Systems and Precision Medicine in Necrotizing Soft Tissue Infections

Vitor A.P. Martins dos Santos*, Christopher Hardt, Steinar Skrede, Edoardo Saccenti

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Necrotizing soft tissue infections (NSTI) are multifactorial and characterized by dysfunctional, time dependent, highly varying hyper- to hypo-inflammatory host responses contributing to disease severity. Furthermore, host-pathogen interactions are diverse and difficult to identify and characterize, due to the many different disease endotypes. There is a need for both refined bedside diagnostics as well as novel targeted treatment options to improve outcome in NSTI. In order to achieve clinically relevant results and to guide preclinical and clinical research the vast amount of fragmented clinical and experimental datasets, which often include omics data at different levels (transcriptomics, proteomics, metabolomics, etc.), need to be organized, harmonized, integrated, and analyzed taking into account the Big Data nature of these datasets. In this chapter, we address these matters from a systems perspective and yet personalized approach. The chapter provides an overview on the increasingly more frequent use of Big Data and Artificial Intelligence (AI) to aggregate and generate knowledge from burgeoning clinical and biochemical information, addresses the challenges to manage this information, and summarizes current efforts to develop robust computer-aided clinical decision support systems so to tackle the serious challenges in NSTI diagnosis, stratification, and optimized tailored therapy.
Original languageEnglish
Title of host publicationNecrotizing Soft Tissue Infections
EditorsAnna Norrby-Teglund, Mattias Svensson, Steinar Skrede
PublisherSpringer
Chapter12
Pages187-207
Number of pages21
ISBN (Electronic)9783030576165
ISBN (Print)9783030576158
DOIs
Publication statusE-pub ahead of print - 21 Oct 2020

Publication series

NameAdvances in Experimental Medicine and Biology
Volume1294
ISSN (Print)0065-2598
ISSN (Electronic)2214-8019

Keywords

  • Artificial intelligence
  • Big data
  • Clinical decision support systems
  • Deep learning
  • Information management
  • Personalized medicine
  • Semantic technologies

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