Graph Contrastive Pretraining. 232-243 - Ryan King, Tianbao Yang, Bobak J. Mortazavi:
Multimodal Pretraining of Medical Time Series and Notes. 244-255 - Rafal Kocielnik, Elyssa Y. Wong, Timothy N. Chu, Lydia Lin, De-An Huang, Jiayun Wang, Anima Anandkumar, Andrew J. Hung:
Deep Multimodal Fusion for Surgical Feedback Classification. 256-267 - Rita Kuznetsova, Alizée Pace, Manuel Burger, Hugo Yèche, Gunnar Rätsch:
On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series. 268-291 - Bingnan Li, Zhitong Gao, Xuming He:
Gradient-Map-Guided Adaptive Domain Generalization for Cross Modality MRI Segmentation. 292-306 - Ivana Malenica, Yongyi Guo, Kyra Gan, Stefan Konigorski:
Anytime-valid inference in N-of-1 trials. 307-322 - Aishwarya Mandyam, Andrew Jones, Jiayu Yao, Krzysztof Laudanski, Barbara E. Engelhardt:
Compositional Q-learning for electrolyte repletion with imbalanced patient sub-populations. 323-339 - Dominik Meier, Ipek Ensari, Stefan Konigorski:
Designing and evaluating an online reinforcement learning agent for physical exercise recommendations in N-of-1 trials. 340-352 - Michael Moor, Qian Huang, Shirley Wu, Michihiro Yasunaga, Yash Dalmia, Jure Leskovec, Cyril Zakka, Eduardo Pontes Reis, Pranav Rajpurkar:
Med-Flamingo: a Multimodal Medical Few-shot Learner. 353-367 - Yousef Nademi, Sunil Vasu Kalmady, Weijie Sun, Shiang Qi, Abram Hindle, Padma Kaul, Russell Greiner:
Supervised Electrocardiogram(ECG) Features Outperform Knowledge-based And Unsupervised Features In Individualized Survival Prediction. 368-384 - Dang Nguyen, Chacha Chen, He He, Chenhao Tan:
Pragmatic Radiology Report Generation. 385-402 - Shahriar Noroozizadeh, Jeremy C. Weiss, George H. Chen:
Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression. 403-427 - Arina Odnoblyudova, Caglar Hizli, St John, Andrea Cognolato, Anne Juuti, Simo Särkkä, Kirsi Pietiläinen, Pekka Marttinen:
Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamics. 428-444 - Xueqiao Peng, Jiaqi Xu, Xi Chen, Dinh Song An Nguyen, Andrew Perrault:
Using Reinforcement Learning for Multi-Objective Cluster-Level Optimization of Non-Pharmaceutical Interventions for Infectious Disease. 445-460 - Onur Poyraz, Pekka Marttinen:
Mixture of Coupled HMMs for Robust Modeling of Multivariate Healthcare Time Series. 461-479 - Jielin Qiu, Jiacheng Zhu, Shiqi Liu, William Han, Jingqi Zhang, Chaojing Duan, Michael A. Rosenberg, Emerson Liu, Douglas Weber, Ding Zhao:
Automated Cardiovascular Record Retrieval by Multimodal Learning between Electrocardiogram and Clinical Report. 480-497 - Yifei Ren, Jian Lou, Li Xiong, Joyce C. Ho, Xiaoqian Jiang, Sivasubramanium Venkatraman Bhavani:
MULTIPAR: Supervised Irregular Tensor Factorization with Multi-task Learning for Computational Phenotyping. 498-511 - Margherita Rosnati, Mélanie Roschewitz, Ben Glocker:
Robust semi-supervised segmentation with timestep ensembling diffusion models. 512-527 - Vivek Shankar, Xiaoli Yang, Vrishab Krishna, Brent Tan, Oscar Silva, Rebecca Rojansky, Andrew Y. Ng, Fabiola Valvert, Edward Briercheck, David Weinstock, Yasodha Natkunam, Sebastian Fernandez-Pol, Pranav Rajpurkar:
LymphoML: An interpretable artificial intelligence-based method identifies morphologic features that correlate with lymphoma subtype. 528-558 - Abhishek Singh, Venkatapathy Subramanian, Ayush Maheshwari, Pradeep Narayan, Devi Prasad Shetty, Ganesh Ramakrishnan:
Eigen: Expert-Informed Joint Learning Aggregation for High-Fidelity Information Extraction from Document Images. 559-573 - Mike Van Ness, Tomas M. Bosschieter, Natasha Din, Andrew Ambrosy, Alexander Sandhu, Madeleine Udell:
Interpretable Survival Analysis for Heart Failure Risk Prediction. 574-593 - Milos Vukadinovic, Alan C. Kwan, Debiao Li, David Ouyang:
GANcMRI: Cardiac magnetic resonance video generation and physiologic guidance using latent space prompting. 594-606 - Ke Alexander Wang, Emily B. Fox:
Interpretable Mechanistic Representations for Meal-level Glycemic Control in the Wild. 607-622 - Yanbo Xu, Shangqing Xu, Manav Ramprassad, Alexey Tumanov, Chao Zhang:
TransEHR: Self-Supervised Transformer for Clinical Time Series Data. 623-635 - Jennifer Yu, Tina Behrouzi, Kopal Garg, Anna Goldenberg, Sana Tonekaboni:
Dynamic Interpretable Change Point Detection for Physiological Data Analysis. 636-649 - Han Yu, Peikun Guo, Akane Sano:
Zero-Shot ECG Diagnosis with Large Language Models and Retrieval-Augmented Generation. 650-663 - Xiaohui Zhang, Ahana Gangopadhyay, Hsi-Ming Chang, Ravi Soni:
Diffusion Model-Based Data Augmentation for Lung Ultrasound Classification with Limited Data. 664-676
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