The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
IoT Healthcare Analytics: The Importance of Anomaly Detection
Abstract
Healthcare data is quite rich and often contains human survival related information. Analyzing healthcare data is of prime importance particularly considering the immense potential of saving human life and improving quality of life. Furthermore, IoT revolution has redefined modern health care systems and management. IoT offers its greatest promise to deliver excellent progress in healthcare domain. In this talk, proactive healthcare analytics specifically for cardiac disease prevention will be discussed. Anomaly detection plays a prominent role in healthcare analytics. In fact, the anomalous events are to be accurately detected with low false negative alarms often under high noise (low SNR) condition. An exemplary case of smartphone based cardiac anomaly detection will be presented.
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Cites
Hillestad, R., Bigelow, J., Bower, A., Girosi, F., Meili, R., Scoville, R., & Taylor, R. (2005) · Health Affairs · doi:10.1377/hlthaff.24.5.1103 · Cited by 1,675
Cited by
Attack and anomaly detection in IoT sensors in IoT sites using machine learning approaches
Hasan, M., Islam, M. M., Zarif, M. I. I., & Hashem, M. M. A. (2019) · Internet of Things · doi:10.1016/j.iot.2019.100059 · Cited by 841
IoT anomaly detection methods and applications: A survey
Chatterjee, A., & Ahmed, B. S. (2022) · Internet of Things · doi:10.1016/j.iot.2022.100568 · Cited by 323
A Survey on Explainable Anomaly Detection
Li, Z., Zhu, Y., & Van Leeuwen, M. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3609333 · Cited by 139
“For example, anomaly detection algorithms are being used to diagnose diseases in healthcare [213].”
Daskalos, A.-C., Theodoropoulos, P., Spandonidis, C., & Vordos, N. (2022) · Signals · doi:10.3390/signals3010002 · Cited by 26
Multimodal Deep Learning for Crop Yield Prediction
Jácome-Galarza, L.-R. (2022) · Communications in Computer and Information Science · doi:10.1007/978-3-031-18347-8_9 · Cited by 9
Metadata
- Authors
- Ukil, A., Bandyoapdhyay, S., Puri, C., & Pal, A.
- Venue
- 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA)
- Publisher
- IEEE
- Published
- 2016-03
- Pages
- 994-997
- OpenAlex
- W2403816530
- Cited by
- 222
- References
- 1
- FWCI
- 31.68
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Ukil, A., Bandyoapdhyay, S., Puri, C., & Pal, A. (2016). IoT Healthcare Analytics: The Importance of Anomaly Detection. 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA). https://doi.org/10.1109/aina.2016.158
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