- Title
- Dynamically recommending repositories for health data : a machine learning model
- Creator
- Uddin, Md Ashraf; Stranieri, Andrew; Gondal, Iqbal; Balasubramanian, Venki
- Date
- 2020
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/171698
- Identifier
- vital:14368
- Identifier
-
https://doi.org/10.1145/3373017.3373041
- Identifier
- ISBN:9781450376976 (ISBN)
- Abstract
- Recently, a wide range of digital health record repositories has emerged. These include Electronic Health record managed by the government, Electronic Medical Record (EMR) managed by healthcare providers, Personal Health Record (PHR) managed directly by the patient and new Blockchain-based systems mainly managed by technologies. Health record repositories differ from one another on the level of security, privacy, and quality of services (QoS) they provide. Health data stored in these repositories also varies from patient to patient in sensitivity, and significance depending on medical, personal preference, and other factors. Decisions regarding which digital record repository is most appropriate for the storage of each data item at every point in time are complex and nuanced. The challenges are exacerbated with health data continuously streamed from wearable sensors. In this paper, we propose a recommendation model for health data storage that can accommodate patient preferences and make storage decisions rapidly, in real-time, even with streamed data. The model maps health data to be stored in the repositories. The mapping between health data features and characteristics of each repository is learned using a machine learning-based classifier mediated through clinical rules. Evaluation results demonstrate the model's feasibility. © 2020 ACM.; E1
- Publisher
- Association for Computing Machinery
- Relation
- 2020 Australasian Computer Science Week Multiconference, ACSW 2020
- Rights
- © 2020 Association for Computing Machinery
- Rights
- a24
- Rights
- This metadata is freely available under a CCO license
- Subject
- Big Health data; Classifier; Digital health record storage; Electronic Health Record; Quality of Services; Security and Privacy; Stream data
- Reviewed
- Hits: 2745
- Visitors: 2333
- Downloads: 0
| Thumbnail | File | Description | Size | Format |
|---|