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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.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None Abstract words count: 389 Manuscript word count: 2870 NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice.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 The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.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..