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pro vyhledávání: '"Pakbin, Arash"'
Objective: realtime monitoring of invasive ventilation (iV) in intensive care units (ICUs) plays a crucial role in ensuring prompt interventions and better patient outcomes. However, conventional methods often overlook valuable insights embedded with
Externí odkaz:
http://arxiv.org/abs/2410.03725
Electronic Health Record (EHR) systems provide critical, rich and valuable information at high frequency. One of the most exciting applications of EHR data is in developing a real-time mortality warning system with tools from survival analysis. Howev
Externí odkaz:
http://arxiv.org/abs/2110.08949
Modern applications of survival analysis increasingly involve time-dependent covariates. The Python package BoXHED2.0 is a tree-boosted hazard estimator that is fully nonparametric, and is applicable to survival settings far more general than right-c
Externí odkaz:
http://arxiv.org/abs/2103.12591
Publikováno v:
ICML 9973-9982 (2020)
The proliferation of medical monitoring devices makes it possible to track health vitals at high frequency, enabling the development of dynamic health risk scores that change with the underlying readings. Survival analysis, in particular hazard estim
Externí odkaz:
http://arxiv.org/abs/2006.14218
Autor:
Huo, Zepeng, PakBin, Arash, Chen, Xiaohan, Hurley, Nathan, Yuan, Ye, Qian, Xiaoning, Wang, Zhangyang, Huang, Shuai, Mortazavi, Bobak
Activity recognition in wearable computing faces two key challenges: i) activity characteristics may be context-dependent and change under different contexts or situations; ii) unknown contexts and activities may occur from time to time, requiring fl
Externí odkaz:
http://arxiv.org/abs/2003.01753