Machine Learning in Rheumatic Diseases
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:60 |
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| Enthalten in: |
Clinical reviews in allergy & immunology - 60(2021), 1 vom: 17. Feb., Seite 96-110 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Jiang, Mengdi [VerfasserIn] |
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| Links: |
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| Themen: |
Clinical application |
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| Anmerkungen: |
Date Completed 24.09.2021 Date Revised 24.09.2021 published: Print Citation Status MEDLINE |
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| doi: |
10.1007/s12016-020-08805-6 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM312577346 |
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| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a Rheumatic diseases | |
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| 700 | 1 | |a Jiang, Chendan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Lidan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Xuan |e verfasserin |4 aut | |
| 700 | 1 | |a Lipsky, Peter E |e verfasserin |4 aut | |
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