Search

Your search keyword '"Hertzberg, Vicki"' showing total 627 results
Start Over Author "Hertzberg, Vicki"
627 results on '"Hertzberg, Vicki"'

4. Competence-Level Prediction and Resume & Job Description Matching Using Context-Aware Transformer Models

Author

Li, Changmao, Fisher, Elaine, Thomas, Rebecca, Pittard, Steve, Hertzberg, Vicki, and Choi, Jinho D.

Subjects
Computer Science - Computation and Language, I.2.7
Abstract

This paper presents a comprehensive study on resume classification to reduce the time and labor needed to screen an overwhelming number of applications significantly, while improving the selection of suitable candidates. A total of 6,492 resumes are extracted from 24,933 job applications for 252 positions designated into four levels of experience for Clinical Research Coordinators (CRC). Each resume is manually annotated to its most appropriate CRC position by experts through several rounds of triple annotation to establish guidelines. As a result, a high Kappa score of 61% is achieved for inter-annotator agreement. Given this dataset, novel transformer-based classification models are developed for two tasks: the first task takes a resume and classifies it to a CRC level (T1), and the second task takes both a resume and a job description to apply and predicts if the application is suited to the job T2. Our best models using section encoding and multi-head attention decoding give results of 73.3% to T1 and 79.2% to T2. Our analysis shows that the prediction errors are mostly made among adjacent CRC levels, which are hard for even experts to distinguish, implying the practical value of our models in real HR platforms., Comment: Accepted by the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)

Published
2020

10. GASP: Graph-Based Approximate Sequential Pattern Mining for Electronic Health Records

Author

Dong, Wenqin, Lee, Eric W., Hertzberg, Vicki Stover, Simpson, Roy L., Ho, Joyce C., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Bellatreche, Ladjel, editor, Dumas, Marlon, editor, Karras, Panagiotis, editor, Matulevičius, Raimundas, editor, Awad, Ahmed, editor, Weidlich, Matthias, editor, Ivanović, Mirjana, editor, and Hartig, Olaf, editor

Published
2021
Full Text
View/download PDF

13. Dynamic communicability and epidemic spread: a case study on an empirical dynamic contact network

Author

Chen, Isabel, Benzi, Michele, Chang, Howard H., and Hertzberg, Vicki S.

Subjects
Physics - Physics and Society, Computer Science - Social and Information Networks
Abstract

We analyze a recently proposed temporal centrality measure applied to an empirical network based on person-to-person contacts in an emergency department of a busy urban hospital. We show that temporal centrality identifies a distinct set of top-spreaders than centrality based on the time-aggregated binarized contact matrix, so that taken together, the accuracy of capturing top-spreaders improves significantly. However, with respect to predicting epidemic outcome, the temporal measure does not necessarily outperform less complex measures. Our results also show that other temporal markers such as duration observed and the time of first appearance in the the network can be used in a simple predictive model to generate predictions that capture the trend of the observed data remarkably well., Comment: 31 pages, 15 figures, 11 tables; typos corrected; references added; Figure 3 added; some changes to the conclusion and introduction

Published
2016

16. The Airplane Cabin Microbiome

Author

The FlyHealthy Research Team, Weiss, Howard, Hertzberg, Vicki Stover, Dupont, Chris, Espinoza, Josh L., Levy, Shawn, Nelson, Karen, and Norris, Sharon

Published
2019

19. Very early administration of progesterone for acute traumatic brain injury.

Author

Wright, David W, Yeatts, Sharon D, Silbergleit, Robert, Palesch, Yuko Y, Hertzberg, Vicki S, Frankel, Michael, Goldstein, Felicia C, Caveney, Angela F, Howlett-Smith, Harriet, Bengelink, Erin M, Manley, Geoffrey T, Merck, Lisa H, Janis, L Scott, Barsan, William G, and NETT Investigators

