Machine learning methods to predict presence of intestine damage in patients with Crohn's disease.
Binu EnchakolodyBrianna HendersonStewart C. WangGrace L. SuAshish P. WasnikMahmoud M. Al-HawaryRyan W. StidhamPublished in: Medical Imaging: Computer-Aided Diagnosis (2020)
Keyphrases
- machine learning methods
- medical doctors
- capsule endoscopy
- machine learning
- multiple sclerosis
- early diagnosis
- patient groups
- machine learning algorithms
- machine learning approaches
- cardiovascular disease
- amyotrophic lateral sclerosis
- emergency department
- diagnostic tool
- ensemble methods
- statistical methods
- clinically relevant
- diabetic patients
- clinical studies
- disease progression
- chronic obstructive pulmonary disease
- computer aided
- clinical data
- caudate nucleus
- infectious disease
- liver disease
- medical data
- learning algorithm
- medical practitioners
- disease diagnosis
- chronic disease
- risk factors
- clinical trials
- learned knowledge
- medical diagnosis
- medical treatment
- colorectal cancer
- electronic medical record