Evaluation of standard and semantically-augmented distance metrics for neurology patients
Abstract
Patient distances can be calculated based on signs and symptoms derived from an ontological hierarchy. There is controversy as to whether patient distance metrics that consider the semantic similarity between concepts can outperform standard patient distance metrics that are agnostic to concept similarity. The choice of distance metric can dominate the performance of classification or clustering algorithms. Our objective was to determine if semantically augmented distance metrics would outperform standard metrics on machine learning tasks.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Aug 26, 2020
- Source ID
- 10.1186/s12911-020-01217-8
Entities
People
- Blaine Allen
- Daniel B Hier
- Donald Wunsch
- Gayla R Olbricht
- Jonathan Kopel
- Sima Azizi
- Steven U. Brint
Organizations
- United States Army Research Laboratory