eHealth solutions to fight against COVID-19: A scoping review of applications.

Background: eHealth has a notable potential to help in prevention, diagnosis, treatment, screening, management, and control of the COVID-19 pandemic. Since ehealth is considered here broadly, as an umbrella term, it also covers subsets like telehealth and mhealth. This study aimed to review the literature to identify and classify subdomains of eHealth solutions that have been utilized, developed, or suggested for the COVID-19 pandemic. Methods: A comprehensive literature search was performed using the PubMed, Scopus, Embase, and Cochrane library databases in April 2020, with no time limitation. The search strategy was built based on 2 concept domains of eHealth solutions and covid-19. For each concept domain, the search query comprised a combination of free text keywords identified from reference papers and controlled vocabulary terms. Obtained results were classified, graphically presented, and discussed. Results: Of the 423 studies identified initially, 35 were included in this study. From related papers, general characteristics, study objective, eHealth-related outcomes, target populations, eHealth interventions, health service category, eHealth solution, and eHealth domain were extracted, classified, and tabulated. Most publication types were ideas, editorials, or opinions (46%). The most targeted populations were people of the community and medical staff (80%). The most implemented or suggested eHealth solution was telehealth (63%), followed by mhealth, health information technology, and health data analytics. Most of the COVID-19 ehealth interventions designed or suggested for improving prevention (48%) and diagnosis (48%). Most of the studies applied or proposed eHealth solutions for general practice or epidemiological purposes (48%). Conclusion: eHealth solutions have the potential to provide useful services to help in COVID-19 pandemics in terms of prevention, diagnosis, treatment, screening, surveillance, resource allocation, education, management, and control. The obtained results from this review might be used for a better understanding of current ehealth solutions provided or recommended in response to the COVID-19 pandemic.

Application and evaluation of virtual technologies for anatomy education to medical students: A review.

To learn anatomy, medical students need to look at body structures and manipulate anatomical structures. Simulation-based education is a promising opportunity for the upgrade and sharing of knowledge. The purpose of this review is to investigate the evaluation of virtual technologies in teaching anatomy to medical students. METHODS: In this review, we searched PubMed, Web of Sciences, Scopus, and Embase for relevant articles in November 2018. Information retrieval was done without time limitation. The search was based on the following keywords: virtual reality, medical education, and anatomy. RESULTS: 2483 articles were identified by searching databases. Finally, the fulltext of 12 articles was reviewed. The results of the review showed that virtual technologies had been used to train internal human anatomy, ear anatomy, nose anatomy, temporal bone anatomy, surgical anatomy, neuroanatomy, and cardiac anatomy. CONCLUSION: Virtual reality, augmented reality, and games can enhance students' anatomical learning skills and are proper alternatives to traditional methods in case of no access to the cadavers and mannequin.

Virtual reality applications for chronic conditions management: A review.

Background: Virtual Reality (VR) as a computer technology that simulating real environments and situations exploited in numerous healthcare areas such as chronic diseases. The significance of timely treatment and rehabilitation of patients with chronic conditions is high due to the long lasting nature of these conditions. This paper sought to perform a review of published works in the field of VR application in chronic conditions for treatment and rehabilitation purposes. Methods: We searched the MEDLINE database through PubMed in April 2016 for retrieving published papers from January 2001 to December 2015. From 117 retrieved papers, 52had the inclusion criteria, and their full texts were accessible. Data were extracted from papers based on following items: the name of the first author, year of the study, applied VR methods, type of condition and disease, number of subjects that participated in the study, and finally the status of success and failure of VR application. Data were analyzed using descriptive analysis. Results: Results of the reviewed investigations have been considered in two main categories including treatment oriented papers (n=38, 73%) while twenty of these papers have been conducted on phobias (53%); also, there are rehabilitation-oriented experiments (n=14, 27%) while thirteen of these papers have been performed on stroke. In 40 papers (77%), the VR technology application reported proper and in 11 papers (21%) the application of VR resulted in relatively proper outcomes and only there is a work (2%) with poor results for VR intervention. Conclusion: VR technology has been increasingly used in recent years for treatment and rehabilitation purposes among patients affected by chronic conditions in order to motivate them for more successful management.

