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Found 9 papers, 4 papers with code
COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised Learning
Estimation of temporal counterfactual outcomes from observed history is crucial for decision-making in many domains such as healthcare and e-commerce, particularly when randomized controlled trials (RCTs) suffer from high cost or impracticality.
Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders
Causal analysis for time series data, in particular estimating individualized treatment effect (ITE), is a key task in many real-world applications, such as finance, retail, healthcare, etc.
When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning
Physics-informed machine learning (PIML), referring to the combination of prior knowledge of physics, which is the high level abstraction of natural phenomenons and human behaviours in the long history, with data-driven machine learning models, has emerged as an effective way to mitigate the shortage of training data, to increase models' generalizability and to ensure the physical plausibility of results.
BIG-bench Machine Learning
Physics-informed machine learning
Cross-Node Federated Graph Neural Network for Spatio-Temporal Data Modeling
Vast amount of data generated from networks of sensors, wearables, and the Internet of Things (IoT) devices underscores the need for advanced modeling techniques that leverage the spatio-temporal structure of decentralized data due to the need for edge computation and licensing (data access) issues.
MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset
The recent release of large-scale healthcare datasets has greatly propelled the research of data-driven deep learning models for healthcare applications.
PolSIRD: Modeling Epidemic Spread under Intervention Policies
Epidemic spread in a population is traditionally modeled via compartmentalized models which represent the free evolution of disease in absence of any intervention policies.
Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning
Although the knowledge of governing partial differential equations (PDE) of data can be helpful for the fast adaptation to few observations, it is mostly infeasible to exactly find the equation for observations in real-world physical systems.
COVID-19 on Social Media: Analyzing Misinformation in Twitter Conversations
The analysis is presented and updated on a publically accessible dashboard (https://usc-melady. github. io/COVID-19-Tweet-Analysis) to track the nature of online discourse and misinformation about COVID-19 on Twitter from March 1 - June 5, 2020.
Benchmark of Deep Learning Models on Large Healthcare MIMIC Datasets
Deep learning models (aka Deep Neural Networks) have revolutionized many fields including computer vision, natural language processing, speech recognition, and is being increasingly used in clinical healthcare applications.
