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Found 7 papers, 4 papers with code
Dynamic Healthcare Embeddings for Improving Patient Care
Here, we propose DECENT, an auto-encoding heterogeneous co-evolving dynamic neural network, for learning heterogeneous dynamic embeddings of patients, doctors, rooms, and medications from diverse data streams.
EINNs: Epidemiologically-informed Neural Networks
We introduce EINNs, a framework crafted for epidemic forecasting that builds upon the theoretical grounds provided by mechanistic models as well as the data-driven expressibility afforded by AI models, and their capabilities to ingest heterogeneous information.
Incorporating Expert Guidance in Epidemic Forecasting
Forecasting influenza like illnesses (ILI) has rapidly progressed in recent years from an art to a science with a plethora of data-driven methods.
NetReAct: Interactive Learning for Network Summarization
The analyst can express her agreement/disagreement with the visualization of the network summary via iterative feedback, e. g. closing or moving documents ("nodes") together.
Mapping Network States Using Connectivity Queries
In contrast, we study the harder problem of inferring failed components given partial information of some `serviceable' reachable nodes and a small sample of point probes, being the first often more practical to obtain.
Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19
Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic.
Distributed Representation of Subgraphs
Motivated by the recent successes of embeddings in natural language processing, researchers have tried to find network embeddings in order to exploit machine learning algorithms for mining tasks like node classification and edge prediction.
Ranked #4 on
Malware Detection
on Android Malware Dataset
