Anomaly Detection in COVID-19 Time-Series Data

Anomaly detection and explanation in big volumes of real-world medical data, such as those pertaining to COVID-19, pose some challenges. First, we are dealing with time-series data. Typical time-series data describe behavior of a single object over time. In medical data, we are dealing with time-ser...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	SN computer science Jg. 2; H. 4; S. 279
Hauptverfasser:	Homayouni, Hajar, Ray, Indrakshi, Ghosh, Sudipto, Gondalia, Shlok, Kahn, Michael G.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Singapore Springer Singapore 01.07.2021 Springer Nature B.V
Schlagworte:	Anomalies Artificial Intelligence for HealthCare Computer Imaging Computer Science Computer Systems Organization and Communication Networks COVID-19 Data analysis Data Structures and Information Theory Datasets Epidemics Health care facilities Information Systems and Communication Service Machine learning Original Research Outliers (statistics) Pattern Recognition and Graphics Quality control Scientific visualization Software Engineering/Programming and Operating Systems Subject specialists Time series Vision Visualization COVID-19 data LSTM-autoencoder Explainability Anomaly detection Time series Data quality tests
ISSN:	2662-995X, 2661-8907, 2661-8907
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Schreiben Sie den ersten Kommentar!