Forecasting zoonotic cutaneous leishmaniasis using meteorological factors in eastern Fars province, Iran: a SARIMA analysis.

OBJECTIVES: To predict the occurrence of zoonotic cutaneous leishmaniasis (ZCL) and evaluate the effect of climatic variables on disease incidence in the east of Fars province, Iran using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. METHODS: The Box-Jenkins approach was applied to fit the SARIMA model for ZCL incidence from 2004 to 2015. Then the model was used to predict the number of ZCL cases for the year 2016. Finally, we assessed the relation of meteorological variables (rainfall, rainy days, temperature, hours of sunshine and relative humidity) with ZCL incidence. RESULTS: SARIMA(2,0,0) (2,1,0)12 was the preferred model for predicting ZCL incidence in the east of Fars province (validation Root Mean Square Error, RMSE = 0.27). It showed that ZCL incidence in a given month can be estimated by the number of cases occurring 1 and 2 months, as well as 12 and 24 months earlier. The predictive power of SARIMA models was improved by the inclusion of rainfall at a lag of 2 months (ß = -0.02), rainy days at a lag of 2 months (ß = -0.09) and relative humidity at a lag of 8 months (ß = 0.13) as external regressors (P-values < 0.05). The latter was the best climatic variable for predicting ZCL cases (validation RMSE = 0.26). CONCLUSIONS: Time series models can be useful tools to predict the trend of ZCL in Fars province, Iran; thus, they can be used in the planning of public health programmes. Introducing meteorological variables into the models may improve their precision.

Personalized Mobile Health for Elderly Home Care: A Systematic Review of Benefits and Challenges.

Mobile health as one of the new technologies can be a proper solution to support care provision for the elderly and provide personalized care for them. This study is aimed at reviewing the benefits and challenges of personalized mobile health (PMH) for elderly home care. With a systematic review methodology, 1895 records were retrieved by searching four databases. After removing duplicates, 1703 articles remained. Following full-text examination, 21 articles that met the inclusion criteria were studied in detail, and the output was presented in different tables. The results indicated that 25% of the challenges were related to privacy, cybersecurity, and data ownership (10%), technology (7.5%), and implementation (7.5%). The most frequent benefits were related to cost-saving (17.5%), nurse engagement improvement (10%), and caregiver stress reduction (7.5%). In general, the number of benefits in this study was slightly higher than the challenges, but in order to use PMH technologies, the challenges presented in this study must be carefully considered and a suitable solution must be adopted. Benefits can also be helpful in persuading individuals and health-care providers. This study shed light on those points that need to be highlighted for further work in order to convert the challenges toward benefits.

Designing and Evaluating a Nutrition Recommender System for Improving Food Security in a Developing Country.

BACKGROUND: Due to the increased price of foods in recent years and the diminished food security in Iran, nutrition recommender systems can suggest the most suitable and affordable foods and diets to users based on their health status and food preferences. Objective: The present study aimed to design and evaluate a recommender system to suggest healthy and affordable meals and provide a tele-nutrition consulting service. METHODS: This applied three-phase study was conducted in 2020. In the first stage, the food items' daily prices were extracted from credible sources, and accordingly, meals were placed in three price categories. After conducting a systematic review of similar systems, the requirements and data elements were specified and confirmed by 10 nutritionists and 10 health information management and medical informatics experts. In the second phase, the software was designed and developed based on the findings. In the third phase, system usability was evaluated by four experts based on Nielsen's heuristic evaluation. RESULTS: Initially, 72 meals complying with nutritional principles were placed in three price categories. Following a literature review and expert survey, 31 data elements were specified for the system, and the experts confirmed system requirements. Based on the information collected in the previous stage, the Web-based software TanSa in the Persian language was designed, developed, and presented on a unique domain. During the evaluation, the mean severity of the problems associated with Nielsen's 10 principles was 1.2, which is regarded as minor. CONCLUSION: To promote food security, the designed system recommends healthy, nutritional, and affordable meals to individuals and households based on user characteristics.

