Scientific Results

  • ID:
    publications-4035
  • Type:
    article
  • Year:
    2021
  • Authors:
    Saberi-Movahed, Farshad and F, Saberi-Movahed and Saberi-Movahed, Farshad and Saberi-Movahed, Farid and Mohammadifard, Mahyar and Mehrpooya, Adel and Mohammadifard, Mahyar and M, Mohammadifard and Mehrpooya, Adel and Mehrpooya, Adel and Rezaei-Ravari, Mohammad and M, Rezaei-Ravari and Berahmand, Kamal and Berahmand, Kamal and Rostami, Mehrdad and Rostami, Mehrdad and Rostami, Mehrdad and Karami, Saeed and S, Karami and Karami, Saeed and Najafzadeh, Mohammad and Najafzadeh, Mohammad and Hajinezhad, Davood and Jamshidi, Mina and Hajinezhad, Davood and Jamshidi, Mina and M, Jamshidi and Abedi, Farshid and Abedi, Farshid and Mohammadifard, Mahtab and Mohammadifard, Mahtab and E, Farbod and Farbod, Elnaz and Safavi, Farinaz and Farbod, Elnaz and Safavi, Farinaz and Dorvash, Mohammadreza and M, Dorvash and Dorvash, Mohammadreza and S, Vahedi and Dorvash, Mohammadreza and Dorvash, Mohammadreza and Eftekhari, Mohammad and Eftekhari, Mahdi and Vahedi, Shahrzad and Vahedi, Shahrzad and Tavassoly, Iman and Saberi-Movahed, Farid and Tavassoly, Iman
  • Title:
    Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods
  • Venue/Journal:
    medRxiv
  • DOI:
    10.1101/2021.07.07.21259699
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  • Abstract:
    One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O 2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.
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