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There are over a hundred recognized autoimmune diseases, and an increasingly high prevalence of autoimmune diseases among the global population poses a significant challenge to human health and imposes a substantial economic burden on the healthcare system. In autoimmune diseases, the immune system attacks the body’s own cells. Autoimmune diseases are generally characterized by a range of symptoms, including common systemic symptoms, musculoskeletal symptoms, digestive symptoms, neurological symptoms, visual symptoms, persistent, widespread tissue inflammation, shortened lifespan, and significant damage to end-organ systems. When an autoimmune disease remains undiagnosed and is left untreated, the patient’s condition may worsen over time, and it may lead to complex, heterogeneous clinical manifestations and disorders. Over the past few decades, advancements in medical science for the diagnosis and treatment of autoimmune diseases, along with related digital data collection initiatives, have led to the collection of large, heterogeneous datasets for several autoimmune diseases. Over recent years, researchers and experts have explored the applications of artificial intelligence (AI) and machine learning (ML) techniques in descriptive, diagnostic, predictive, and prescriptive analytics for the management of autoimmune diseases. Machine learning techniques can automatically detect underlying patterns in complex datasets without human intervention. Identifying underlying patterns in complex biological and clinical data related to autoimmune diseases may enable early diagnosis, thereby mitigating disease progression and improving patient health. Machine learning models can be trained on multimodal data, including patient demographics, genetics, symptom information, laboratory test results, image-based tests, and radiological tests, to diagnose autoimmune diseases. Ensemble approaches in ML can also integrate diverse clinical, genetic, and demographic datasets; as a result, these models can enhance the accuracy of autoimmune disease diagnosis, enable personalized treatment, and improve disease prognosis. In this chapter, we examine the applications of machine learning techniques in diagnosing various autoimmune diseases, focusing on Rheumatoid Arthritis, Multiple Sclerosis, Type 1 Diabetes, Psoriasis, and Systemic Lupus Erythematosus.

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Role and Application of Machine Learning Techniques in Early Diagnosis and Management of Autoimmune Diseases

  • 章节

  • 发表日期：

    2026年

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  • Akshay Chadha,
  • Naman Garg

摘要

There are over a hundred recognized autoimmune diseases, and an increasingly high prevalence of autoimmune diseases among the global population poses a significant challenge to human health and imposes a substantial economic burden on the healthcare system. In autoimmune diseases, the immune system attacks the body’s own cells. Autoimmune diseases are generally characterized by a range of symptoms, including common systemic symptoms, musculoskeletal symptoms, digestive symptoms, neurological symptoms, visual symptoms, persistent, widespread tissue inflammation, shortened lifespan, and significant damage to end-organ systems. When an autoimmune disease remains undiagnosed and is left untreated, the patient’s condition may worsen over time, and it may lead to complex, heterogeneous clinical manifestations and disorders. Over the past few decades, advancements in medical science for the diagnosis and treatment of autoimmune diseases, along with related digital data collection initiatives, have led to the collection of large, heterogeneous datasets for several autoimmune diseases. Over recent years, researchers and experts have explored the applications of artificial intelligence (AI) and machine learning (ML) techniques in descriptive, diagnostic, predictive, and prescriptive analytics for the management of autoimmune diseases. Machine learning techniques can automatically detect underlying patterns in complex datasets without human intervention. Identifying underlying patterns in complex biological and clinical data related to autoimmune diseases may enable early diagnosis, thereby mitigating disease progression and improving patient health. Machine learning models can be trained on multimodal data, including patient demographics, genetics, symptom information, laboratory test results, image-based tests, and radiological tests, to diagnose autoimmune diseases. Ensemble approaches in ML can also integrate diverse clinical, genetic, and demographic datasets; as a result, these models can enhance the accuracy of autoimmune disease diagnosis, enable personalized treatment, and improve disease prognosis. In this chapter, we examine the applications of machine learning techniques in diagnosing various autoimmune diseases, focusing on Rheumatoid Arthritis, Multiple Sclerosis, Type 1 Diabetes, Psoriasis, and Systemic Lupus Erythematosus.