Comparing ChatGPT and Bing, in response to the Home Blood Pressure Monitoring (HBPM) knowledge checklist.

High blood pressure is one of the major public health problems that is prevalent worldwide. Due to the rapid increase in the number of users of artificial intelligence tools such as ChatGPT and Bing, it is expected that patients will use these tools as a source of information to obtain information about high blood pressure. The purpose of this study is to check the accuracy, completeness, and reproducibility of answers provided by ChatGPT and Bing to the knowledge questionnaire of blood pressure control at home. In this study, ChatGPT and Bing's responses to the HBPM 10-question knowledge checklist on blood pressure measurement were independently reviewed by three cardiologists. The mean accuracy rating of ChatGPT was 5.96 (SD = 0.17) indicating the responses were highly accurate overall, with the vast majority receiving the top score. The mean accuracy and completeness of ChatGPT were 5.96 (SD = 0.17) and 2.93 (SD = 0.25) and in Bing were 5.31 (SD = 0.67), and 2.13 (SD = 0.53) Respectively. Due to the expansion of artificial intelligence applications, patients can use new tools such as ChatGPT and Bing to search for information and at the same time can trust the information obtained. we found that the answers obtained from ChatGPT are reliable and valuable for patients, while Bing is also considered a powerful tool, it has more limitations than ChatGPT, and the answers should be interpreted with caution.

Investigating the performance of machine learning algorithms in predicting the survival of COVID-19 patients: A cross section study of Iran.

Background and Aims: Like early diagnosis, predicting the survival of patients with Coronavirus Disease 2019 (COVID-19) is of great importance. Survival prediction models help doctors be more cautious to treat the patients who are at high risk of dying because of medical conditions. This study aims to predict the survival of hospitalized patients with COVID-19 by comparing the accuracy of machine learning (ML) models. Methods: It is a cross-sectional study which was performed in 2022 in Fasa city in Iran country. The research data set was extracted from the period February 18, 2020 to February 10, 2021, and contains 2442 hospitalized patients' records with 84 features. A comparison was made between the efficiency of five ML algorithms to predict survival, includes Naive Bayes (NB), K-nearest neighbors (KNN), random forest (RF), decision tree (DT), and multilayer perceptron (MLP). Modeling steps were done with Python language in the Anaconda Navigator 3 environment. Results: Our findings show that NB algorithm had better performance than others with accuracy, precision, recall, F-score, and area under receiver operating characteristic curve of 97%, 96%, 96%, 96%, and 97%, respectively. Based on the analysis of factors affecting survival, heart disease, pulmonary diseases and blood related disease were the most important disease related to death. Conclusion: The development of software systems based on NB will be effective to predict the survival of COVID-19 patients.

Supervised Machine Learning Approach to COVID-19 Detection Based on Clinical Data.

Background: The new coronavirus has been spreading since the beginning of 2020, and many efforts have been made to develop vaccines to help patients recover. It is now clear that the world needs a rapid solution to curb the spread of COVID-19 worldwide with non-clinical approaches such as artificial intelligence techniques. These approaches can be effective in reducing the burden on the health care system to provide the best possible way to diagnose the COVID-19 epidemic. This study was conducted to use Machine Learning (ML) algorithms for the early detection of COVID-19 in patients. Methods: This retrospective study used data from hospitals affiliated with Shiraz University of Medical Sciences in Iran. This dataset was collected in the period March to October 2020 andcontained 10055 cases with 63 features. We selected and compared six algorithms: C4.5, support vector machine (SVM), Naive Bayes, logistic Regression (LR), Random Forest, and K-Nearest Neighbor algorithm using Rapid Miner software. The performance of algorithms was measured using evaluation metrics, such as precision, recall, accuracy, and f-measure. Results: The results of the study show that among the various used classification methods in the diagnosis of coronavirus, SVM (93.41% accuracy) and C4.5 (91.87% accuracy) achieved the highest performance. According to the C4.5 decision tree, "contact with a person who has COVID-19" was considered the most important diagnostic criterion based on the Gini index. Conclusion: We found that ML approaches enable a reasonable level of accuracy in the diagnosis of COVID-19.

Lipid profile dysregulation in opium users based on Fasa PERSIAN cohort study results.

