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- W4310122789 endingPage "6248" @default.
- W4310122789 startingPage "6230" @default.
- W4310122789 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." @default.
- W4310122789 created "2022-11-30" @default.
- W4310122789 creator A5011514095 @default.
- W4310122789 creator A5013927228 @default.
- W4310122789 creator A5048675989 @default.
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- W4310122789 creator A5057693346 @default.
- W4310122789 creator A5062696393 @default.
- W4310122789 creator A5090395437 @default.
- W4310122789 date "2022-11-28" @default.
- W4310122789 modified "2023-09-27" @default.
- W4310122789 title "Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases" @default.
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