Privacy-Preserving Healthcare System for Clinical Decision-Support and Emergency Call Systems

Abstract

Healthcare centers always aim to deliver the best quality healthcare services to patients and earn their satisfaction. Technology has played a major role in achieving these goals, such as clinical decision-support systems and mobile health social networks. These systems have improved the quality of care services by speeding-up the diagnosis process with accuracy, and allowing caregivers to monitor patients remotely through the use of WBS, respectively. However, these systems' accuracy and efficiency are dependent on patients' health information, which must be inevitably shared over the network, thus exposing them to cyber-attacks. Therefore, privacy-preserving services are ought to be employed to protect patients' privacy. In this work, we proposed a privacy-preserving healthcare system, which is composed of two subsystems. The first is a privacy-preserving clinical decision-support system. The second subsystem is a privacy-preserving Mobile Health Social Network (MHSN). The former was based on decision tree classifier that is used to diagnose patients with new symptoms without disclosing patients' records. Whereas the latter would allow physicians to monitor patients' current condition remotely through WBS; thus sending help immediately in case of a distress situation detected. The social network, which connects patients of similar symptoms together, would also provide the service of seeking help of near-by passing people while the patient is waiting for an ambulance to arrive. Our model is expected to improve healthcare services while protecting patients' privacy.

PPSDT: A Novel Privacy-Preserving Single Decision Tree Algorithm for Clinical Decision-Support Systems Using IoT Devices

“…Moreover, multiple hospitals may collaborate together to produce a more accurate model by combining their datasets. However, the privacy of shared data is a serious issue [7,8]; therefore, this will violate patients’ privacy [9,10,11,12]. Privacy preserving data mining is to extract hidden patterns from a dataset without exactly accessing it [13].…”

“…We have proposed in Ref. [12] a privacy-preserving model for a healthcare system. Part of the proposed model addresses the privacy and security challenges faced by clinical decision-support systems.…”

A Privacy-Preserving Algorithm for Clinical Decision-Support Systems Using Random Forest

“…For further enhancement, hospitals may collaborate to generate a more accurate classification model by combining their datasets. However, such collaboration will raise privacy concerns that are related to patients' records [Alabdulkarim, Al-Rodhaan and Tian (2017); Liang, Lu, Chen et al (2011); Liu, Lu, Ma et al (2016); Lu, Lin and Shen (2013)]. The privacy of shared data is a serious issue [Ma, Zhang, Cao et al (2015); Rong, Ma, Tang et al (2018); Xiong and Shi (2018)]; thus, privacy preserving algorithms for securing data-mining techniques enable the extraction of hidden patterns from a dataset without actually accessing it [Zhang, Tong, Tang et al (2005)].…”

“…Motivated by the need for a privacy-preserving CDSS, that would mainly aim to build an accurate classification model to aid physicians while maintaining patients' health records private, in this paper, we propose a privacy-preserving clinical decision-support system (P-PCDSS) that uses random forests to enable multiple parties (hospitals), through a cloud, to collaborate in creating a classification model (random forest) for the CDSS without revealing patients' records. We have proposed in Alabdulkarim et al [Alabdulkarim, Al-Rodhaan and Tian (2017)] a privacy-preserving model for a healthcare systems. Part of the proposed model addresses the privacy and security challenges faced by clinical decision-support systems.…”