This paper is in the following e-collection/theme issue:

Falls and Emergency Detection with Sensors and other Technologies (28) Sensors at Home and Domotics (51) Dementia and Cognitive Decline (425) Ubiquitous Health (uHealth) (116) Telehealth and Telemonitoring (1563)

Published on in Vol 5, No 3 (2022): Jul-Sep

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38211, first published .
An Unsupervised Data-Driven Anomaly Detection Approach for Adverse Health Conditions in People Living With Dementia: Cohort Study

An Unsupervised Data-Driven Anomaly Detection Approach for Adverse Health Conditions in People Living With Dementia: Cohort Study

An Unsupervised Data-Driven Anomaly Detection Approach for Adverse Health Conditions in People Living With Dementia: Cohort Study

Authors of this article:

Nivedita Bijlani1 Author Orcid Image ;   Ramin Nilforooshan2, 3, 4 Author Orcid Image ;   Samaneh Kouchaki1, 3 Author Orcid Image
Article Authors Cited by (9) Tweetations (2) Metrics

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  4. Srimokla O, Pan-Ngum W, Khamsiriwatchara A, Padungtod C, Tipmontree R, Choosri N, Saralamba S. Early warning systems for malaria outbreaks in Thailand: an anomaly detection approach. Malaria Journal 2024;23(1) View
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  7. Nafea A, AL-Mahdawi M, AL-Ani M, Omar N. A Review on Adverse Drug Reaction Detection Techniques. ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY 2024;12(1):143 View
  8. Piaseczna N, Doniec R, Sieciński S, Barańska K, Jędrychowski M, Grzegorzek M. Driving Reality vs. Simulator: Data Distinctions. Electronics 2024;13(14):2708 View