4.7 Review

Machine Learning Techniques for Personalised Medicine Approaches in Immune-Mediated Chronic Inflammatory Diseases: Applications and Challenges

期刊

FRONTIERS IN PHARMACOLOGY
卷 12, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fphar.2021.720694

关键词

machine learning; autoimmune disease; personalised medicine; biomarker; omics

资金

  1. Versus Arthritis [21226, 21593, 20164]
  2. Lupus UK
  3. Dunhill Medical Trust [RPGF1902\117]
  4. NIHR UCLH Biomedical Research Centre grant [BRC772/III/EJ/101350]
  5. GOSCC
  6. NIHR-Biomedical Research Centres at GOSH
  7. NIHR-Biomedical Research Centres at UCLH

向作者/读者索取更多资源

Machine learning applications in the clinical study of chronic inflammatory diseases show promising results and potential for precision medicine, aiding in prediction, diagnosis, prognosis, disease management, and drug development.
In the past decade, the emergence of machine learning (ML) applications has led to significant advances towards implementation of personalised medicine approaches for improved health care, due to the exceptional performance of ML models when utilising complex big data. The immune-mediated chronic inflammatory diseases are a group of complex disorders associated with dysregulated immune responses resulting in inflammation affecting various organs and systems. The heterogeneous nature of these diseases poses great challenges for tailored disease management and addressing unmet patient needs. Applying novel ML techniques to the clinical study of chronic inflammatory diseases shows promising results and great potential for precision medicine applications in clinical research and practice. In this review, we highlight the clinical applications of various ML techniques for prediction, diagnosis and prognosis of autoimmune rheumatic diseases, inflammatory bowel disease, autoimmune chronic kidney disease, and multiple sclerosis, as well as ML applications for patient stratification and treatment selection. We highlight the use of ML in drug development, including target identification, validation and drug repurposing, as well as challenges related to data interpretation and validation, and ethical concerns related to the use of artificial intelligence in clinical research.

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