Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases

Abstract

The liver is a key organ involved in a wide range of functions, whose damage can lead to chronic liver disease (CLD). CLD accounts for more than two million deaths worldwide, becoming a social and economic burden for most countries. Among the different factors that can cause CLD, alcohol abuse, viruses, drug treatments, and unhealthy dietary patterns top the list. These conditions prompt and perpetuate an inflammatory environment and oxidative stress imbalance that favor the development of hepatic fibrogenesis. High stages of fibrosis can eventually lead to cirrhosis or hepatocellular carcinoma (HCC). Despite the advances achieved in this field, new approaches are needed for the prevention, diagnosis, treatment, and prognosis of CLD. In this context, the scientific com-munity is using machine learning (ML) algorithms to integrate and process vast amounts of data with unprecedented performance. ML techniques allow the integration of anthropometric, genetic, clinical, biochemical, dietary, lifestyle and omics data, giving new insights to tackle CLD and bringing personalized medicine a step closer. This review summarizes the investigations where ML techniques have been applied to study new approaches that could be used in inflammatory-related, hepatitis viruses-induced, and coronavirus disease 2019-induced liver damage and enlighten the factors involved in CLD development.

Keywords: Comorbidity; Liver disease; Liver inflammation; Machine learning; Viral diseases.

Figure 1

Factors involved in the development of chronic liver disease triggering associated processes that lead to increased fibrosis stage.

Figure 2

Data implicated in the onset of inflammatory-related liver diseases can be used to train machine learning algorithms for prediction, diagnosis, treatment, and prognosis of chronic liver disease, leading the way to personalized medicine.