Ricardo Henao
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Explore the profile of Ricardo Henao including associated specialties, affiliations and a list of published articles.
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126
Citations
1514
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Recent Articles
1.
Hurst J, Mohan A, Dalapati T, George I, Aquino J, Lugo D, et al.
Nat Commun
. 2025 Mar;
16(1):2383.
PMID: 40064870
Age is among the strongest risk factors for severe outcomes from SARS-CoV-2 infection. Here we describe upper respiratory tract (URT) and peripheral blood transcriptomes of 202 participants (age range of...
2.
Zaribafzadeh H, Henson J, Chan N, Rogers U, Webster W, Schappe T, et al.
Am J Transplant
. 2025 Mar;
PMID: 40057196
Disparities in access to the organ transplant waitlist are well-documented, but research into modifiable factors has been limited due to lack of access to organized pre-waitlisting data. This study aimed...
3.
Chu Y, Luo G, Zhou L, Cao S, Ma G, Meng X, et al.
Nat Commun
. 2025 Mar;
16(1):2262.
PMID: 40050617
Pulmonary artery-vein segmentation is critical for disease diagnosis and surgical planning. Traditional methods rely on Computed Tomography Pulmonary Angiography (CTPA), which requires contrast agents with potential health risks. Non-contrast CT,...
4.
Iglesias-Ussel M, OGrady N, Anderson J, Mitsis P, Burke T, Henao R, et al.
Open Forum Infect Dis
. 2025 Jan;
12(1):ofae729.
PMID: 39758742
Background: Difficulty discriminating bacterial versus viral etiologies of infection drives unwarranted antibacterial prescriptions and, therefore, antibacterial resistance. Methods: Utilizing a rapid portable test that measures peripheral blood host gene expression...
5.
Steinbrink J, Liu Y, Henao R, Tsalik E, Ginsburg G, Ramsburg E, et al.
PLoS One
. 2024 Dec;
19(12):e0311007.
PMID: 39666613
Immune responses during acute infection often contain canonical elements which are shared across the responses to an array of agents within a given pathogen class (i.e., respiratory viral infection). Identification...
6.
Artman C, Henao R
Res Sq
. 2024 Nov;
PMID: 39574901
Video-based deep learning (DL) algorithms often rely on segmentation models to detect clinically important features in transthoracic echocardiograms (TTEs). While effective, these algorithms can be too data hungry for practice...
7.
Wong A, Wischmeyer P, Lee H, Gorenshtein L, Sytsma T, Hao S, et al.
J Surg Res
. 2024 Nov;
304:181-189.
PMID: 39551012
Introduction: Racial and ethnic disparities in malnutrition are well-known, but it is unknown if there are disparities in early nutrition delivery for intensive care unit (ICU) patients, which is associated...
8.
Park C, Jeong H, Henao R, Kheterpal M
JMIR Dermatol
. 2024 Oct;
5(2):e35497.
PMID: 39475766
Background: Deidentifying facial images is critical for protecting patient anonymity in the era of increasing tools for automatic image analysis in dermatology. Objective: The aim of this paper was to...
9.
Huang W, Engelhard M, Coffman M, Hill E, Weng Q, Scheer A, et al.
J Biomed Inform
. 2024 Aug;
157:104711.
PMID: 39182632
Objective: This study aimed to develop a novel approach using routinely collected electronic health records (EHRs) data to improve the prediction of a rare event. We illustrated this using an...
10.
Xia M, Wilson J, Goldstein B, Henao R
Proc Mach Learn Res
. 2024 Aug;
235:54156-54177.
PMID: 39148511
The use of machine learning models to predict clinical outcomes from (longitudinal) electronic health record (EHR) data is becoming increasingly popular due to advances in deep architectures, representation learning, and...