Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a Novel Deep Neural Network
Naseem et al.,
Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a Novel Deep..,
medRxiv, doi:10.1101/2020.12.13.20247254 (Preprint)
Retrospective 1,214 hospitalized patients in Pakistan, 77 HCQ patients, showing 33% lower mortality with HCQ, multivariate Cox HR 0.67, p = 0.34.
risk of death, 33.3% lower, RR 0.67, p = 0.34, treatment 77, control 1,137, multivariate Cox.
|
Effect extraction follows pre-specified rules prioritizing more serious outcomes. Submit updates
|
Naseem et al., 14 Dec 2020, retrospective, Pakistan, preprint, 5 authors.
Abstract: medRxiv preprint doi: https://doi.org/10.1101/2020.12.13.20247254; this version posted August 29, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
All rights reserved. No reuse allowed without permission.
Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient
setting using a Novel Deep Neural Network.
Maleeha Naseem1, Hajra Arshad2, Syeda Amrah Hashmi2, Furqan Irfan3, Fahad Shabbir
Ahmed4, 5, 6
1
Department of Community Health Sciences, Aga Khan University, Karachi, Pakistan 74900.
2
Medical College, Aga Khan University, Karachi, Pakistan 74900.
3
College of Osteopathic Medicine, Michigan State University, East Lansing, MI 48824.
4
Clinicaro Machine Learning Group, New Haven, CT, 06510.
5
Department of Pathology, Wayne State University, Detroit, MI 48201.
6
Corresponding author
Email Address: Fahad Shabbir Ahmed, fahadshabbirahmed@gmail.com
Corresponding author: Fahad Shabbir Ahmed, Lead Scientist, Clinicaro Machine Learning
group; Department of Pathology, Wayne State University / Detroit Medical Center. Harper
Professional Building, 4160 John R St, Detroit, MI 48201. Phone: 631-644-3981; Email;
fahadshabbirahmed@gmail.com.
Conflict of Interest: None.
Disclosures: None
Abstract words count: 389
Manuscript word count: 2870
NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.
medRxiv preprint doi: https://doi.org/10.1101/2020.12.13.20247254; this version posted August 29, 2021. The copyright holder for this preprint
(which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
All rights reserved. No reuse allowed without permission.
ABSTRACT
Background
The second wave of COVID-19 pandemic is anticipated to be worse than the initial one and
will strain the healthcare systems even more during the winter months. Our aim was to
develop a machine learning-based model to predict mortality using the deep learning Neo-V
framework. We hypothesized this novel machine learning approach could be applied to
COVID-19 patients to predict mortality successfully with high accuracy.
Methods
The current Deep-Neo-V model is built on our previously statistically rigorous machine
learning framework [Fahad-Liaqat-Ahmad Intensive Machine (FLAIM) framework] that
evaluated statistically significant risk factors, generated new combined variables and then
supply these risk factors to deep neural network to predict mortality in RT-PCR positive
COVID-19 patients in the inpatient setting. We analyzed adult patients (≥18 years) admitted
to the Aga Khan University Hospital, Pakistan with a working diagnosis of COVID-19
infection (n=1228). We excluded patients that were negative on COVID-19 on RT-PCR, had
incomplete or missing health records. The first phase selection of risk factor was done using
Cox-regression univariate and multivariate analyses. In the second phase, we generated new
variables and tested those statistically significant for mortality and in the third and final phase
we applied deep neural networks and other traditional machine learning models like Decision
Tree Model, k-nearest neighbor models and others.
Results
A total of 1228 cases were diagnosed as COVID-19 infection, we excluded 14 patients after
the exclusion criteria and..
Late treatment
is less effective
Please send us corrections, updates, or comments. Vaccines and
treatments are complementary. All practical, effective, and safe means should
be used based on risk/benefit analysis. No treatment, vaccine, or intervention
is 100% available and effective for all current and future variants. We do not
provide medical advice. Before taking any medication, consult a qualified
physician who can provide personalized advice and details of risks and
benefits based on your medical history and situation.
FLCCC and
WCH
provide treatment protocols.
Submit