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Related Concept Videos

Rheumatic Heart Disease III: Medical Management01:21

Rheumatic Heart Disease III: Medical Management

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Rheumatic heart disease (RHD) management can be divided into two main strategies: prevention and long-term management.Primary PreventionPrimary prevention focuses on timely diagnosis and management of group A streptococcal pharyngitis to prevent acute rheumatic fever. The most widely used antibiotic for treating this condition is intramuscular benzathine penicillin G.Acute Rheumatic Fever TreatmentThe primary treatment goal for a patient diagnosed with acute rheumatic fever is to suppress the...
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Rheumatic Heart Disease II: Clinical Manifestations and Diagnostic Studies01:22

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The key clinical manifestations of Rheumatic heart disease (RHD) include several distinct cardiac symptoms.Carditis, a hallmark of acute rheumatic fever, involves inflammation of the heart's endocardium, myocardium, and pericardium. Chronic RHD often results from recurrent episodes of carditis. Its symptoms include the following:Murmurs are caused by valvular damage, especially to the mitral and aortic valves. Mitral stenosis or regurgitation is common, with characteristic heart murmurs...
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Rheumatic Heart Disease I: Introduction01:23

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Rheumatic heart disease or RHD is a chronic condition that results from rheumatic fever, causing permanent damage to the heart valves.Etiology and Risk FactorsIt primarily arises from rheumatic fever, an inflammatory disease that can develop after untreated or inadequately treated group A streptococcal (GAS) pharyngitis. Streptococcus spreads through direct contact with oral or respiratory secretions. While the bacteria are the causative agents, factors like malnutrition, overcrowding, poor...
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AssessmentA comprehensive assessment is essential in managing a patient with rheumatic heart disease (RHD). Begin with obtaining a detailed medical history, including recent streptococcal infections, a history of rheumatic fever, or previously diagnosed rheumatic heart disease. Assess the patient for symptoms such as fever, chest pain, widespread joint pain (arthralgia), tachycardia, pericardial friction rub, muffled heart sounds, heart murmurs, peripheral edema, subcutaneous nodules, and...
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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Machine Learning in Rheumatic Diseases.

Mengdi Jiang1,2, Yueting Li1,2, Chendan Jiang3

  • 1Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College; The Ministry of Education Key Laboratory, Beijing, 100730, China.

Clinical Reviews in Allergy & Immunology
|July 19, 2020
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) offers powerful tools for analyzing complex rheumatic diseases. This technology can improve patient stratification, treatment strategies, and outcome predictions in rheumatology.

Keywords:
Clinical applicationMachine learningMedicineRheumatic diseases

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Area of Science:

  • Data science and artificial intelligence in medical research.
  • Application of machine learning in clinical practice.

Background:

  • Healthcare data science is rapidly advancing, increasing the need for sophisticated analytical tools.
  • Machine learning (ML) is a key technology for solving complex problems in medical science.

Purpose of the Study:

  • To review the fundamentals of machine learning (ML).
  • To explore the potential clinical applications of ML in rheumatology.
  • To summarize the strengths and limitations of ML in rheumatology.

Main Methods:

  • Review of machine learning principles.
  • Analysis of current and potential applications in rheumatology.
  • Discussion of ML's advantages and disadvantages in managing rheumatic diseases.

Main Results:

  • Machine learning can analyze massive datasets to reveal complex interrelationships in disease development and progression.
  • ML aids in understanding heterogeneous chronic conditions like rheumatic diseases.
  • Potential applications include optimizing patient stratification, therapeutic strategies, and prognosis prediction.

Conclusions:

  • Machine learning is a valuable tool for advancing rheumatology research and clinical practice.
  • ML can enhance the understanding and management of chronic and heterogeneous diseases.
  • Further exploration of ML's strengths and limitations is warranted for its effective implementation.