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Mehdi Nourelahi

Showing results (1-10 of 9) with videos related to

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Asian Pacific Journal of Cancer Prevention : APJCP|June 28, 2021
Evaluation of Twenty Genes in Prognosis of Patients with Ovarian Cancer Using Four Different Clustering MethodsSaeedeh Pourahmad, Somayeh Foroozani, Mehdi Nourelahi, et al.
Acute and Critical Care|November 11, 2021
A machine learning model for predicting favorable outcome in severe traumatic brain injury patients after 6 monthsMehdi Nourelahi, Fardad Dadboud, Hosseinali Khalili, et al.
Journal of the American Medical Informatics Association : JAMIA|April 22, 2025
A resource for Logical Observation Identifiers Names and Codes terms that may be associated with identifying informationMehdi Nourelahi, Eugene M Sadhu, Malarkodi J Samayamuthu, et al.
Medrxiv : the Preprint Server for Health Sciences|August 13, 2025
Automated and interoperable methods for generalizable development of clinical machine-learning models for predicting neuromorbidity in critically ill childrenRuoting Li, Christopher M Horvat, Mehdi Nourelahi, et al.
Medrxiv : the Preprint Server for Health Sciences|February 27, 2026
Leveraging Expert Knowledge and Causal Structure Learning to Build Parsimonious Models of Acute Brain Dysfunction in the Pediatric Intensive Care Unit (PICU)Eddie Pérez Claudio, Christopher M Horvat, W Michael Taylor, et al.
Medrxiv : the Preprint Server for Health Sciences|June 10, 2024
Machine Learning - Based Bleeding Risk Predictions in Atrial Fibrillation Patients on Direct Oral AnticoagulantsRahul Chaudhary, Mehdi Nourelahi, Floyd W Thoma, et al.
The American Journal of Cardiology|February 27, 2025
Machine Learning Predicts Bleeding Risk in Atrial Fibrillation Patients on Direct Oral AnticoagulantRahul Chaudhary, Mehdi Nourelahi, Floyd W Thoma, et al.
PLOS Digital Health|July 11, 2025
Raising awareness of potential biases in medical machine learning: Experience from a DatathonHarry Hochheiser, Jesse Klug, Thomas Mathie, et al.
Medrxiv : the Preprint Server for Health Sciences|November 6, 2024
Raising awareness of potential biases in medical machine learning: Experience from a DatathonHarry Hochheiser, Jesse Klug, Thomas Mathie, et al.
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Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
Asian Pacific Journal of Cancer Prevention : APJCP|June 28, 2021
Evaluation of Twenty Genes in Prognosis of Patients with Ovarian Cancer Using Four Different Clustering MethodsSaeedeh Pourahmad, Somayeh Foroozani, Mehdi Nourelahi, et al.
Acute and Critical Care|November 11, 2021
A machine learning model for predicting favorable outcome in severe traumatic brain injury patients after 6 monthsMehdi Nourelahi, Fardad Dadboud, Hosseinali Khalili, et al.
Journal of the American Medical Informatics Association : JAMIA|April 22, 2025
A resource for Logical Observation Identifiers Names and Codes terms that may be associated with identifying informationMehdi Nourelahi, Eugene M Sadhu, Malarkodi J Samayamuthu, et al.
Medrxiv : the Preprint Server for Health Sciences|August 13, 2025
Automated and interoperable methods for generalizable development of clinical machine-learning models for predicting neuromorbidity in critically ill childrenRuoting Li, Christopher M Horvat, Mehdi Nourelahi, et al.
Medrxiv : the Preprint Server for Health Sciences|February 27, 2026
Leveraging Expert Knowledge and Causal Structure Learning to Build Parsimonious Models of Acute Brain Dysfunction in the Pediatric Intensive Care Unit (PICU)Eddie Pérez Claudio, Christopher M Horvat, W Michael Taylor, et al.
Medrxiv : the Preprint Server for Health Sciences|June 10, 2024
Machine Learning - Based Bleeding Risk Predictions in Atrial Fibrillation Patients on Direct Oral AnticoagulantsRahul Chaudhary, Mehdi Nourelahi, Floyd W Thoma, et al.
The American Journal of Cardiology|February 27, 2025
Machine Learning Predicts Bleeding Risk in Atrial Fibrillation Patients on Direct Oral AnticoagulantRahul Chaudhary, Mehdi Nourelahi, Floyd W Thoma, et al.
PLOS Digital Health|July 11, 2025
Raising awareness of potential biases in medical machine learning: Experience from a DatathonHarry Hochheiser, Jesse Klug, Thomas Mathie, et al.
Medrxiv : the Preprint Server for Health Sciences|November 6, 2024
Raising awareness of potential biases in medical machine learning: Experience from a DatathonHarry Hochheiser, Jesse Klug, Thomas Mathie, et al.
Pageof 1