A systematic review of the predicted outcomes related to hematopoietic stem cell transplantation: focus on applied machine learning methods' performance.

INTRODUCTION: Hematopoietic stem cell transplantation (HSCT) is a critical therapeutic procedure in blood diseases, and the investigation of HSCT data can provide valuable information. Machine learning (ML) techniques are useful data analysis tools which applied in many studies to predict HSCT survival and estimate the risk of transplantation. AREAS COVERED: A systematic review was performed with a search of PubMed, Science Direct, Embase, Scopus, and the European Society for Blood and Marrow Transplantation, the Center for International Blood and Marrow Transplant Research, and the American Society for Transplantation and Cellular Therapy publications for articles published by September 2020. EXPERT OPINION: 24 papers that met eligibility criteria were included in this study. The applied ML algorithms with the highest performance were Random Survival Forests (AUC = 0.72) for survival-related, Random Survival Forests and Logistic Regression (AUC = 0.77) for mortality-related, Deep Learning (AUC = 0.8) for relapse, L2-Regularized Logistic Regression (AUC = 0.66) for Acute-Graft Versus Host Disease, Random Survival Forests (AUC = 0.88) for sepsis, Elastic-Net Regression (AUC = 0.89) for cognitive impairment, and Bayesian Network (AUC = 0.997) for oral mucositis outcome. This review reveals the potential of ML techniques to predict HSCT outcomes and apply them to developing clinical decision support systems.

Development and validation of the Neonatal Abstinence Syndrome Minimum Data Set (NAS-MDS): a systematic review, focus group discussion, and Delphi technique.

OBJECTIVES: Neonatal abstinence syndrome (NAS) is a combination of symptoms in infants exposed to any variety of substances in utero. Information systems and registries help to collect information about these patients; however, there is always a deep gap between complete and accurate information to be collected, understood, and applied in the health care system; thus, defining a minimum data sets (MDS) as one of the primarily steps of designing a registry system is essential. The aim of this study was to develop an MDS of the registry for infants with NAS in Iran. METHODS: This research is a descriptive cross-sectional study. In this study, three steps were carried out to develop the MDS including systematic review, Delphi technique, and focus group discussion. A systematic review was conducted in relevant databases to identify appropriate related data. In the second phase, a focus group discussion was used to classify the extracted data elements by contributing neonatologists. Finally, data elements were chosen through the decision Delphi technique in two distinct rounds. Collected data were analyzed using SPSS 22 (SPSS Inc., Chicago, IL). RESULTS: By reviewing related papers and available NAS registries in other countries, 145 essential data elements were identified. They were classified into two main categories based on the eight experts' opinions including maternal with two sections and infant with two sections. After applying two rounds of Delphi technique, the final data elements for maternal and infant categories were 42 and 31, respectively. Thus, on completion of the survey, 73 data elements were approved. CONCLUSION: The proposed MDS for NAS can help to store an accurate and comprehensive data, document medical records, integrate them with other information systems and registries, and communicate with other healthcare providers and healthcare centers. This MDS can contribute to the provision of high-quality care and better clinical decisions.

Effect of social media interventions on the education and communication among patients with cancer: a systematic review protocol.

