Abstract
The proliferation of wearable sensors that record physiological signals has resulted in an exponential growth of data on digital health. To select the appropriate repository for the increasing amount of collected data, intelligent procedures are becoming increasingly necessary. However, allocating storage space is a nuanced process. Generally, patients have some input in choosing which repository to use, although they are not always responsible for this decision. Patients are likely to have idiosyncratic storage preferences based on their unique circumstances. The purpose of the current study is to develop a new predictive model of health data storage to meet the needs of patients while ensuring rapid storage decisions, even when data is streaming from wearable devices. To create the machine learning classifier, we used a training set synthesized from small samples of experts who exhibited correlations between health data and storage features. The results confirm the validity of the machine learning methodology.
Highlights
In the modern era, clinicians no longer manage health data exclusively, but are increasingly responsible for obtaining consent from patients [1]
Health data will increasingly be preserved in a variety of repositories, so patients can select the repository that best meets their needs
Patients are realistically expected to avoid using a single repository for all their health data because the context of treatment, patterns of data, and legal constraints may change
Summary
Clinicians no longer manage health data exclusively, but are increasingly responsible for obtaining consent from patients [1]. Patients can lose medical information when their electronic health records are malfunctioning [18]. Due to the manual uploading of data generated by wearable sensors to personal health records, caregiver responses were delayed. For this reason, [19] developed methods for storing patientgenerated health information on commercial blood glucose monitors. Checklists mostly cover client meetings on site, site visits, and maintaining live workflows Health data sources such as hospitals, clinics, insurers, and patients should be integrated into centralized databases, according to the author [25]. Utilization of a compression algorithm to retrieve the health repository data as fast as possible using blockchain and interplanetary file systems (IPFS) without data loss [28]. No machine learning mechanisms were developed to cater to user preferences
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