Predicting healthcare outcomes in prematurely born infants using cluster analysis.
Victoria MacBeanAlan LuntSimon B DrysdaleMuska N YarziGerrard F RaffertyAnne GreenoughPublished in: Pediatric pulmonology (2018)
Infants could be classified according to birth weight and duration of neonatal invasive mechanical ventilation (MV) into three clusters. Cluster one (MV ≤5 days) had few LRTIs. Clusters two and three (both MV ≥6 days, but BW ≥or <882 g respectively), had significantly higher LRTI rates. Cluster two had a higher proportion of infants experiencing respiratory syncytial virus LRTIs (P = 0.01) and cluster three a higher proportion of rhinovirus LRTIs (P < 0.001) CONCLUSIONS: Readily available clinical data allowed classification of prematurely born infants into one of three distinct groups with differing subsequent respiratory morbidity in infancy.
Keyphrases
- gestational age
- mechanical ventilation
- birth weight
- healthcare
- respiratory syncytial virus
- weight gain
- acute respiratory distress syndrome
- machine learning
- deep learning
- preterm birth
- big data
- electronic health record
- body mass index
- preterm infants
- social media
- extracorporeal membrane oxygenation
- data analysis
- physical activity
- weight loss