INTRODUCTION: Cancer is a leading cause of death worldwide. In addition, it accounted for approximately 10 million deaths in 2020 alone. Information and communication technologies have great potential for improving health education and communication. Social media is one of the technologies that can help patients with cancer and healthcare providers communicate and provide educational information. Social media are increasingly being used for health promotion and behaviour change. This is a protocol of systematic review to identify the effect of social media interventions on the education and communication among patients affected by cancer. This study aims to reveal the steps of conducting research that systematically reviews all studies for the specific objective. This study aims to examine the social media interventions to improve awareness and knowledge about the disease for patients with cancer and improve communication among them. METHODS AND ANALYSIS: This protocol is reported in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols checklist. We will include experimental design studies that report the effect of social media interventions on education and communication among patients with cancer or malignancy and any stage of the disease. Interventions will be inclusive, using all social network platforms for patients' communication and education. We will search PubMed, Web of Science, Scopus and the Cochrane Library from inception until 23 May 2022. Two independent reviewers will screen titles, abstracts and full-text articles with conflicts resolved through discussion or by a third reviewer, as needed. All titles, abstracts and full-text papers will be reviewed independently by two reviewers according to the inclusion and exclusion criteria. Discrepancies will resolve by discussion or SRNK if needed. The two reviewers will also independently complete risk of bias assessments for each included study. The descriptive analysis, including frequency and percentage parameters, will be calculated based on the study's variables. Furthermore, we will report the results of the quality assessment of studies in table format. In the result section, a narrative synthesis will be applied to describe and compare the paper's results. ETHICS AND DISSEMINATION: Ethics approval will not be needed because the data to be used in this systematic review and meta-analysis will be extracted from published studies. It will be disseminated by publication in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42022334691.

STO: Stroke Ontology for Accelerating Translational Stroke Research.

INTRODUCTION: Ontology-based annotation of evidence, using disease-specific ontologies, can accelerate analysis and interpretation of the knowledge domain of diseases. Although many domain-specific disease ontologies have been developed so far, in the area of cardiovascular diseases, there is a lack of ontological representation of the disease knowledge domain of stroke. METHODS: The stroke ontology (STO) was created on the basis of the ontology development life cycle and was built using Protégé ontology editor in the ontology web language format. The ontology was evaluated in terms of structural and functional features, expert evaluation, and competency questions. RESULTS: The stroke ontology covers a broad range of major biomedical and risk factor concepts. The majority of concepts are enriched by synonyms, definitions, and references. The ontology attempts to incorporate different users' views on the stroke domain such as neuroscientists, molecular biologists, and clinicians. Evaluation of the ontology based on natural language processing showed a high precision (0.94), recall (0.80), and F-score (0.78) values, indicating that STO has an acceptable coverage of the stroke knowledge domain. Performance evaluation using competency questions designed by a clinician showed that the ontology can be used to answer expert questions in light of published evidence. CONCLUSIONS: The stroke ontology is the first, multiple-view ontology in the domain of brain stroke that can be used as a tool for representation, formalization, and standardization of the heterogeneous data related to the stroke domain. Since this is a draft version of the ontology, the contribution of the stroke scientific community can help to improve the usability of the current version.

A review and content analysis of national apps for COVID-19 management using Mobile Application Rating Scale (MARS).

The expansion of mobile health apps for the management of COVID-19 grew exponentially in recent months. However, no study has evaluated these apps. The objective of this study was to develop a reliable measure and rate the quality of COVID-19 mobile health apps, to eventually provide a roadmap for future mHealth app development. In this study, we used COVID-related keywords to identify apps for iOS and Android devices. 13 apps (13.5% of the total number of apps identified) were selected for evaluation. App quality was assessed independently using MARS by two reviewers. Search queries yielded a total of 97 potentially relevant apps, of which 13 met our final inclusion criteria. Kendall's coefficient of concordance value for the inter-rater agreement was 0.93 (p = .03). COVID-19 GOV PK app had the highest average MARS score (4.7/5), and all of the apps had acceptable MARS scores (> 3.0). This study suggests that most COVID-related apps meet acceptable criteria for quality, content, or functionality, and they must highlight esthetic and interesting features for overall quality improvement to be welcomed by users.

