TY - CHAP
T1 - Systems and Precision Medicine in Necrotizing Soft Tissue Infections
AU - Martins dos Santos, Vitor A.P.
AU - Hardt, Christopher
AU - Skrede, Steinar
AU - Saccenti, Edoardo
PY - 2020/10/21
Y1 - 2020/10/21
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Big data
KW - Clinical decision support systems
KW - Deep learning
KW - Information management
KW - Personalized medicine
KW - Semantic technologies
U2 - 10.1007/978-3-030-57616-5_12
DO - 10.1007/978-3-030-57616-5_12
M3 - Chapter
C2 - 33079370
AN - SCOPUS:85093818863
SN - 9783030576158
T3 - Advances in Experimental Medicine and Biology
SP - 187
EP - 207
BT - Necrotizing Soft Tissue Infections
A2 - Norrby-Teglund, Anna
A2 - Svensson, Mattias
A2 - Skrede, Steinar
PB - Springer
ER -