Identification of temporal condition patterns associated with pediatric obesity incidence using sequence mining and big data.
Elizabeth A CampbellTing QianJeffrey M MillerEllen J BassAaron J MasinoPublished in: International journal of obesity (2005) (2020)
The novel application of SPADE on a large retrospective dataset revealed temporally dependent condition associations with obesity incidence. Allergic rhinitis and asthma had a particularly high prevalence during pre-index visits. These conditions, along with those exclusively observed during pre-index visits, may represent signals of future obesity. While causation cannot be inferred from these associations, the temporal condition patterns identified here represent hypotheses that can be investigated to determine causal relationships in future obesity research.
Keyphrases
- insulin resistance
- metabolic syndrome
- weight loss
- big data
- allergic rhinitis
- high fat diet induced
- type diabetes
- weight gain
- risk factors
- machine learning
- artificial intelligence
- chronic obstructive pulmonary disease
- current status
- adipose tissue
- skeletal muscle
- body mass index
- high resolution
- young adults
- lung function
- mass spectrometry
- cystic fibrosis
- amino acid
- childhood cancer