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Objectives: Symptomatic treatment is an essential component in the overall treatment of multiple sclerosis (MS). However, knowledge in this regard is confusing and scattered. Physicians also have challenges in choosing symptomatic treatment based on the patient’s condition. To share, update, and reuse this knowledge, the aim of this study was to provide an ontology for MS symptomatic treatment.Methods: The Symptomatic Treatment of Multiple Sclerosis Ontology (STMSO) was developed according to Ontology Development 101 and a guideline for developing good ontologies in the biomedical domain. We obtained knowledge and rules through a systematic review and entered this knowledge in the form of classes and subclasses in the ontology. We then mapped the ontology using the Basic Formal Ontology (BFO) and Ontology for General Medical Sciences (OGMS) as reference ontologies. The ontology was built using Protégé Editor in the Web Ontology Language format. Finally, an evaluation was done by experts using criterion-based approaches in terms of accuracy, clarity, consistency, and completeness.Results: The knowledge extraction phase identified 110 articles related to the ontology in the form of 626 classes, 40 object properties, and 139 rules. Five general classes included “patient,” “symptoms,” “pharmacological treatment,” “treatment plan,” and “measurement index.” The evaluation in terms of standards for biomedical ontology showed that STMSO was accurate, clear, consistent, and complete.Conclusions: STMSO is the first comprehensive semantic representation of the symptomatic treatment of MS and provides a major step toward the development of intelligent clinical decision support systems for symptomatic MS treatment.

Web-based interventions have the potential to educate persons with mental disorders. But the effects of these interventions on the patient’s symptoms and psychological outcomes and also design features of these interventions are not often explicitly described. The purpose of this study is to systematically present the overall effects and design features of psycho-educational web-based mental health interventions. A systematic search in PubMed, Scopus, EMBASE, and Cochrane Library from January 1990 to January 2020 was conducted. To capture relevant studies, two reviewers independently reviewed and assessed titles and abstracts of the papers against predefined inclusion and exclusion criteria with three groups of keywords addressing mental health, psycho-education, and online intervention. Eligible studies were systematically reviewed on the basis of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The risk of bias was assessed by using the Jadad tool for methodological quality. For analyzing the results, a combination of frequency and descriptive synthesis was used. From 2227 studies initially identified, 20 studies met all inclusion criteria. Depression disorders with 40% had the highest web-based intervention. In terms of outcome, 70% of included studies had positive effects on symptoms. Also 60% of included studies improved psychological outcomes. In terms of design features, most of included studies had program exposure (100%), social context and support (60%), information architecture (55%), and self-management (50%) features. The infrequent features were contacts with intervention (40%), usability (25%), updated information (20%), and tailoring and targeting (15%). The results of this study showed that program exposure, social context and support, information architecture, and self-management features could influence the effectiveness of a web-based mental health intervention. This review will be helpful for researchers and clinicians as a guide to develop more effective psycho-educational web-based interventions for people with mental disorders.

Background Picture Archiving and Communication System (PACS) is an evolving technology in health care domains that is used for storage, management, retrieval, transfer, and delivery of medical images. Some medical centers in Iran have installed the PACS in recent years but have not used it appropriately. One of the problems in implementing this system is inability to select appropriate PACS. Several factors are involved in the selection process. The objective of this study was to determine the factors that influence PACS selection. Methods This qualitative study aimed to identify factors influencing the PACS selection. Data were collected through semistructured interviews with 10 experts in three educational hospitals and in the position to make decision for the purchase of PACS. Data were analyzed by the conventional qualitative content analysis method proposed by Lundman and Graneheim. Results Analyses achieved 11 subcategories in two specific and general categories that influence PACS selection. The specific category of this study included six subcategories, and the general category included five subcategories. Conclusion The results of this study determined that usability was the most important factor from the perspective of participants. Since the main users of a system have a critical role in adoption or rejection of a system, ease of use (usability) is significant and must be considered in system selection as a significant factor.

Objectives Users attitude toward a picture archiving and communication system (PACS) and their interaction with this system are among the most important factors that influence its acceptance. This study aimed to augment the user's interaction with the user's perspective to select a usable PACS among three systems available on the market. Methods We augmented the think aloud (TA) usability evaluation method with the Post-Study System Usability Questionnaire (PSSUQ) to compare user interaction problems of three PACS user interfaces. Four radiologists and four internist physicians participated in this study. Usability characteristics including efficiency, effectiveness, learnability, error, and satisfaction were used to assess the usability of each PACS. Results There was a significant difference in efficiency (p = 0.01), effectiveness (p = 0.005), learnability (p = 0.001), and satisfaction (p = 0.009). However, no significant difference in the number of errors (p = 0.18), mouse clicks and keystrokes (p = 0.12), and the number of usability problems (p = 0.6) were observed among the three PACS systems studied. Conclusions This study showed that applying the proposed approach to augment TA with the user's perspective addresses almost all of the theoretical aspects of usability and can be employed to select the most usable PACS.

Objective: There are several user-based and expert-based usability evaluation methods that may perform differently according to the context in which they are used. The objective of this study was to compare 2 expert-based methods, heuristic evaluation (HE) and cognitive walkthrough (CW), for evaluating usability of health care information systems. Materials and methods: Five evaluators independently evaluated a medical office management system using HE and CW. We compared the 2 methods in terms of the number of identified usability problems, their severity, and the coverage of each method. Results: In total, 156 problems were identified using the 2 methods. HE identified a significantly higher number of problems related to the “satisfaction” attribute (P = .002). The number of problems identified using CW concerning the “learnability” attribute was significantly higher than those identified using HE (P = .005). There was no significant difference between the number of problems identified by HE, based on different usability attributes (P = .232). Results of CW showed a significant difference between the number of problems related to usability attributes (P Conclusion: This study showed that HE and CW do not differ significantly in terms of the number of usability problems identified, but they differ based on the severity of problems and the coverage of some usability attributes. The results suggest that CW would be the preferred method for evaluating systems intended for novice users and HE for users who have experience with similar systems. However, more studies are needed to support this finding.

Background and Objectives: Colon cancer is the third most common cancer in the world and the fourth most common cancer in Iran. It is very important to predict the cancer outcome and its basic clinical data. Due to to the high rate of colon cancer and the benefits of data mining to predict survival, the aim of this study was to survey two widely used machine learning algorithms, Bagging and Support Vector Machines (SVM), to predict the outcome of colon cancer patients. Methods: The population of this study was 567 patients with stage 1-4 of colon cancer in Namazi Radiotherapy Center, Shiraz in 2006-2011. Three hundred and thirty eight patients were alive and 229 patients were dead. We used the Support Vector Machines (SVM) and Bagging methods in order to predict the survival of patients with colon cancer. The Weka software ver 3.6.10 was used for data analysis. Results: The performance of two algorithms was determined using the confusion matrix. The accuracy, specificity, and sensitivity of the SVM was 84.48%, 81%, and 87%, and the accuracy, specificity, and sensitivity of Bagging was 83.95%, 78%, and 88%, respectively. Conclusion: The results showed both algorithms have a high performance in survival prediction of patients with colon cancer but the Support Vector Machines has a higher accuracy.