Trustworthy Precision Medicine: An Interpretable Approach to Detecting Anomalous Behavior of IoT Devices...Proceedings of the 20th International Conference on Wearable Micro and Nano Technologies for Personalized Health - pHealth 2024, May 27-29, 2024, Rende, Italy.
The growing integration of Internet of Things (IoT) technology within the healthcare sector has revolutionized healthcare delivery, enabling advanced personalized care and precise treatments. However, this raises significant challenges, demanding robust, intelligible, and effective monitoring mechan...
| Publicado en: | Studies in Health Technology & Informatics Vol. 314; pp. 108 - 113 |
|---|---|
| Autores principales: | , , , |
| Formato: | abstract algorithm proceedings tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177548706&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177548706 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2024 vid: 314 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 177548706 177548706 177548706 10.3233/SHTI240068 177548706 ppf: 108 ppct: 5 formats: tig: atl: Trustworthy Precision Medicine: An Interpretable Approach to Detecting Anomalous Behavior of IoT Devices...Proceedings of the 20th International Conference on Wearable Micro and Nano Technologies for Personalized Health - pHealth 2024, May 27-29, 2024, Rende, Italy. aug: au: COSTA, Gianni FORESTIERO, Agostino MACRÌ, Davide ORTALE, Riccardo affil: Institute for High-Performance Computing and Networking, National Research Council, Via P. Bucci 8-9C, Rende (CS), Italy. sug: subj: Trust Internet of Things Equipment and Supplies Machine Learning Data Security Congresses and Conferences Italy Italy Health Care Industry Artificial Intelligence Ecosystem Privacy and Confidentiality ab: The growing integration of Internet of Things (IoT) technology within the healthcare sector has revolutionized healthcare delivery, enabling advanced personalized care and precise treatments. However, this raises significant challenges, demanding robust, intelligible, and effective monitoring mechanisms. We propose an interpretable machine- learning approach to the trustworthy and effective detection of behavioral anomalies within the realm of medical IoT. The discovered anomalies serve as indicators of potential system failures and security threats. Essentially, the detection of anomalies is accomplished by learning a classifier from the operational data generated by smart devices. The learning problem is dealt with in predictive association modeling, whose expressiveness and intelligibility enforce trustworthiness to offer a comprehensive, fully interpretable, and effective monitoring solution for the medical IoT ecosystem. Preliminary results show the effectiveness of our approach. pubtype: Academic Journal doctype: abstract algorithm proceedings tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|