Document Type
Article
Publication Date
2023
Original Citation
Rashid A,
Anwary A,
Al-Obeidat F,
Brierley J,
Uddin M,
Alkhzaimi H,
Sarpal A,
Toufiq M,
Malik Z,
Kadwa R,
Khilnani P,
Shaikh MG,
Benakatti G,
Sharief J,
Zaki SA,
Zeyada A,
Al-Dubai A,
Hafez W,
Hussain A.
Application of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machinApplication of a gene modular approach for clinical phenotype genotype association and sepsis prediction using machine learning in meningococcal sepsise learning in meningococcal sepsis Informatics in Medicine Unlocked. 2023; 41():
DOI
https://doi.org/10.1016/j.imu.2023.101293
Abstract
Sepsis is a major global health concern causing high morbidity and mortality rates. Our study utilized a Meningo- coccal Septic Shock (MSS) temporal dataset to investigate the correlation between gene expression (GE) changes and clinical features. The research used Weighted Gene Co-expression Network Analysis (WGCNA) to establish links between gene expression and clinical parameters in infants admitted to the Pediatric Critical Care Unit with MSS. Additionally, various machine learning (ML) algorithms, including Support Vector Machine (SVM), Naive Bayes, K- Nearest Neighbors (KNN), Decision Tree, Random Forest, and Artificial Neural Network (ANN) were implemented to predict sepsis survival. The findings revealed a transition in gene function pathways from nuclear to cytoplasmic to extracellular, corresponding with Pediatric Logistic Organ Dysfunction score (PELOD) readings at 0, 24, and 48 h. ANN was the most accurate of the six ML models applied for survival prediction. This study successfully correlated PELOD with transcriptomic data, mapping enriched GE modules in acute sepsis. By integrating network analysis methods to identify key gene modules and using machine learning for sepsis prognosis, this study offers valuable insights for precision-based treatment strategies in future research. The observed temporal-spatial pattern of cellular recovery in sepsis could prove useful in guiding clinical management and therapeutic interventions.
Comments
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by- nc-nd/4.0/).