Prospectively predicting Pseudomonas aeruginosa infection/s using routine data from the UK cystic fibrosis register.
Nikki TottonMike BradburnZhe Hui HooJen LewisDaniel HindCarla GirlingElizabeth ShepherdJulia NightingaleThomas DanielsJane DewarSophie DawsonMary CarrollMark AllenbyFrank EdenboroughRachael CurleyCharlotte CarolanMartin WildmanPublished in: Health science reports (2021)
Historic registry data can be used to contemporaneously identify a subgroup of patients with chronic PA. Since this patient group has a narrower treatment schedule, this can facilitate a better benchmarking of adherence across centers.
Keyphrases
- cystic fibrosis
- pseudomonas aeruginosa
- electronic health record
- big data
- biofilm formation
- lung function
- case report
- randomized controlled trial
- acinetobacter baumannii
- type diabetes
- clinical trial
- escherichia coli
- machine learning
- air pollution
- chronic obstructive pulmonary disease
- skeletal muscle
- candida albicans
- replacement therapy
- deep learning
- study protocol
- glycemic control