Search Results for author: Bijaya Adhikari

Found 7 papers, 4 papers with code

Dynamic Healthcare Embeddings for Improving Patient Care

1 code implementation21 Mar 2023 Hankyu Jang, Sulyun Lee, D. M. Hasibul Hasan, Philip M. Polgreen, Sriram V. Pemmaraju, Bijaya Adhikari

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.

severity prediction

EINNs: Epidemiologically-informed Neural Networks

1 code implementation21 Feb 2022 Alexander Rodríguez, Jiaming Cui, Naren Ramakrishnan, Bijaya Adhikari, B. Aditya Prakash

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.

Inductive Bias

Incorporating Expert Guidance in Epidemic Forecasting

no code implementations24 Dec 2020 Alexander Rodríguez, Bijaya Adhikari, Naren Ramakrishnan, B. Aditya Prakash

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

no code implementations22 Dec 2020 Sorour E. Amiri, Bijaya Adhikari, John Wenskovitch, Alexander Rodriguez, Michelle Dowling, Chris North, B. Aditya Prakash

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

1 code implementation7 Dec 2020 Alexander Rodríguez, Bijaya Adhikari, Andrés D. González, Charles Nicholson, Anil Vullikanti, B. Aditya Prakash

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.

Distributed Representation of Subgraphs

no code implementations22 Feb 2017 Bijaya Adhikari, Yao Zhang, Naren Ramakrishnan, B. Aditya Prakash

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.

Community Detection Node Classification

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