Predicting the risk of asthma attacks in children, adolescents and adults: protocol for a machine learning algorithm derived from a primary care-based retrospective cohort

Journal article

Most asthma attacks and subsequent deaths are potentially preventable. We aim to develop a prognostic tool for identifying patients at high risk of asthma attacks in primary care by leveraging advances in machine learning

Authors: Hussain, Z; Shah, SA; Mukherjee, M; Sheikh, A

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BMJ Publishing Group
• Journal: BMJ Open
• Volume: 10
• Issue: 7
• Pages: e036099-e036099
• Publication date: 2020-07-23
• DOI: 10.1136/bmjopen-2019-036099
• EISSN: 2044-6055
• ISSN: 2044-6055
• Language: English
• Keywords: Protocol (science); Retrospective cohort study; Logistic regression; Family medicine; Artificial intelligence; Computer science; Internal medicine; Asthma; Alternative medicine; Machine learning; Medicine