Application of Machine Learning Models in Systemic Lupus Erythematosus.
Fulvia CeccarelliFrancesco NatalucciLicia PicciarielloClaudia CiancarellaGiulio DolciniAngelica GattamelataCristiano AlessandriFabrizio ContiPublished in: International journal of molecular sciences (2023)
Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease and is extremely heterogeneous in terms of immunological features and clinical manifestations. This complexity could result in a delay in the diagnosis and treatment introduction, with impacts on long-term outcomes. In this view, the application of innovative tools, such as machine learning models (MLMs), could be useful. Thus, the purpose of the present review is to provide the reader with information about the possible application of artificial intelligence in SLE patients from a medical perspective. To summarize, several studies have applied MLMs in large cohorts in different disease-related fields. In particular, the majority of studies focused on diagnosis and pathogenesis, disease-related manifestations, in particular Lupus Nephritis, outcomes and treatment. Nonetheless, some studies focused on peculiar features, such as pregnancy and quality of life. The review of published data demonstrated the proposal of several models with good performance, suggesting the possible application of MLMs in the SLE scenario.
Keyphrases
- systemic lupus erythematosus
- machine learning
- artificial intelligence
- big data
- disease activity
- end stage renal disease
- deep learning
- case control
- newly diagnosed
- multiple sclerosis
- chronic kidney disease
- rheumatoid arthritis
- ejection fraction
- healthcare
- prognostic factors
- pregnant women
- type diabetes
- health information
- drug induced
- preterm birth
- weight loss
- glycemic control