A Taxonomic Framework Based on Machine Learning for Unsupervised Anomaly Detection in Healthcare Data
- DOI
- 10.3390/healthcare14203380
- Published
- 2026-10-10
- Container
- Healthcare
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/healthcare14203380,
title = {A Taxonomic Framework Based on Machine Learning for Unsupervised Anomaly Detection in Healthcare Data},
author = {Gelson Schneider and Moisés Lima Júnior and Davenilcio de Souza and Domingos Valladares and Mikael Batista and André Korzenowski},
year = {2026},
journal = {Healthcare},
doi = {10.3390/healthcare14203380},
url = {https://doi.org/10.3390/healthcare14203380}
}RIS
TY - JOUR TI - A Taxonomic Framework Based on Machine Learning for Unsupervised Anomaly Detection in Healthcare Data AU - Gelson Schneider AU - Moisés Lima Júnior AU - Davenilcio de Souza AU - Domingos Valladares AU - Mikael Batista AU - André Korzenowski PY - 2026 JO - Healthcare DO - 10.3390/healthcare14203380 UR - https://doi.org/10.3390/healthcare14203380 ER -
APA
Schneider, G., Júnior, M. L., Souza, D. D., Valladares, D., Batista, M., & Korzenowski, A. (2026). A Taxonomic Framework Based on Machine Learning for Unsupervised Anomaly Detection in Healthcare Data. Healthcare. https://doi.org/10.3390/healthcare14203380
Source records
- crossref · retrieved 2026-11-05T12:27:17.048Z