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Volume 209: Conference on Health, Inference, and Learning, , 415 Main Street, Cambridge, MA USA 02142
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Editors: Bobak J. Mortazavi, Tasmie Sarker, Andrew Beam, Joyce C. Ho
Conference on Health, Inference, and Learning (CHIL) 2023
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:1-5
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Virus2Vec: Viral Sequence Classification Using Machine Learning
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:6-18
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Adaptive Weighted Multi-View Clustering
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:19-36
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Bayesian Active Questionnaire Design for Cause-of-Death Assignment Using Verbal Autopsies
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:37-49
Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:50-71
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Token Imbalance Adaptation for Radiology Report Generation
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:72-85
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:86-99
Revisiting Machine-Learning based Drug Repurposing: Drug Indications Are Not a Right Prediction Target
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:100-116
Multi-modal Pre-training for Medical Vision-language Understanding and Generation: An Empirical Study with A New Benchmark
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:117-132
SRDA: Mobile Sensing based Fluid Overload Detection for End Stage Kidney Disease Patients using Sensor Relation Dual Autoencoder
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:133-146
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Federated Multilingual Models for Medical Transcript Analysis
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:147-162
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Towards the Practical Utility of Federated Learning in the Medical Domain
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:163-181
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Semantic match: Debugging feature attribution methods \titlebreak in XAI for healthcare
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:182-190
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Self-Supervised Pretraining and Transfer Learning Enable\titlebreak Flu and COVID-19 Predictions in Small Mobile Sensing Datasets
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:191-206
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Homekit2020: A Benchmark for Time Series Classification on a Large Mobile Sensing Dataset with Laboratory Tested Ground Truth of Influenza Infections
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:207-228
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Collecting data when missingness is unknown: a method for improving model performance given under-reporting in patient populations
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:229-242
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Large-Scale Study of Temporal Shift in Health Insurance Claims
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:243-278
Rare Life Event Detection via Mobile Sensing Using Multi-Task Learning
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:279-293
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Rediscovery of CNN’s Versatility for Text-based Encoding of Raw Electronic Health Records
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:294-313
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Clinical Relevance Score for Guided Trauma Injury Pattern Discovery with Weakly Supervised $β$-VAE
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:314-339
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Who Controlled the Evidence? Question Answering for Disclosure Information Extraction
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:340-349
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Fair admission risk prediction with proportional multicalibration
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:350-378
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Neural Fine-Gray: Monotonic neural networks for competing risks
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:379-392
Denoising Autoencoders for Learning from Noisy Patient-Reported Data
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:393-409
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Contrastive Learning of Electrodermal Activity Representations for Stress Detection
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:410-426
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Machine Learning for Arterial Blood Pressure Prediction
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:427-439
A General Framework for Visualizing Embedding Spaces of\titlebreak Neural Survival Analysis Models Based on Angular Information
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:440-476
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Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:477-497
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Evaluating Model Performance in Medical Datasets Over Time
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:498-508
MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:509-525
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PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:526-544
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Understanding and Predicting the Effect of Environmental Factors on People with Type 2 Diabetes
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:545-555
Explaining a machine learning decision to physicians via counterfactuals
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:556-577
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Do We Still Need Clinical Language Models?
; Proceedings of the Conference on Health, Inference, and Learning, PMLR 209:578-597
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