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.
Zain HussainSyed Ahmar ShahMome MukherjeeAziz SheikhPublished in: BMJ open (2020)
We have obtained approval from OPCRD's Anonymous Data Ethics Protocols and Transparency (ADEPT) Committee. We will seek ethics approval from The University of Edinburgh's Research Ethics Group (UREG). We aim to present our findings at scientific conferences and in peer-reviewed journals.
Keyphrases
- big data
- machine learning
- primary care
- public health
- artificial intelligence
- young adults
- deep learning
- chronic obstructive pulmonary disease
- global health
- randomized controlled trial
- physical activity
- lung function
- drug administration
- electronic health record
- cross sectional
- cystic fibrosis
- meta analyses
- air pollution