Assessing clinical heterogeneity in sepsis through treatment patterns and machine learning.
Alison E. FohnerJohn D. GreeneBrian L. LawsonJonathan H. ChenPatricia KipnisGabriel J. EscobarVincent X. LiuPublished in: J. Am. Medical Informatics Assoc. (2019)
Keyphrases
- machine learning
- medical treatment
- clinical diagnosis
- machine learning methods
- pattern recognition
- real patient data
- therapy planning
- machine learning algorithms
- ischemic stroke
- feature selection
- artificial intelligence
- data mining
- clinically relevant
- disease progression
- treatment planning
- mental health
- lung disease
- medical doctors
- cancer patients
- initial stage
- acute myocardial infarction
- medical domain
- pattern discovery
- pattern mining
- frequent patterns
- knowledge acquisition
- text classification
- text mining
- information extraction
- learning algorithm
- clinical information
- cancer treatment
- supervised learning
- data analysis
- reinforcement learning
- radio frequency ablation