PMID- 38263446
OWN - NLM
STAT- MEDLINE
DCOM- 20240125
LR  - 20240201
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 14
IP  - 1
DP  - 2024 Jan 23
TI  - Application of multi-gene genetic programming to the prognosis prediction of 
      COVID-19 using routine hematological variables.
PG  - 2043
LID - 10.1038/s41598-024-52529-y [doi]
LID - 2043
AB  - Identifying patients who may develop severe COVID-19 has been of interest to 
      clinical physicians since it facilitates personalized treatment and optimizes the 
      allocation of medical resources. In this study, multi-gene genetic programming 
      (MGGP), as an advanced artificial intelligence (AI) tool, was used to determine 
      the importance of laboratory predictors in the prognosis of COVID-19 patients. 
      The present retrospective study was conducted on 1455 patients with COVID-19 (727 
      males and 728 females), who were admitted to Allameh Behlool Gonabadi Hospital, 
      Gonabad, Iran in 2020-2021. For each patient, the demographic characteristics, 
      common laboratory tests at the time of admission, duration of hospitalization, 
      admission to the intensive care unit (ICU), and mortality were collected through 
      the electronic information system of the hospital. Then, the data were normalized 
      and randomly divided into training and test data. Furthermore, mathematical 
      prediction models were developed by MGGP for each gender. Finally, a sensitivity 
      analysis was performed to determine the significance of input parameters on the 
      COVID-19 prognosis. Based on the achieved results, MGGP is able to predict the 
      mortality of COVID-19 patients with an accuracy of 60-92%, the duration of 
      hospital stay with an accuracy of 53-65%, and admission to the ICU with an 
      accuracy of 76-91%, using common hematological tests at the time of admission. 
      Also, sensitivity analysis indicated that blood urea nitrogen (BUN) and aspartate 
      aminotransferase (AST) play key roles in the prognosis of COVID-19 patients. AI 
      techniques, such as MGGP, can be used in the triage and prognosis prediction of 
      COVID-19 patients. In addition, due to the sensitivity of BUN and AST in the 
      estimation models, further studies on the role of the mentioned parameters in the 
      pathophysiology of COVID-19 are recommended.
CI  - (c) 2024. The Author(s).
FAU - Niazkar, Hamid Reza
AU  - Niazkar HR
AUID- ORCID: 0000-0002-6143-9979
AD  - Gonabad University of Medical Sciences, Gonabad, Iran. hrn185@hotmail.com.
AD  - Breast Diseases Research Center, Shiraz University of Medical Sciences, Shiraz, 
      Iran. hrn185@hotmail.com.
FAU - Moshari, Jalil
AU  - Moshari J
AUID- ORCID: 0000-0003-0483-2747
AD  - Pediatric Department, School of Medicine, Gonabad University of Medical Sciences, 
      Gonabad, Iran.
FAU - Khajavi, Abdoljavad
AU  - Khajavi A
AUID- ORCID: 0000-0001-6625-6998
AD  - Community Medicine Department, School of Medicine, Gonabad University of Medical 
      Sciences, Gonabad, Iran.
FAU - Ghorbani, Mohammad
AU  - Ghorbani M
AUID- ORCID: 0000-0001-8829-4258
AD  - Laboratory hematology and Transfusion medicine, Department of Medical Laboratory 
      Sciences, Faculty of Allied Medicine, Gonabad University of Medical Sciences, 
      Gonabad, Iran.
FAU - Niazkar, Majid
AU  - Niazkar M
AUID- ORCID: 0000-0002-5022-1026
AD  - Faculty of Engineering, Free University of Bozen-Bolzano, Piazza Universita 5, 
      39100 Bolzano, Italy.
FAU - Negari, Aida
AU  - Negari A
AUID- ORCID: 0000-0002-1063-4432
AD  - Gonabad University of Medical Sciences, Gonabad, Iran.
LA  - eng
PT  - Journal Article
DEP - 20240123
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
SB  - IM
MH  - Female
MH  - Male
MH  - Humans
MH  - *Artificial Intelligence
MH  - Retrospective Studies
MH  - *COVID-19
MH  - Prognosis
MH  - Blood Coagulation Tests
PMC - PMC10806074
COIS- The authors declare no competing interests.
EDAT- 2024/01/24 00:42
MHDA- 2024/01/25 06:44
PMCR- 2024/01/23
CRDT- 2024/01/24 00:00
PHST- 2023/05/17 00:00 [received]
PHST- 2024/01/19 00:00 [accepted]
PHST- 2024/01/25 06:44 [medline]
PHST- 2024/01/24 00:42 [pubmed]
PHST- 2024/01/24 00:00 [entrez]
PHST- 2024/01/23 00:00 [pmc-release]
AID - 10.1038/s41598-024-52529-y [pii]
AID - 52529 [pii]
AID - 10.1038/s41598-024-52529-y [doi]
PST - epublish
SO  - Sci Rep. 2024 Jan 23;14(1):2043. doi: 10.1038/s41598-024-52529-y.