One of the main health problems in many societies is the increased opium abuse, which was found to be correlated with many problems like cardiovascular disease. This study aimed to evaluate the correlation of opium use with blood lipoproteins as the risk factor of CVD. This was a cross-sectional study conducted on participants of the first phase of the PERSIAN Cohort study who were aged between 35 and 70 years old. Demographic characteristics; history of smoking, alcohol, and opium consumption; medical history; and medications were asked and the related checklists were filled out. The levels of physical activity and fat intake were also registered. As well, lipoprotein profiles were investigated by blood sampling. The linear and logistic regression was used to analyze the relationship between opium and lipid profile and the statistical significant level was considered as < 0.05. Among 9300 participants with a mean age of 48.06 ± 9.44 years old, 49.6% of them were men. About 24.1% of the participants used opium. In the linear regression models, unlike TG (ß = 2.2, p = 0.36), total cholesterol (ß = - 2.5, p = 0.02), LDL (ß = - 2.0, p = 0.04), and HDL (ß = - 1.0, p = 0.04) were significantly lower in people who used opium compared to the others. In the logistic regression models, abnormal level of LDL (OR = 0.78, p = 0.003) and total cholesterol (OR = 0.82, p = 0.008) were less in people who used opium compared to the others. This study showed that there is a correlation between opium usage and lower levels of total cholesterol and LDL; however, the lower level of HDL in normal range was seen in opium users. Considering the current evidences, most of them showed the increased risks of ischemic heart disease, heart attack, hypertension, cerebrovascular disease, and cancer in opium users. Therefore, Healthcare providers and patients should be noticed about the deleterious effects of opium consumption on various vascular events. In addition, it is necessary for managers and policy makers of the health care system to take the necessary measures to raise the level of awareness and health literacy of the general public about the high-risk side effects of opium use and to take necessary and effective strategies to prevent and reduce its use.

The use of mobile health interventions for gestational diabetes mellitus: a descriptive literature review.

This study attempted to review the evidence for or against the effectiveness of mobile health (m-health) interventions on health outcomes improvement and/or gestational diabetes mellitus (GDM) management. PubMed, Web of Science, Scopus, and Embase databases were searched from 2000 to 10 July 2018 to find studies investigating the effect of m-health on GDM management. After removing duplications, a total of 27 articles met our defined inclusion criteria. m-health interventions were implemented by smartphone, without referring to its type, in 26% (7/27) of selected studies, short message service (SMS) in 14.9% (4/27), mobile-based applications in 33.3% (9/27), telemedicine-based on smartphones in 18.5% (5/27), and SMS reminder system in 7.1% (2/27). Most of the included studies (n=23) supported the effectiveness of m-health interventions on GDM management and 14.3% (n=4) reported no association between m-health interventions and pregnancy outcomes. Based on our findings, m-health interventions could enhance GDM patients' pregnancy outcomes. A majority of the included studies suggested positive outcomes. M-health can be one of the most prominent technologies for the management of GDM.

Clinical decision support systems-based interventions to improve medication outcomes: A systematic literature review on features and effects.

Background: Clinical decision support systems (CDSSs) interventions were used to improve the life quality and safety in patients and also to improve practitioner performance, especially in the field of medication. Therefore, the aim of the paper was to summarize the available evidence on the impact, outcomes and significant factors on the implementation of CDSS in the field of medicine. Methods: This study is a systematic literature review. PubMed, Cochrane Library, Web of Science, Scopus, EMBASE, and ProQuest were investigated by 15 February 2017. The inclusion requirements were met by 98 papers, from which 13 had described important factors in the implementation of CDSS, and 86 were medicated-related. We categorized the system in terms of its correlation with medication in which a system was implemented, and our intended results were examined. In this study, the process outcomes (such as; prescription, drug-drug interaction, drug adherence, etc.), patient outcomes, and significant factors affecting the implementation of CDSS were reviewed. Results: We found evidence that the use of medication-related CDSS improves clinical outcomes. Also, significant results were obtained regarding the reduction of prescription errors, and the improvement in quality and safety of medication prescribed. Conclusion: The results of this study show that, although computer systems such as CDSS may cause errors, in most cases, it has helped to improve prescribing, reduce side effects and drug interactions, and improve patient safety. Although these systems have improved the performance of practitioners and processes, there has not been much research on the impact of these systems on patient outcomes.

Better monitoring of abused children by designing a child abuse surveillance system: Determining national child abuse minimum data set.

