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ORCIDCHIL 2023: Cambridge, MA, USA
- Bobak J. Mortazavi, Tasmie Sarker, Andrew Beam, Joyce C. Ho:
Conference on Health, Inference, and Learning, CHIL 2023, Broad Institute of MIT and Harvard (Merkin Building), 415 Main Street, Cambridge, MA, USA. Proceedings of Machine Learning Research 209, PMLR 2023 - Preface. 1-5
- Sarwan Ali, Babatunde Bello, Prakash Chourasia, Ria Thazhe Punathil, Pin-Yu Chen, Imdadullah Khan, Murray Patterson:
Counterfactually Guided Policy Transfer in Clinical Settings. 6-18 - Shuo Shuo Liu, Lin Lin:
Adaptive Weighted Multi-View Clustering. 19-36 - Toshiya Yoshida, Trinity Shuxian Fan, Tyler H. McCormick, Zhenke Wu, Zehang Richard Li
:
Bayesian Active Questionnaire Design for Cause-of-Death Assignment Using Verbal Autopsies. 37-49 - Siyi Tang, Jared A. Dunnmon, Liangqiong Qu, Khaled Kamal Saab, Tina Baykaner, Christopher Lee-Messer, Daniel L. Rubin:
Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models. 50-71 - Yuexin Wu, I-Chan Huang, Xiaolei Huang:
Token Imbalance Adaptation for Radiology Report Generation. 72-85 - Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana:
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? 86-99 - Siun Kim, Jung-Hyun Won, David Seung U. Lee, Renqian Luo, Lijun Wu, Yingce Xia, Tao Qin, Howard Lee:
Revisiting Machine-Learning based Drug Repurposing: Drug Indications Are Not a Right Prediction Target. 100-116 - Li Xu, Bo Liu, Ameer Hamza Khan, Lu Fan, Xiao-Ming Wu:
Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models. 117-132 - Mingyue Tang, Jiechao Gao, Guimin Dong, Carl Yang, Bradford Campbell, Brendan Bowman, Jamie Marie Zoellner, Emaad Abdel-Rahman, Mehdi Boukhechba:
øurs: Mobile Sensing based Fluid Overload Detection for End Stage Kidney Disease Patients using _Sensor _Relation _Dual _Autoencoder. 133-146 - Andre Manoel, Mirian del Carmen Hipolito Garcia, Tal Baumel, Shize Su, Jialei Chen, Robert Sim, Dan Miller, Danny Karmon, Dimitrios Dimitriadis:
Federated Multilingual Models for Medical Transcript Analysis. 147-162 - Hyeonji Hwang, Seongjun Yang, Daeyoung Kim, Radhika Dua, Jong-Yeup Kim, Eunho Yang, Edward Choi:
Towards the Practical Utility of Federated Learning in the Medical Domain. 163-181 - Giovanni Cinà, Tabea E. Röber, Rob Goedhart, S. Ilker Birbil:
Semantic match: Debugging feature attribution methods in XAI for healthcare. 182-190 - Mike A. Merrill, Tim Althoff:
Self-Supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets. 191-206 - Mike A. Merrill, Esteban Safranchik, Arinbjörn Kolbeinsson, Piyusha Gade, Ernesto Ramirez, Ludwig Schmidt, Luca Foshchini, Tim Althoff:
Homekit2020: A Benchmark for Time Series Classification on a Large Mobile Sensing Dataset with Laboratory Tested Ground Truth of Influenza Infections. 207-228 - Kevin Wu, Dominik Dahlem, Christopher Hane, Eran Halperin, James Zou:
Collecting data when missingness is unknown: a method for improving model performance given under-reporting in patient populations. 229-242 - Christina X. Ji, Ahmed M. Alaa, David A. Sontag:
Large-Scale Study of Temporal Shift in Health Insurance Claims. 243-278 - Arvind Pillai, Subigya Nepal, Andrew Campbell:
Rare Life Event Detection via Mobile Sensing Using Multi-Task Learning. 279-293 - Min Jae Lee, Kyunghoon Hur, Jiyoun Kim, Jinsung Yoon, Edward Choi:
Rediscovery of CNN's Versatility for Text-based Encoding of Raw Electronic Health Records. 294-313 - Qixuan Jin, Jacobien H. F. Oosterhoff, Yepeng Huang, Marzyeh Ghassemi, Gabriel A Brat:
Clinical Relevance Score for Guided Trauma Injury Pattern Discovery with Weakly Supervised β-VAE. 314-339 - Hardy Hardy, Derek Ruths, Nicholas B. King:
Who Controlled the Evidence? Question Answering for Disclosure Information Extraction. 340-349 - William G. La Cava, Elle Lett, Guangya Wan:
Fair admission risk prediction with proportional multicalibration. 350-378 - Vincent Jeanselme, Chang Ho Yoon, Brian D. M. Tom, Jessica K. Barrett:
Neural Fine-Gray: Monotonic neural networks for competing risks. 379-392 - Harry Rubin-Falcone, Joyce M. Lee, Jenna Wiens:
Denoising Autoencoders for Learning from Noisy Patient-Reported Data. 393-409 - Katie Matton, Robert Lewis, John V. Guttag, Rosalind W. Picard:
Contrastive Learning of Electrodermal Activity Representations for Stress Detection. 410-426 - Jessica Zheng, Hanrui Wang, Anand Chandrasekhar, Aaron D. Aguirre, Song Han, Hae-Seung Lee, Charles G. Sodini:
Machine Learning for Arterial Blood Pressure Prediction. 427-439 - George H. Chen:
A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular Information. 440-476 - Donna Tjandra, Jenna Wiens:
Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise. 477-497 - Helen Zhou, Yuwen Chen, Zachary C. Lipton:
Evaluating Model Performance in Medical Datasets Over Time. 498-508 - Iman Deznabi, Madalina Fiterau:
MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction. 509-525 - Yi Yang, Hejie Cui, Carl Yang:
PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis. 526-544 - Kailas Vodrahalli, Gregory D. Lyng, Brian L. Hill, Kimmo Kärkkäinen, Jeffrey Hertzberg, James Zou, Eran Halperin:
Understanding and Predicting the Effect of Environmental Factors on People with Type 2 Diabetes. 545-555 - Supriya Nagesh, Nina Mishra, Yonatan Naamad, James M. Rehg, Mehul A. Shah, Alexei Wagner:
Explaining a machine learning decision to physicians via counterfactuals. 556-577 - Eric Lehman, Evan Hernandez, Diwakar Mahajan, Jonas Wulff, Micah J. Smith, Zachary Ziegler, Daniel Nadler, Peter Szolovits, Alistair E. W. Johnson, Emily Alsentzer:
Do We Still Need Clinical Language Models? 578-597
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last updated on 2025-12-05 22:22 CET by the dblp team
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