A Computable Phenotype for Acute Respiratory Distress Syndrome Using Natural Language Processing and Machine Learning.
Majid AfsharCara JoyceAnthony OakeyPerry FormanekPhilip YangMatthew M. ChurpekRichard S. CooperRon PriceSusan J. ZeliskoDmitriy DligachPublished in: AMIA (2018)
Keyphrases
- natural language processing and machine learning
- natural language processing
- social networks
- active learning
- machine learning
- apnea hypopnea
- genome wide
- gene expression
- motion compensation
- early detection
- finite sets
- information extraction
- obstructive sleep apnea
- sleep stage
- biological systems
- biologically meaningful
- neural network
- image sequences
- sleep apnea
- intensive care units
- traditional chinese medicine
- motion vectors
- decision support system
- text mining
- learning algorithm
- knowledge base
- computational complexity