Clinical decision-support systems are technology-based tools that help healthcare providers enhance the quality of their services to satisfy their patients and earn their trust.These systems are used to improve physic...Clinical decision-support systems are technology-based tools that help healthcare providers enhance the quality of their services to satisfy their patients and earn their trust.These systems are used to improve physicians’diagnostic processes in terms of speed and accuracy.Using data-mining techniques,a clinical decision support system builds a classification model from hospital’s dataset for diagnosing new patients using their symptoms.In this work,we propose a privacy-preserving clinical decision-support system that uses a privacy-preserving random forest algorithm to diagnose new symptoms without disclosing patients’information and exposing them to cyber and network attacks.Solving the same problem with a different methodology,the simulation results show that the proposed algorithm outperforms previous work by removing unnecessary attributes and avoiding cryptography algorithms.Moreover,our model is validated against the privacy requirements of the hospitals’datasets and votes,and patients’diagnosed symptoms.展开更多
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 sys...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.展开更多
文摘Clinical decision-support systems are technology-based tools that help healthcare providers enhance the quality of their services to satisfy their patients and earn their trust.These systems are used to improve physicians’diagnostic processes in terms of speed and accuracy.Using data-mining techniques,a clinical decision support system builds a classification model from hospital’s dataset for diagnosing new patients using their symptoms.In this work,we propose a privacy-preserving clinical decision-support system that uses a privacy-preserving random forest algorithm to diagnose new symptoms without disclosing patients’information and exposing them to cyber and network attacks.Solving the same problem with a different methodology,the simulation results show that the proposed algorithm outperforms previous work by removing unnecessary attributes and avoiding cryptography algorithms.Moreover,our model is validated against the privacy requirements of the hospitals’datasets and votes,and patients’diagnosed symptoms.
文摘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.