Subjects
NETT Investigators, Humans, Brain Injuries, Phlebitis, Progesterone, Treatment Failure, Infusions, Intravenous, Drug Administration Schedule, Glasgow Outcome Scale, Injury Severity Score, Double-Blind Method, Accidents, Traffic, Adolescent, Adult, Aged, Aged, 80 and over, Middle Aged, Female, Male, Young Adult, Clinical Research, Physical Injury - Accidents and Adverse Effects, Clinical Trials and Supportive Activities, Traumatic Brain Injury (TBI), Neurosciences, Brain Disorders, Patient Safety, Traumatic Head and Spine Injury, Rehabilitation, Evaluation of treatments and therapeutic interventions, 6.1 Pharmaceuticals, Injuries and accidents, Good Health and Well Being, Medical and Health Sciences, General & Internal Medicine
Abstract

BackgroundTraumatic brain injury (TBI) is a major cause of death and disability worldwide. Progesterone has been shown to improve neurologic outcome in multiple experimental models and two early-phase trials involving patients with TBI.MethodsWe conducted a double-blind, multicenter clinical trial in which patients with severe, moderate-to-severe, or moderate acute TBI (Glasgow Coma Scale score of 4 to 12, on a scale from 3 to 15, with lower scores indicating a lower level of consciousness) were randomly assigned to intravenous progesterone or placebo, with the study treatment initiated within 4 hours after injury and administered for a total of 96 hours. Efficacy was defined as an increase of 10 percentage points in the proportion of patients with a favorable outcome, as determined with the use of the stratified dichotomy of the Extended Glasgow Outcome Scale score at 6 months after injury. Secondary outcomes included mortality and the Disability Rating Scale score.ResultsA total of 882 of the planned sample of 1140 patients underwent randomization before the trial was stopped for futility with respect to the primary outcome. The study groups were similar with regard to baseline characteristics; the median age of the patients was 35 years, 73.7% were men, 15.2% were black, and the mean Injury Severity Score was 24.4 (on a scale from 0 to 75, with higher scores indicating greater severity). The most frequent mechanism of injury was a motor vehicle accident. There was no significant difference between the progesterone group and the placebo group in the proportion of patients with a favorable outcome (relative benefit of progesterone, 0.95; 95% confidence interval [CI], 0.85 to 1.06; P=0.35). Phlebitis or thrombophlebitis was more frequent in the progesterone group than in the placebo group (relative risk, 3.03; CI, 1.96 to 4.66). There were no significant differences in the other prespecified safety outcomes.ConclusionsThis clinical trial did not show a benefit of progesterone over placebo in the improvement of outcomes in patients with acute TBI. (Funded by the National Institute of Neurological Disorders and Stroke and others; PROTECT III ClinicalTrials.gov number, NCT00822900.).

Published
2014

23. Black-White Differences in Chronic Stress Exposures to Predict Preterm Birth: Interpretable, Race/Ethnicity-Specific Machine Learning Models.

Author

Sangmi KIM, HIGGINS, Melinda K., BRENNAN, Patricia, SLAVICH, George M., Li XIONG, HERTZBERG, Vicki, KELLY, Ursula, and DUNLOP, Anne L.

Abstract

We developed Multivariate Adaptive Regression Splines (MARS) machine learning models of chronic stressors using the Pregnancy Risk Assessment Monitoring System data (2012-2017) to predict preterm birth (PTB) more accurately and identify chronic stressors driving PTB among non-Hispanic (N-H) Black and N-H White pregnant women in the U.S. We trained the MARS models using 5-fold cross-validation, whose performance was evaluated with AUC. We computed variable importance for PTB prediction. Our models showed high accuracy (AUC: 0.754-0.765). The number of prenatal care visits, premature rupture of membrane, and medical conditions were the most important variables in predicting PTB across the populations. Chronic stressors (e.g., low maternal education and violence) and their correlates were pivotal for PTB prediction only for N-H Black women. Interpretable, race/ethnicity-specific MARS models can predict PTB accurately and explain the most impactful life stressors and their magnitude of effect on PTB risk among N-H Black and N-H White women. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

30. The Airplane Cabin Microbiome

Author

Weiss, Howard, Hertzberg, Vicki Stover, Dupont, Chris, Espinoza, Josh L., Levy, Shawn, Nelson, Karen, Norris, Sharon, and The FlyHealthy Research Team

Published
2019
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results