BACKGROUND: Monitoring the trend of child abuse can significantly help in measuring the magnitude of the problem and understanding its recurrence. The minimum data set (MDS) is a set of elements of each domain that provides the basis for decision-making. This study was conducted to determine the comprehensive national minimum data set for child abuse surveillance system (CASS) in Iran. METHODS: This is a cross-sectional descriptive study. Data were gathered from the selected countries and child abuse registry and surveillance systems. The MDS questionnaire was designed based on a review of the publications and experts' opinions. The final data elements of the CASS were determined using the Delphi technique by visiting pediatricians. RESULTS: In total, 147 data elements were included in the Delphi survey. The data elements of the CASS were classified into seven categories as follows: demographic data, incident related data, medical history, diagnostic tests, incident nature, therapeutic measures, and other required data. CONCLUSION: The existence of national MDS as the core of the child abuse surveillance program is essential and leads to appropriate decisions in this regard. The MDS can meet the needs of professionals, decision makers, researchers, and policymakers who decide on reducing the incidence of child abuse.

Data Requirements for Information Management System Development for Poisoning with Acidic and Alkaline Substances.

INTRODUCTION: Ingestion of acidic or alkaline substances and its long-term effects on digestive system indicates is a common health problem worldwide. To identify the root causes of injuries, standard data collection is required. AIM: The present study was conducted to determine the data requirements for the establishment of information management system for poisoning with acidic and alkaline substances in Iran. METHODS: This is a descriptive and cross-sectional study conducted in 2017. First, we attended at the hospitals affiliated to Iran, Tehran and Shahid Beheshti universities of medical sciences, which had poisoning wards; we studied all forms, reports and medical records of people who had been poisoned by acidic or alkaline substances. In the next step, a comprehensive literature review was carried out to retrieve related resources. Data were collected using data extraction form and Delphi method was used to survey them. Validity of the questionnaire was evaluated through content validity and its reliability checked by the test-retest method and Cronbach's alpha. RESULTS: A minimum data set (MDS) of alkaline and acid poisoning divided into two categories: administrative with three classes including 35 data elements, and clinical with 6 classes including 145 data elements. CONCLUSION: Comprehensive and uniform data elements about alkaline and acid poisoning was not available in Iran. Development of a MDS resulted in standardization and effective management of the data through providing uniform and comprehensive data elements for alkaline and acid poisoning and comparability of information in various levels and made effective decision-making and policy-making possible.

Design of Infertility Monitoring System: Minimum Data Set Approach.

Reproductive health is vital for human and infertility is also one of the most important challenges in the reproductive system. Infertility is one of the most common chronic health disorders, regardless of age. The Minimum Data Set (MDS) helps to manage infertility by monitoring and evaluating infertility interventions based on collecting data. The development of MDS is an essential objective in order to implement an infertility monitoring system for the creation of standardized and effective data management through the provision of comprehensive and identical data elements for infertility. This is a descriptive cross-sectional study conducted in 2017. The data has been collected from infertility clinics in the world, as well as WHO, CDC, ASRM, and ESHRE reports. In order to decide on data elements, the Delphi technique was used using a questionnaire that contained data elements which were distributed among 12 experts including one reproductive endocrinology and infertility fellow, six obstetrician-gynecologists, two reproductive biologists, two urologists and one community medicine specialist using the 5 point Likert scale. The questionnaire was divided into two categories: managerial and clinical, each with 4 sections, and 60 and 940 data elements, respectively. MDS is an essential tool for evaluating the infertility process. Using this tool will provide an opportunity to develop a set of quality care criteria that can be used to ensure the quality of infertility care.

ART Registries-Characteristics and experiences: A comparative study.

BACKGROUND: The incident of infertility is continuously increasing. As a result, the demand for medical care such as assisted reproductive technology (ART) technology is equally increasing. In order to manage the growing data and information collected on ART, there is a need for a registry system can provide accurate statistics about activities and outcomes and ensure the quality control. Therefore, the aim of this study was to examine and compare In vitro fertilization (IVF) and ART registries. METHODS: This is a descriptive-comparative study in which data from the national ART registries of 14 selected countries in 2018 were collected. In this study, databases such as PubMed, Web of Sciences, and Scopus, as well as Google Scholar websites were searched. RESULTS: Important aspects of the registry were studied. One of the most important goals of these systems is to collect information about ART, as well as to monitor and report the results and implications, and also implement new care plans. CONCLUSION: A national registry helps to better understand the scope and the effect of assisted reproduction on the health of infertile couples. By this registry system, different countries can compare the data with other countries, allowing the improvement of techniques and the best possible care for patients.