Clustering and Prediction Models for Monitoring Symptoms Severity in Patients with Systemic Lupus Erythematosus ( SLE ) in Oman

المشروع: Other project

تفاصيل المشروع

Description

Systemic lupus erythematosus (SLE) is the prototypic multisystem autoimmune disorder with a broad spectrum of clinical presentations encompassing almost all organs and tissues. The extreme heterogeneity of the disease has led some investigators to propose that SLE represents a "syndrome" rather than a single disease. The management of patients with SLE is aimed not just at immediate control of disease activity, but also at the prevention of organ damage from treatment and comorbidity. Increase survival of an SLE patient implies the development of chronic damage and this damage need to be predicted. As for the SLE causes, they involve genetic factors, environmental factors and hormonal factors. Drugs used to cure SLE patients proved to be useful but to some extent might cause other problems. Particularly in this research we are interested in how to improve the quality of life and the management of systemic lupus erythematosus (SLE) patients. We are investigating the application of data analysis tools in SLE patients for diagnostic and prediction purposes, as well as for understanding contributing factors. In the last era, it has been suggested that Artificial intelligence techniques could provide a useful prediction tools in medical scenarios. Such techniques express complex relationships between input and output data in different ways in order to learn the connectivity between a set of inputs and their outputs [3]. In the medical application, patients? data considered as inputs and the specific outcomes as outputs, and these outcomes could be a drug damage index, disease activity index, identifying factors that impact the severity of the disease which will provide expert with better insight into the disease, and predict the evolution of it.

Layman's description

Systemic lupus erythematosus (SLE) is the prototypic multisystem autoimmune disorder with a broad spectrum of clinical presentations encompassing almost all organs and tissues. The extreme heterogeneity of the disease has led some investigators to propose that SLE represents a "syndrome" rather than a single disease. The management of patients with SLE is aimed not just at immediate control of disease activity, but also at the prevention of organ damage from treatment and comorbidity. Increase survival of an SLE patient implies the development of chronic damage and this damage need to be predicted. As for the SLE causes, they involve genetic factors, environmental factors and hormonal factors. Drugs used to cure SLE patients proved to be useful but to some extent might cause other problems. Particularly in this research we are interested in how to improve the quality of life and the management of systemic lupus erythematosus (SLE) patients. We are investigating the application of data analysis tools in SLE patients for diagnostic and prediction purposes, as well as for understanding contributing factors. In the last era, it has been suggested that Artificial intelligence techniques could provide a useful prediction tools in medical scenarios. Such techniques express complex relationships between input and output data in different ways in order to learn the connectivity between a set of inputs and their outputs [3]. In the medical application, patients? data considered as inputs and the specific outcomes as outputs, and these outcomes could be a drug damage index, disease activity index, identifying factors that impact the severity of the disease which will provide expert with better insight into the disease, and predict the evolution of it.
اختصارTTotP
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بصمة

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