Machine Learning in Rheumatic Diseases
| Autor: | Chendan Jiang, Lidan Zhao, Mengdi Jiang, Yueting Li, Peter E. Lipsky, Xuan Zhang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Evaluation system
business.industry Computer science Clinical Decision-Making Information technology General Medicine Extremely Helpful Disease Machine learning computer.software_genre Machine Learning Patient Satisfaction Rheumatic Diseases Practice Guidelines as Topic Health care Animals Humans Immunology and Allergy Artificial intelligence Information Technology business Medical science Complex problems computer |
| Zdroj: | Clinical Reviews in Allergy & Immunology. 60:96-110 |
| ISSN: | 1559-0267 1080-0549 |
| Popis: | With advances in information technology, the demand for using data science to enhance healthcare and disease management is rapidly increasing. Among these technologies, machine learning (ML) has become ubiquitous and indispensable for solving complex problems in many scientific fields, including medical science. ML allows the development of guidelines and framing of the evaluation system for complex diseases based on massive data. In the analysis of rheumatic diseases, which are chronic and remarkably heterogeneous, ML can be anticipated to be extremely helpful in deciphering and revealing the inherent interrelationships in disease development and progression, which can further enhance the overall understanding of the disease, optimize patients' stratification, calibrate therapeutic strategies, and predict prognosis and outcomes. In this review, the basics of ML, its potential clinical applications in rheumatology, together with its strengths and limitations are summarized. |
| Databáze: | OpenAIRE |
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