Prediction of Patient's Adherence to the Post-Intubation Tracheal Stenosis Follow-up Plan in Iran: Application of two Data Mining Techniques.

BACKGROUND: Timely diagnosis of post-intubation tracheal stenosis (PITS), which is one of the most serious complications of endotracheal intubation, may change its natural history. To prevent PITS, patients who are discharged from the intensive care unit (ICU) with more than 24 hours of intubation should be actively followed-up for three months after extubation. This study aimed to evaluate the abilities of artificial neural network (ANN) and decision tree (DT) methods in predicting the patients' adherence to the follow-up plan and revealing the knowledge behind PITS screening system development requirements. MATERIALS AND METHODS: In this cohort study, conducted in 14 ICUs during 12 months in ten cities of Iran, the data of 203 intubated ICU-discharged patients were collected. Ten influential factors were defined for adherences to the PITS follow-up (P<0.05). A feed-forward multilayer perceptron algorithm was applied using a training set (two-thirds of the entire data) to develop a model for predicting the patients' adherence to the follow-up plan three months after extubation. The same data were used to develop a C5.0 DT in MATLAB 2010a. The remaining one-third of data was used for model testing, based on the holdout method. RESULTS: The accuracy, sensitivity, and specificity of the developed ANN classifier were 83.30%, 72.70%, and 89.50%, respectively. The accuracy of the DT model with five nodes, 13 branches, and nine leaves (producing nine rules for active follow-up) was 75.36%. CONCLUSION: The developed classifier might aid care providers to identify possible cases of non-adherence to the follow-up and care plans. Overall, active follow-up of these patients may prevent the adverse consequences of PITS after ICU discharge.

Optimized Patients' Length of Hospital Stay with Interventions Based on Health Information Technology: A Review Study.

BACKGROUND: Information and communications technologies (ICTs) may facilitate shorting length of stay (LOS) of patients through the optimization of processes and delivery services. OBJECTIVES: This study aims to provide technology-based solutions and interventions based on health information technology (HIT) that have optimization potentials of patients' LOS. METHODS: This review study searched papers in PubMed, Scopus as well as Google Scholar without presuming time limits by the end of 2019. English and Persian Papers were included, which addressed an association between the ICT and LOS as well as its positive effect in shortening LOS. RESULTS: Identified technologies were finally classified into eleven groups. Based on the findings, common health technologies such as health information systems, telemedicine especially tele-consultation, electronic discharge planning tools, and visual analytical dashboards in order to expedite the process and help to optimize LOS seem appropriate. CONCLUSIONS: HIT-based interventions have potential that may support better management of processes related to patients' admission, hospitalization, and discharge. However consistently evaluation along with using any new technology is necessary.

Surgical Patients Follow-Up by Smartphone-Based Applications: A Systematic Literature Review.

BACKGROUND: Telemedicine technology with the development of mobile applications (apps) has provided a new approach for the follow-up of patients. OBJECTIVES: This study aims to carry out an overview of the studies related to the use of mobile apps in the follow-up of surgical patients. METHODS: In this study, an electronic search of four databases included PubMed, Scopus, Embase, and web of science was carried out. It included studies in the English language from the beginning of 2009 to June 2019. RESULTS: Twenty-three articles were selected for the final analysis, that all of them were published from 2015 onwards. In most studies, fourteen to thirty-days follow-up period for different outpatient and inpatient surgeries was planned. Apps' components in the studies mostly include indexes for evaluation of recovery quality, pain level, and the surgical site infection. The most important achievement of studies included feasibility, early detection of complications, reducing unscheduled in-person visits, patients' self-efficiency, and satisfaction. CONCLUSIONS: Our review showed that mHealth-based interventions have potential that may support better management of post-discharge systematic follow-up of surgery patients.

Artificial Intelligence Applications in Type 2 Diabetes Mellitus Care: Focus on Machine Learning Methods.

