activity
20202024
most citedReal-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards

12 citations · 23 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2024

Realtime, multimodal invasive ventilation risk monitoring using language models and BoXHED

Arash Pakbin, Aaron Su, Donald K. K. Lee +1

Objective: realtime monitoring of invasive ventilation (iV) in intensive care units (ICUs) plays a crucial role in ensuring prompt interventions and better patient outcomes. Howeve…

cs.LG2021★ 12 cited

Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards

Zhale Nowroozilarki, Arash Pakbin, James Royalty +2

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-…

cs.LG2021

BoXHED2.0: Scalable boosting of dynamic survival analysis

Arash Pakbin, Xiaochen Wang, Bobak J. Mortazavi +1

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…

stat.ML2020★ 6 cited

BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates

Xiaochen Wang, Arash Pakbin, Bobak J. Mortazavi +2

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…

cs.LG2020★ 5 cited

Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery

Zepeng Huo, Arash PakBin, Xiaohan Chen +6

Activity recognition in wearable computing faces two key challenges: i) activity characteristics may be context-dependent and change under different contexts or situations; ii) unk…