Opportunities for machine learning to transform care for people with cystic fibrosis
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Cites
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Self-citationDemystifying Black-box Models with Symbolic Metamodels
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Flagged first, then by citations
1 of the 13 references couldn’t be matched to a known paper.
Cited by
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Artificial intelligence and machine learning for clinical pharmacology
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Cystic fibrosis-related bone disease: an update on screening, diagnosis, and treatment
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- Authors
- Abroshan, M., Alaa, A. M., Rayner, O., & van der Schaar, M.
- Affiliations
- University of Cambridge (GB)
- University of California, Los Angeles (US)
- Venue
- Journal of Cystic Fibrosis
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 19
- Issue
- 1
- Pages
- 6-8
- ISSN
- 1569-1993 (ISSN-L)
- OpenAlex
- W3003191070
- Semantic Scholar
- 67816ddd35d7255cce95156b673350c4a61a2af7
- Cited by
- 8
- References
- 13
- Citations by year
- 2025: 2 · 2024: 3 · 2023: 3
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Cystic Fibrosis (publishedVersion)
- PubMed (publishedVersion)
- Cambridge University Engineering Department Publications Database (submittedVersion)
- Funders
- Alan Turing Institute (TU/ASG/R-SPEH-108)
- Cystic Fibrosis Trust
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Cystic Fibrosis Research Advances
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- machine learning, patient registry, outcome prediction, personalized medicine, CFTR modulators
- MeSH terms
- Clinical Decision-Making, Machine Learning, Cystic Fibrosis, Humans, Patient Care Management, Prognosis, Registries, Risk Assessment, Critical Pathways, Precision Medicine
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