J Med Syst
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The study of electroencephalography (EEG) signals is not a new topic. However, the analysis of human emotions upon exposure to music considered as important direction. Although distributed in various academic databases, research on this concept is limited. ⋯ The fourth class includes 26 articles (26%) comprises studies that compare between or among two or more groups to identify and discover human emotion-based EEG. The final class includes six articles (6%) represents articles that study music as a stimulus and its impact on brain signals. Then, discussed the five main categories which are action types, age of the participants, and number size of the participants, duration of recording and listening to music and lastly countries or authors' nationality that published these previous studies. it afterward recognizes the main characteristics of this promising area of science in: motivation of using EEG process for measuring human brain signals, open challenges obstructing employment and recommendations to improve the utilization of EEG process.
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The sharing of patients' locations is an important part in mobile medical services and modern smart healthcare. Although location sharing based on blockchains has advantages on decentralization and openness, there is also a challenge to guarantee the security and the privacy of locations recorded in a blockchain. To this end, this paper investigates the location sharing based on blockchains for telecare medical information systems. ⋯ The analysis results show that our scheme satisfies the above requirements. Finally, the performance of our scheme is evaluated and the experiment results show that our scheme is efficient and feasible for both patients and medical workers. In a word, our scheme can be applied to realize privacy-preserving location sharing based on blockchains for telecare medical information systems.
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Electronic health record sharing can help to improve the accuracy of diagnosis, where security and privacy preservation are critical issues in the systems. In recent years, blockchain has been proposed to be a promising solution to achieve personal health information (PHI) sharing with security and privacy preservation due to its advantages of immutability. This work proposes a blockchain-based secure and privacy-preserving PHI sharing (BSPP) scheme for diagnosis improvements in e-Health systems. ⋯ Furthermore, the block generators are required to provide proof of conformance for adding new blocks to the blockchains, which guarantees the system availability. Security analysis demonstrates that the proposed protocol can meet with the security goals. Furthermor, we implement the proposed scheme on JUICE to evaluate the performance.
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The widely used American Society of Anesthesiologists Physical Status (ASA PS) classification is subjective, requires manual clinician review to score, and has limited granularity. Our objective was to develop a system that automatically generates an ASA PS with finer granularity by creating a continuous ASA PS score. Supervised machine learning methods were used to create a model that predicts a patient's ASA PS on a continuous scale using the patient's home medications and comorbidities. ⋯ A model consisting of three random forest classifiers (split model) achieved the best Cohen's Kappa. The model's agreement with our anesthesiologists on the ASA PS 3 case pairs yielded fair to moderate Kappa values. The results suggest that the random forest split classification model can predict ASA PS with agreement similar to that of anesthesiologists reported in literature and produce a continuous score in which agreement in accurately judging granularity is fair to moderate.
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The current state of clinical trials underscores a need for timely interventions to reduce the cost and length of the average trial. Newly developed health informatics technologies-including electronic health records, telemedicine systems, and mobile health applications-have recently been employed in a wide range of clinical trials in an effort to improve different aspects of the clinical trial process. The current review will focus on the observed benefits and drawbacks of using such technology to improve various patient-centered aspects of the clinical trial process, namely its potential to improve patient recruitment, patient retention, and data collection. Broad future challenges and opportunities in the field as a whole will also be covered.