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Found 8 papers, 2 papers with code
Large-Scale Domain-Specific Pretraining for Biomedical Vision-Language Processing
Our dataset (PMC-15M) is two orders of magnitude larger than existing biomedical image-text datasets such as MIMIC-CXR, and spans a diverse range of biomedical images.
UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification
It achieves within 0. 1% accuracy from the highest-performing multi-class baseline, while saving close to 20X on spatio-temporal cost of inference and earlier (3. 5hrs) disease onset prediction.
TransEditor: Transformer-Based Dual-Space GAN for Highly Controllable Facial Editing
In this study, we highlight the importance of interaction in a dual-space GAN for more controllable editing.
Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions
For many kinds of interventions, such as a new advertisement, marketing intervention, or feature recommendation, it is important to target a specific subset of people for maximizing its benefits at minimum cost or potential harm.
DeepRite: Deep Recurrent Inverse TreatmEnt Weighting for Adjusting Time-varying Confounding in Modern Longitudinal Observational Data
How to model such time-varying effects from longitudinal observational data?
HOLMES: Health OnLine Model Ensemble Serving for Deep Learning Models in Intensive Care Units
HOLMES is tested on risk prediction task on pediatric cardio ICU data with above 95% prediction accuracy and sub-second latency on 64-bed simulation.
RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data
With the improvement of medical data capturing, vast amount of continuous patient monitoring data, e. g., electrocardiogram (ECG), real-time vital signs and medications, become available for clinical decision support at intensive care units (ICUs).
A Bayesian Nonparametric Approach for Estimating Individualized Treatment-Response Curves
We study the problem of estimating the continuous response over time to interventions using observational time series---a retrospective dataset where the policy by which the data are generated is unknown to the learner.
