Works (5)
Updated: July 2nd, 2024 05:10
2024 journal article
Risk score models for urinary tract infection hospitalization
PLOS ONE, 19(6).
Ed(s): G. Villavicencio
open access via journals.plos.org (publisher PDF)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: June 15, 2024
2022 article
Improved feature selection with simulation optimization
Shashaani, S., & Vahdat, K. (2022, May 30). OPTIMIZATION AND ENGINEERING, Vol. 5.
author keywords: Stochastic error analysis; Adaptive sampling; Predictive accuracy; Data-driven dependence; Reliability
TL;DR:
This work develops adaptive sampling strategies for large enough datasets, where the number of training and test resamples vary for each solution, and develops adaptive sample sizes, which reach the same quality level of recommended feature subsets but significantly faster than the fixed sample size version.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: June 4, 2022
2022 conference paper
Personalized Predictions for Unplanned Urinary Tract Infection Hospitalizations with Hierarchical Clustering
Springer Proceedings in Business and Economics, 453–465.
TL;DR:
A hierarchical clustering approach that leverages existing knowledge and data-driven algorithms to partition the population into groups of similar risk, followed by building a LASSO-Logistic Regression model for each group, achieves more accurate and precise predictions and offers more granular feature importance insights for each patient group.
(via Semantic Scholar)
Source: ORCID
Added: January 29, 2021
2021 conference paper
Non-Parametric Uncertainty Bias and Variance Estimation via Nested Bootstrapping and Influence Functions
2021 Winter Simulation Conference (WSC).
Source: ORCID
Added: March 30, 2022
2020 article
SIMULATION OPTIMIZATION BASED FEATURE SELECTION, A STUDY ON DATA-DRIVEN OPTIMIZATION WITH INPUT UNCERTAINTY
2020 WINTER SIMULATION CONFERENCE (WSC), pp. 2149–2160.
TL;DR:
This work proposes a framework for simulation optimization over a high dimensional binary space in place of the classic greedy search in forward or backward selection or regularization methods, and provides insight for leveraging Monte Carlo methodology in probabilistic data-driven modeling and analysis.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science)
Sources: Web Of Science, NC State University Libraries, ORCID
Added: August 7, 2021
Employment
Updated: April 26th, 2022 10:14
2018 - 2023
North Carolina State University Raleigh, North Carolina, US
PhD Student
Industrial and Systems Engineering
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