Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data

• Verfasst von: Schaack, Dominik [VerfasserIn]
• Verfasst von: Weigand, Markus A. [VerfasserIn]
• Verfasst von: Uhle, Florian [VerfasserIn]
• Titel: Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data
• Verf.angabe: Dominik Schaack, Markus A. Weigand, Florian Uhle
• E-Jahr: 2021
• Jahr: May 17, 2021
• Umfang: 18 S.
• Teil: volume:16
• Teil: year:2021
• Teil: number:5
• Teil: elocationid:e0251800
• Teil: pages:1-18
• Teil: extent:18
• Fussnoten: Gesehen am 10.07.2021
• Titel Quelle: Enthalten in: PLOS ONE
• Ort Quelle: San Francisco, California, US : PLOS, 2006
• Jahr Quelle: 2021
• Band/Heft Quelle: 16(2021), 5, Artikel-ID e0251800, Seite 1-18
• ISSN Quelle: 1932-6203
• DOI: doi:10.1371/journal.pone.0251800
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Diagnostic medicine
• Sach-SW: Gene expression
• Sach-SW: Machine learning
• Sach-SW: Microarrays
• Sach-SW: Neural networks
• Sach-SW: Sepsis
• Sach-SW: Support vector machines
• Sach-SW: Systemic inflammatory response syndrome
• K10plus-PPN: 1765992125

Abstract

We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analysis. Publicly available data series were extracted from NCBI Gene Expression Omnibus and EMBL-EBI ArrayExpress to create a comprehensive meta-analysis microarray expression set (meta-expression set). Measurements had to be obtained via microarray-technique from whole blood samples of adult or pediatric patients with sepsis diagnosed based on international consensus definition immediately after admission to the intensive care unit. We aggregate trauma patients, systemic inflammatory response syndrome (SIRS) patients, and healthy controls in a non-septic entity. Differential expression (DE) analysis is compared with machine-learning-based solutions like decision tree (DT), random forest (RF), support vector machine (SVM), and deep-learning neural networks (DNNs). We evaluated classifier training and discrimination performance in 100 independent iterations. To test diagnostic resilience, we gradually degraded expression data in multiple levels. Clustering of expression values based on DE genes results in partial identification of sepsis samples. In contrast, RF, SVM, and DNN provide excellent diagnostic performance measured in terms of accuracy and area under the curve (>0.96 and >0.99, respectively). We prove DNNs as the most resilient methodology, virtually unaffected by targeted removal of DE genes. By surpassing most other published solutions, the presented approach substantially augments current diagnostic capability in intensive care medicine.