Výsledky vyhledávání - "Shashikumar, Supreeth"

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Improving Prediction of Need for Mechanical Ventilation using Cross-Attention

Autor: Mohanty, Anwesh, Shashikumar, Supreeth P., Lam, Jonathan Y., Nemati, Shamim

In the intensive care unit, the capability to predict the need for mechanical ventilation (MV) facilitates more timely interventions to improve patient outcomes. Recent works have demonstrated good performance in this task utilizing machine learning

Externí odkaz: http://arxiv.org/abs/2407.15885

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DeepAISE -- An End-to-End Development and Deployment of a Recurrent Neural Survival Model for Early Prediction of Sepsis

Autor: Shashikumar, Supreeth P., Josef, Christopher, Sharma, Ashish, Nemati, Shamim

Sepsis, a dysregulated immune system response to infection, is among the leading causes of morbidity, mortality, and cost overruns in the Intensive Care Unit (ICU). Early prediction of sepsis can improve situational awareness amongst clinicians and f

Externí odkaz: http://arxiv.org/abs/1908.04759

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Does the 'Artificial Intelligence Clinician' learn optimal treatment strategies for sepsis in intensive care?

Autor: Jeter, Russell, Josef, Christopher, Shashikumar, Supreeth, Nemati, Shamim

From 2017 to 2018 the number of scientific publications found via PubMed search using the keyword "Machine Learning" increased by 46% (4,317 to 6,307). The results of studies involving machine learning, artificial intelligence (AI), and big data have

Externí odkaz: http://arxiv.org/abs/1902.03271

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Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks

Autor: Shashikumar, Supreeth P., Shah, Amit J., Clifford, Gari D., Nemati, Shamim

Detection of atrial fibrillation (AF), a type of cardiac arrhythmia, is difficult since many cases of AF are usually clinically silent and undiagnosed. In particular paroxysmal AF is a form of AF that occurs occasionally, and has a higher probability

Externí odkaz: http://arxiv.org/abs/1805.09133

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