Yousif, MG, Zeiny, L, Tawfeeq, S, Al-Amran, F, Sadeq, AM and Al-Jumeily, D (2023) Predicting perinatal outcomes in women affected by COVID-19: An artificial intelligence (AI) approach. Journal of Medicine and Life, 16 (9). pp. 1421-1427. ISSN 1844-122X

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Abstract

This study aimed to explore the role of artificial intelligence (AI) in predicting perinatal outcomes among women with COVID-19. Data was collected from hospitals in the Middle Euphrates and Southern regions of Iraq, with 152 pregnant patients included in the study. Patients were categorized into mild and severe infection groups, and their serum samples were analyzed for mineral levels (magnesium, copper, calcium, sodium, potassium, zinc, selenium, and iron) and immune factors (IL-6, IL-8, IL-32, IL-10, IL-18, IL-37, IL-38, IL-36, and IL-1). The findings revealed significant associations between specific mineral levels, immune factors, and perinatal outcomes. Mineral levels such as magnesium (75.5% mild infection, 80.9% severe infection), copper (68.2% mild infection, 64.3% severe infection), calcium ion (81.8% mild infection, 76.2% severe infection), sodium (70.9% mild infection, 69.0% severe infection), potassium (72.7% mild infection, 71.4% severe infection), zinc (61.8% mild infection, 54.8% severe infection), selenium (78.2% mild infection, 82.9% severe infection), and iron (74.5% mild infection, 68.3% severe infection) showed varying per-centages associated with mild and severe infections. Immune factors such as IL-6 (32% mild infection, 21% severe infection), IL-8 (15% mild infection, 7% severe infection), IL-32 (24% mild infection, 9% severe infection), IL-10 (7% mild infection, no severe infection), IL-18 (13% mild infection, 11% severe infection) demonstrated varying per-centages associated with perinatal outcomes, while other interleukins showed no changes in severe infections. These results highlight the potential of AI in predicting outcomes for pregnant women with COVID-19, which could aid in improving their management and care. Further research and validation of predictive models are recommended to enhance accuracy and applicability.

Item Type:	Article
Uncontrolled Keywords:	Humans; Selenium; Potassium; Sodium; Calcium; Magnesium; Copper; Iron; Zinc; Interleukin-8; Interleukin-6; Interleukin-10; Interleukin-18; Immunologic Factors; Pregnancy; Artificial Intelligence; Female; COVID-19; AI prediction; COVID-19; Middle Euphrates; healthcare; perinatal outcomes; pregnancy; retrospective study; Humans; Female; Pregnancy; Copper; Magnesium; Selenium; Interleukin-10; Calcium; Interleukin-18; COVID-19; Artificial Intelligence; Interleukin-6; Interleukin-8; Zinc; Iron; Potassium; Sodium; Immunologic Factors
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RG Gynecology and obstetrics
Divisions:	Computer Science & Mathematics
Publisher:	Carol Davila University Press
SWORD Depositor:	A Symplectic
Date Deposited:	08 May 2024 08:48
Last Modified:	08 May 2024 08:48
DOI or ID number:	10.25122/jml-2023-0214
URI:	https://researchonline.ljmu.ac.uk/id/eprint/23192

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