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pro vyhledávání: '"Lee, Donald K. K."'
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
Applications of machine learning (ML) techniques to operational settings often face two challenges: i) ML methods mostly provide point predictions whereas many operational problems require distributional information; and ii) They typically do not inc
Externí odkaz:
http://arxiv.org/abs/2407.19092
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:
Aronow, Peter M., Lee, Donald K. K.
Given samples $x_1,\cdots,x_n$, it is well known that any sample median value (not necessarily unique) minimizes the absolute loss $\sum_{i=1}^n |q-x_i|$. Interestingly, we show that the minimizer of the loss $\sum_{i=1}^n|q-x_i|^{1+\epsilon}$ exhibi
Externí odkaz:
http://arxiv.org/abs/1807.03462
Publikováno v:
Annals of Statistics 49:4:2101-2128 (2021)
Given functional data from a survival process with time-dependent covariates, we derive a smooth convex representation for its nonparametric log-likelihood functional and obtain its functional gradient. From this, we devise a generic gradient boostin
Externí odkaz:
http://arxiv.org/abs/1701.07926
Akademický článek
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Publikováno v:
Annals of Statistics 47:3:1754-1775 (2019)
Exploiting the fact that most arrival processes exhibit cyclic behaviour, we propose a simple procedure for estimating the intensity of a nonhomogeneous Poisson process. The estimator is the super-resolution analogue to Shao 2010 and Shao & Lii 2011,
Externí odkaz:
http://arxiv.org/abs/1610.09600
Publikováno v:
Service Science; Jun2024, Vol. 16 Issue 2, p70-84, 15p
