Abstract
Hospital-associated infections (HAIs) have become a major threat and lead to almost over 90,000 deaths globally per year. Advanced methods of diagnosis and detection would help healthcare workers rapidly determine the most efficient therapeutic process, thereby reducing the use of broad-spectrum antibiotics. Most of the time, HAIs are not detected early, which leads to delays in administering effective treatments. From both clinical and data science points of view, HAI management is a highly complex area. The main aim of integrating systems biology-based approaches such as artificial intelligence (AI) and artificial neural network (ANN), machine learning (ML), big data analysis, and other bioinformatic-based modeling is to regulate the structural and functional system of the biological arrangements. The introduction of systems biology-associated diagnostic, treatment, management, and monitoring methods has led to the unraveling of the underlying genetic associations among the HAI- causative agents and the host. This chapter presents an overview of various types of systems biology-based approaches in controlling the advent of HAIs and how they can be used to monitor and predict the risk of HAI development in hospitalized patients with other comorbidities.
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Ghosh, S., Lahiri, D., Nag, M., Ray, R.R., Bhattacharya, D. (2024). Systems Biology and Hospital-Associated Infections. In: Joshi, S., Ray, R.R., Nag, M., Lahiri, D. (eds) Systems Biology Approaches: Prevention, Diagnosis, and Understanding Mechanisms of Complex Diseases. Springer, Singapore. https://doi.org/10.1007/978-981-99-9462-5_7
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