OBJECTIVES: The incidence of type 2 diabetes mellitus has increased significantly in recent years. With the development of artificial intelligence applications in healthcare, they are used for diagnosis, therapeutic decision making, and outcome prediction, especially in type 2 diabetes mellitus. This study aimed to identify the artificial intelligence (AI) applications for type 2 diabetes mellitus care. METHODS: This is a review conducted in 2018. We searched the PubMed, Web of Science, and Embase scientific databases, based on a combination of related mesh terms. The article selection process was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Finally, 31 articles were selected after inclusion and exclusion criteria were applied. Data gathering was done by using a data extraction form. Data were summarized and reported based on the study objectives. RESULTS: The main applications of AI for type 2 diabetes mellitus care were screening and diagnosis in different stages. Among all of the reviewed AI methods, machine learning methods with 71% (n = 22) were the most commonly applied techniques. Many applications were in multi method forms (23%). Among the machine learning algorithms applications, support vector machine (21%) and naive Bayesian (19%) were the most commonly used methods. The most important variables that were used in the selected studies were body mass index, fasting blood sugar, blood pressure, HbA1c, triglycerides, low-density lipoprotein, high-density lipoprotein, and demographic variables. CONCLUSIONS: It is recommended to select optimal algorithms by testing various techniques. Support vector machine and naive Bayesian might achieve better performance than other applications due to the type of variables and targets in diabetes-related outcomes classification.

Determining minimum set of features for diabetes mobile apps.

PURPOSE: Interest in mobile health applications (apps) for diabetes self-care is growing. Mobile health is a promising new treatment modality for diabetes, though few smartphone apps have been designed based on a proper study and prioritization. The aim of this study was to determine a minimum set of features for diabetes mobile apps. METHODS: This study was conducted in three steps: 1.A review of the literature to collect all available features, 2. Assessing the validity of suggested features by Content Validity Index (CVI) and Content Validity Ratio (CVR), 3. Examining the importance of features by Friedman test. RESULTS: We retrieved all features of available mobile apps for type 2 diabetes, which are suggested and discussed in literature and compiled as a single list comprising of 33 features. Then, a survey of expert's opinion produced a set of 23 final minimum features which includes all types of tracking, mealtime tagging, food database, diet management, educational materials, healthy coping, reducing risks, problem solving, Email, color coding, alerts, reminder, target range setting, trend chart view, logbook view, numerical indicators view, customizable theme, preset notes, and custom notes. According to the mean rank which indicates the priority of each feature, the most important one was blood glucose tracking (with 16.71 mean rank) and the least important feature was the numerical indicators like such as standard deviation or average (with 6.50 mean rank). CONCLUSIONS: The present study is the first step towards the development of our mobile apps for people with type II diabetes, and highest the essential features that are required for an optimal self-care comprehensively.

Modification of the Conventional Influenza Epidemic Models Using Environmental Parameters in Iran.

OBJECTIVES: The association between the spread of infectious diseases and climate parameters has been widely studied in recent decades. In this paper, we formulate, exploit, and compare three variations of the susceptible-infected-recovered (SIR) model incorporating climate data. The SIR model is a well-studied model to investigate the dynamics of influenza viruses; however, the improved versions of the classic model have been developed by introducing external factors into the model. METHODS: The modification models are derived by multiplying a linear combination of three complementary factors, namely, temperature (T), precipitation (P), and humidity (H) by the transmission rate. The performance of these proposed models is evaluated against the standard model for two outbreak seasons. RESULTS: The values of the root-mean-square error (RMSE) and the Akaike information criterion (AIC) improved as they declined from 8.76 to 7.05 and from 98.12 to 93.01 for season 2013/14, respectively. Similarly, for season 2014/15, the RMSE and AIC decreased from 8.10 to 6.45 and from 117.73 to 107.91, respectively. The estimated values of R(t) in the framework of the standard and modified SIR models are also compared. CONCLUSIONS: Through simulations, we determined that among the studied environmental factors, precipitation showed the strongest correlation with the transmission dynamics of influenza. Moreover, the SIR+P+T model is the most efficient for simulating the behavioral dynamics of influenza in the area of interest.

