Sub-phenotyping Metabolic Disorders Using Body Composition: An Individualized, Nonparametric Approach Utilizing Large Data Sets.
Jennifer LingeBrandon WhitcherMagnus BorgaOlof Dahlqvist LeinhardPublished in: Obesity (Silver Spring, Md.) (2019)
The adaptive k-nearest neighbors algorithm allowed an individual-centric assessment of each individual's metabolic phenotype moving beyond discrete categorizations of body composition. Within obesity and NAFLD, this may help in identifying which comorbidities a patient may develop and consequently enable optimization of treatment.
Keyphrases
- body composition
- resistance training
- bone mineral density
- metabolic syndrome
- insulin resistance
- machine learning
- type diabetes
- weight loss
- high throughput
- electronic health record
- deep learning
- big data
- adipose tissue
- skeletal muscle
- physical activity
- replacement therapy
- single cell
- neural network
- artificial intelligence
- clinical evaluation