A hierarchical machine learning model based on Glioblastoma patients' clinical, biomedical, and image data to analyze their treatment plans.
Mohammad Mahdi ErshadiZeinab Rahimi RiseSeyed Taghi Akhavan NiakiPublished in: Comput. Biol. Medicine (2022)
Keyphrases
- medical treatment
- image data
- ischemic stroke
- machine learning
- clinical information
- therapy planning
- clinical studies
- cancer patients
- clinical diagnosis
- medical doctors
- disease progression
- clinically relevant
- clinical data
- acute myocardial infarction
- real patient data
- medical practitioners
- clinical trials
- information extraction
- patient data
- cancer treatment
- electronic medical record
- biomedical images
- text mining
- breast cancer patients
- medical domain
- medical staff
- clinical decision making
- medical care
- medical data
- patient records
- clinical practice
- chronic disease
- drug resistance
- chronic hepatitis
- medical records
- clinical setting
- acute myeloid
- acute coronary syndrome
- multiple sclerosis
- mr images
- treatment plan
- lung cancer patients
- treatment planning
- magnetic resonance
- data mining
- patient care
- cardiovascular disease
- liver disease
- breast cancer
- traumatic brain injury
- neurological disorders
- medical experts
- medical images
- raw data
- magnetic resonance imaging
- prostate cancer
- patient groups
- survival data
- clinical guidelines
- primary care
- intensive care