Discrete-time survival analysis in the critically ill: a deep learning approach using heterogeneous data.
Hans-Christian Thorsen-MeyerDavide PlacidoBenjamin Skov Kaas-HansenAnna P. NielsenTheis LangeAnnelaura B. NielsenPalle ToftJens SchierbeckThomas StrømPiotr ChmuraMarc HeimannKirstine BellingAnders PernerSøren BrunakPublished in: npj Digit. Medicine (2022)
Keyphrases
- heterogeneous data
- deep learning
- survival analysis
- machine learning
- regression model
- breast cancer
- data integration
- predictive modeling
- data management
- unsupervised learning
- complex data
- data mining methods
- data sources
- databases
- weakly supervised
- evolutionary computing
- logistic regression
- data mining
- metadata
- data mining techniques
- model selection
- risk assessment
- decision trees
- text mining
- web data
- variable selection
- mental models
- higher order
- pattern recognition
- natural language
- computer vision
- information extraction
- data sets
- low dimensional