Kimia Vahdat

College of Engineering

Works (5)

Updated: July 2nd, 2024 05:10

2024 journal article

Risk score models for urinary tract infection hospitalization

PLOS ONE, 19(6).

By: N. Alizadeh n, K. Vahdat n , S. Shashaani n , J. Swann n  & O. Ozaltin n 

Ed(s): G. Villavicencio

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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.

By: S. Shashaani n  & K. Vahdat n 

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)
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Sources: Web Of Science, NC State University Libraries
Added: June 6, 2022

2022 conference paper

Personalized Predictions for Unplanned Urinary Tract Infection Hospitalizations with Hierarchical Clustering

Springer Proceedings in Business and Economics, 453–465.

By: L. Mao n, K. Vahdat n , S. Shashaani n  & J. Swann  n

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: March 30, 2022

2021 conference paper

Non-Parametric Uncertainty Bias and Variance Estimation via Nested Bootstrapping and Influence Functions

2021 Winter Simulation Conference (WSC).

By: K. Vahdat n  & S. Shashaani n 

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.

By: K. Vahdat n  & S. Shashaani n 

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)
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Sources: Web Of Science, NC State University Libraries
Added: August 30, 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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