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Nariman Ammar
Researcher at University of Tennessee Health Science Center
Publications - 40
Citations - 380
Nariman Ammar is an academic researcher from University of Tennessee Health Science Center. The author has contributed to research in topics: Medicine & Context (language use). The author has an hindex of 7, co-authored 30 publications receiving 106 citations. Previous affiliations of Nariman Ammar include Wayne State University.
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Public sentiment analysis and topic modeling regarding COVID-19 vaccines on the Reddit social media platform: A call to action for strengthening vaccine confidence.
TL;DR: This paper conducted a sentiment analysis and Latent Dirichlet Allocation topic modeling on textual data collected from 13 Reddit communities focusing on the COVID-19 vaccine from Dec 1, 2020, to May 15, 2021.
Journal ArticleDOI
Generalisable long COVID subtypes: Findings from the NIH N3C and RECOVER programmes
J. Reese,Hannah Blau,Elena Casiraghi,Timothy Bergquist,Johanna Loomba,Tiffany J. Callahan,Bryan Laraway,Corneliu C. Antonescu,B. Coleman,Michael A. Gargano,Kenneth J. Wilkins,Luca Cappelletti,Tommaso Fontana,Nariman Ammar,Blessy Antony,T. M. Murali,J. Harry Caufield,Guy Karlebach,Julie A. McMurry,Andrew E. Williams,Richard A. Moffitt,Jineta Banerjee,Anthony Solomonides,Hannah Davis,Kristin Kostka,Giorgio Valentini,David Sahner,Christopher G. Chute,Charisse R. Madlock-Brown,Melissa A. Haendel,Peter N. Robinson,Heidi Spratt,Shyam Visweswaran,Joseph E. Flack,Yung Jae Yoo,Davera Gabriel,G. Caleb Alexander,Hemalkumar B. Mehta,Feifan Liu,Robert T. Miller,R. Wong,Elaine Hill,Lorna E. Thorpe,Jasmin Divers +43 more
TL;DR: In this article , a nonlinear similarity function is defined to map from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning.
Journal ArticleDOI
Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation
TL;DR: In this paper, the authors developed and validated ICU length of stay and mortality prediction models, which were used in an intelligent ICU patient monitoring module of an Intelligent Remote Patient Monitoring (IRPM) framework that monitors the health status of patients, and generates timely alerts, maneuver guidance, or reports when adverse medical conditions are predicted.
Journal ArticleDOI
Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development
Nariman Ammar,Arash Shaban-Nejad +1 more
TL;DR: This semantic-driven explainable artificial intelligence prototype can enhance health care practitioners’ ability to provide explanations for the decisions they make and enhance credibility, accountability, and trust in mission-critical areas such as medicine.
Journal ArticleDOI
Predicting Intensive Care Unit Length of Stay and Mortality Using Patient Vital Signs: Machine Learning Model Development and Validation
TL;DR: In this article, the authors developed and validated ICU length of stay and mortality prediction models, which were used in an intelligent ICU patient monitoring module of an Intelligent Remote Patient Monitoring (IRPM) framework that monitors the health status of patients and generates timely alerts, maneuver guidance, or reports when adverse medical conditions are predicted.