Questionnaire development and validation for designing a health telemonitoring system for frail elderly people.

BACKGROUND: Advancements in information technology have been instrumental in successful recent developments in telemonitoring systems. In this regard, there is a lack of development of valid and reliable tools to determine the requirements and applications of telemonitoring systems used to provide health care for frail elderly people living at home, specifically in a national setting. METHOD: A cross-sectional study was carried out in 2018. The statistical population was 15 geriatric and gerontology professionals and 15 health information management experts. Then, content validity ratio (CVR), Cronbach's alpha, and correlation coefficient were calculated for measuring content validity, internal consistency and external reliability (through the test-retest method) respectively. SPSS software was used to analyze the collected data. RESULTS: Based on the identified items, a draft questionnaire was developed. Using the validity analysis in two stages, 37 items were removed, and 60 items were approved as the essential system requirements. The final questionnaire was organized into five sections with content validity index 99% and internal reliability (Cronbach's alpha coefficient 0.9). Furthermore, the external reliability results of the questionnaire showed that this instrument has a desirable correlation coefficient (r = 0.85, p-value<0.05). CONCLUSION: Considering the desirable validity and reliability of the questionnaire developed, it is recommended to telemonitoring system designers to determine the usages and requirements of health monitoring systems for frail elderly people living at home. The verified instrument is suitable for use in countries with the same living conditions and level of development as Iran.

Predicting Length of Stay in Intensive Care Units after Cardiac Surgery: Comparison of Artificial Neural Networks and Adaptive Neuro-fuzzy System.

OBJECTIVES: Accurate prediction of patients' length of stay is highly important. This study compared the performance of artificial neural network and adaptive neuro-fuzzy system algorithms to predict patients' length of stay in intensive care units (ICU) after cardiac surgery. METHODS: A cross-sectional, analytical, and applied study was conducted. The required data were collected from 311 cardiac patients admitted to intensive care units after surgery at three hospitals of Shiraz, Iran, through a non-random convenience sampling method during the second quarter of 2016. Following the initial processing of influential factors, models were created and evaluated. RESULTS: The results showed that the adaptive neuro-fuzzy algorithm (with mean squared error [MSE] = 7 and R = 0.88) resulted in the creation of a more precise model than the artificial neural network (with MSE = 21 and R = 0.60). CONCLUSIONS: The adaptive neuro-fuzzy algorithm produces a more accurate model as it applies both the capabilities of a neural network architecture and experts' knowledge as a hybrid algorithm. It identifies nonlinear components, yielding remarkable results for prediction the length of stay, which is a useful calculation output to support ICU management, enabling higher quality of administration and cost reduction.

Study of challenges to utilise mobile-based health care monitoring systems: A descriptive literature review.

Mobile health encompasses remote and wireless applications to provide health services. Despite the advantages of applying mobile-based monitoring systems, there are challenges and limitations; understanding the challenges may assist in identifying available solutions and optimising decision-making to apply mHealth technologies more practically. This study aimed to investigate the main challenges related to mHealth-based systems for health monitoring purposes. This review was carried out through investigation of English evidence from four databases, including Scopus, PubMed, Embase, and Web of Science, using a defined search strategy from 2013 to 2017. Two independent researchers reviewed the results based on PRISMA guidelines, and data was categorised using a bottom-up approach to reach a framework for the most general challenges. Among the 105 papers obtained, eight works were selected. The revealed challenges were categorised into six main branches across a tree (with 55 nodes, four levels) including user-related, infrastructure, process, management, resource and training challenges. Identifying the resolvable and preventable challenges, such as those related to training, design might play a crucial role in preventing loss of resources and in growing the success rate of a project, particularly if considered in national level projects.