T-DPSOM: an interpretable clustering method for unsupervised learning of patient health states.
Laura ManduchiMatthias HüserMartin FaltysJulia E. VogtGunnar RätschVincent FortuinPublished in: CHIL (2021)
Keyphrases
- clustering method
- unsupervised learning
- electronic health records
- primary care
- medical care
- cluster analysis
- health care
- hierarchical clustering
- healthcare delivery
- supervised learning
- patient care
- spectral clustering
- health related
- clinical data
- data clustering
- document clustering
- health records
- relational clustering
- similarity measure
- clustering algorithm
- patient data
- semi supervised
- subspace clustering
- affinity propagation
- clustering analysis
- spatial clustering
- dimensionality reduction
- chronic disease
- cardiovascular disease
- fuzzy c means
- k means
- unsupervised clustering
- medical records
- model selection
- medical data
- clustering result
- hierarchical clustering algorithm
- clinical trials
- dissimilarity measure
- fuzzy clustering method
- object recognition
- feature selection
- health status
- expectation maximization
- clustering framework
- health information
- constrained clustering
- clustering approaches
- data sets