Slowing down the progression of chronic kidney disease (CKD) and its adverse health outcomes requires the patient's self-management and attention to treatment recommendations. Information technology (IT)-based interventions are increasingly being used to support self-management in patients with chronic diseases such as CKD. We conducted a systematic review of randomized controlled trials (RCTs) to assess the features and effects of IT-based interventions on self-management outcomes of CKD patients. A comprehensive search was conducted in Medline, Scopus, and the Cochrane Library to identify relevant papers that were published until May 2016. RCT Studies that assessed at least one automated IT tool in patients with CKD stages 1 to 5, and reported at least one self-management outcome were included. Studies were appraised for quality using the Cochrane Risk of Bias assessment tool. Out of 12,215 papers retrieved, eight study met the inclusion criteria. Interventions were delivered via smartphones/personal digital assistants (PDAs) (three studies), wearable devices (three studies), computerized systems (one study), and multiple component (one study). The studies assessed 15 outcomes, including eight clinical outcomes and seven process of care outcomes. In 12 (80%) of the 15 outcomes, the studies had revealed the effects of the interventions as statistically significant positive. These positive effects were observed in 75% of the clinical outcomes and 86% of the process of care outcomes. The evidence indicates the potential of IT-based interventions (i.e. smartphones/PDAs, wearable devices, and computerized systems) in self-management outcomes (clinical and process of care outcomes) of CKD patients.
BACKGROUND: Hospital Statistics and Information System is one of the most important health information systems in Iran used in all hospitals in this country. Usability problems can reduce the speed and precision of users when interacting with this system. This study aimed to identify the usability problems of a national health system called “AVAB”. MATERIALS AND METHODS: This descriptive cross-sectional study was conducted in 2020, and three experts evaluated the usability of this system independently by the heuristic evaluation method. Nielsen's usability principles were used to identify usability problems and to classify their severity. RESULTS: A total of 86 unique problems were identified. The highest number of problems were related to the two principles of “help and documentation” and “match between system and the real world” with 23 and 11 usability problems, respectively. The lowest number of problems were related to the two principles of “visibility of system status” and “help users recognize, diagnose, and recover from errors,” each with three problems. 58.1% of the identified problems were in the group of major and catastrophic problems. CONCLUSIONS: With the help of heuristic evaluation method, a significant number of usability problems of Hospital Statistics and Information System were identified. Most of the identified problems were major and catastrophic, and it is necessary to solve these problems by the designers and developers of this system.
Introduction: Prostate cancer is one of the leading causes of death in men, and the early detection of this disease can be a significant factor in controlling and managing it. Applying data mining techniques can lead to the extraction of hidden knowledge from a huge amount of data and can help diagnose this disease by physicians. This study aims to determine the algorithm with the best performance to diagnose prostate cancer.Methods: In this study, nine data mining techniques, including Support Vector Machine, Decision Tree, Naive Bayes, K-Nearest Neighbors, Neural Network, Random Forest, Deep Learning, Auto-MLP, and Rule Induction algorithms, were used to extract hidden patterns from prostate cancer data. In this study, the data of 100 patients, which included eight characteristics, were used, and the RapidMiner Studio environment was employed for modeling. To compare the performance of the mentioned approaches used in this study to diagnose prostate cancer, accuracy, recall, precision, AUC, sensitivity, and specificity were calculated and reported for all techniques.Results: The results of this study showed that the accuracy of the applied algorithms was between 77% and 84%. Using different criteria to evaluate the techniques used showed that the two algorithms K-Nearest Neighbors and Neural Network, had better performance and accuracy (84%) than other methods. The sensitivity in these two algorithms was 80% for Neural Networks and 85% for K-Nearest Neighbors, respectively.Conclusion: The usage of different data mining techniques can lead to the discovery of hidden patterns among an enormous amount of data related to prostate cancer, and as a result, it leads to the early diagnosis of this disease and saves the subsequent costs.
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