Skip to main navigation
Skip to search
Skip to main content
Maastricht University Home
Support & FAQ
Home
Researchers
Publications
Activities
Press/Media
Prizes
Organisations
Datasets
Projects
Search by expertise, name or affiliation
Machine learning in medicine: big pictures require small, but crucial strokes
Franciscus Cornelis Bennis
Biomedische Technologie
Carim - Heart
MHeNs - Neuroscience
Research output
:
Thesis
›
Doctoral Thesis
›
Internal
368
Downloads (Pure)
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Machine learning in medicine: big pictures require small, but crucial strokes'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Clinical Practice
100%
Model Complexity
100%
Clinical Problems
100%
Clinical Value
100%
Time Parameter
100%
Ductus Arteriosus
100%
Machine Learning Models
100%
Pulse Transit Time
100%
Machine Learning in Medicine
100%
INIS
medicine
100%
machine learning
100%
data
50%
validation
50%
values
50%
fatigue
50%
newborns
50%
pulses
50%
extraction
50%
Medicine and Dentistry
Medicine
100%
Apoplexy
100%
Physician
50%
Pulse Rate
50%
Neonatal Infant
50%
Ductus Arteriosus
50%
Engineering
Learning System
100%
Raw Data
50%
Complex Model
50%
Neuroscience
Ductus Arteriosus
100%
Cerebrovascular Accident
100%
Biochemistry, Genetics and Molecular Biology
Pulse Rate
100%
Pharmacology, Toxicology and Pharmaceutical Science
Cerebrovascular Accident
100%