علیرضا آتشی

Artificial Intelligence Chatbots (AIC), nowadays, are one of the most important topics in natural language processing which is able to communicate with humans using natural language. The purpose of using AIC is to facilitate user interaction with services, products or companies. The purpose of this study is to investigate the applications of AIC in Iran’s health system generally and Iran Health Insurance Organization in particular and to provide services to people in this way, including the investigation of AIC and its need in the provision of health care, the study of the significant aspects of the workflow of AIC for healthcare, the AIC features in this field, and identifying the significant applications and limitations of artificial intelligence chatbot for providing healthcare services. Purveying AIC services to individuals, such as insurance credit information as well as offering contracting party centers are some of benefits of AIC application in Iran Health Insurance Organization.

During the COVID-19 pandemic, the existing resistance of physicians, care facilities, and the public to digital health or telehealth services was broken. Although digital health businesses in Iran have been expanding for the last decade, the COVID-19 pandemic resulted in realizing the goals intended to expand it. Considering that there is still no approved code of ethics about e-health and telehealth,now, the most important concern is awareness and compliance with ethical considerations in electronic health and telehealth platforms. In this paper, the important ethical axes were brought up in the form of twelve tips. Methods & Materials: A narrative review was conducted by searching the Scopus, PubMed, and Web of Sciences, for the years 2014 to 2022. Non-english language articles, proceedings, and papers related to the ethics of health e-learning, and telemedicine or e-health research were the study’s exclusion criteria.

The most important ethical considerations in 12 axes were classified including ”privacy“, use as a complementary solution“, ”equality and accessibility“, ”trust“, ”informed consent“, ”professional commitment and responsibility“, ”autonomy“, ”confidentiality“, ”validity and reliability“, ”compensation“, evaluation and feedback“ and ”license and certification“.

The twelve tips for ethics, should be the main axes of the e-health and telehealth code of ethics. Meanwhile, it is suggested that all digital or telehealth platforms, inform and sign these in the form of an ethical commitment letter to the people involved in service delivery. It is also necessary for users of these services to be aware of these twelve ethical points.

Mathematical modeling is one of the feasible methods that can be used to solve real problems. Modeling can be done using a variety of methods, including statistical methods that can be used to predict a variety of events. Health is one of the most important areas of research in the world today. Among the various diseases in the health sector, this study concerns stroke which is the second leading cause of death and long-term human disability, that has led to doing this research. The main objective of this research is to design and to build a predictive model of stroke based on symptoms and clinical reports, whether or not stroke occurs in patients in the near future. Using logistic regression technology, the main pathogenic factors of stroke have been found and their incidence has been predicted. In this study, clinical information from 5411 patients was collected and, after applying the LR method, the predictive model was designed.

Early outcome prediction of hospitalized patients is critical because the intensivists are constantly striving to improve patientschr('39') survival by taking effective medical decisions about ill patients in Intensive Care Units (ICUs). Despite rapid progress in medical treatments and intensive care technology, the analysis of outcomes, including mortality prediction, has been a challenge in ICUs. Hence, this study aims to predict the mortality of patients admitted to ICUs using data mining techniques.

In this study, among the cases of patients who were admitted to ICUs of the Rasoul Akram and Firoozgar hospitals of Tehran City, Iran, from December 2017 to March 2018, the first 24 hours of the ICUs admission data of 874 cases were gathered. A new model based on the standard methodology CRISP was developed. In the modeling section, two well-known data mining techniques called artificial neural network (ANN), K nearest neighbor (KNN) and decision tree (DT) were used. WEKA 3.9.2 open-source software was implemented for data analysis. Finally, according to the accuracy, sensitivity, specificity criteria and AUC-ROC Curve, the superior model was introduced.

Based on the WEKA results, 19 variables had the most impact on the mortality prediction of patients admitted to ICUs including Glasgow Coma Scale (GCS), mechanical ventilation, surgical service at ICUs admission, gender, temperature, serum creatinine, diabetes, Blood urea nitrogen (BUN), age, addiction, International Normalized Ratio (INR), PH, Partial Thromboplastin Time (PTT), albumin, hemoglobin, glucose, pulse rate, hematocrit (HCT), PO2.  Based on the created models, some rules have been extracted which can be used as a pattern to predict the probability of mortality. Although the AUC of the three models was acceptable (KNN 81.5%, ANN 77.5% and DT 74.3%), but the accuracy of decision tree J48 (74.2%) was higher.

The study indicated that in the KNN model, the rules derived from it can be effective in mortality prediction in patients admitted to ICUs.

The metastasis of breast cancer, the spread of cancer to different body parts, is considered as one of the most important factors responsible for the majority of deaths caused by breast cancer in women. Diagnosing the breast cancer metastasis at the earliest stages helps to choose the best treatment and improve the quality of life for patients.

In the present fundamental research, the dataset of Iranian patients available at Breast Cancer Research Center of Motamed Cancer Institute in Tehran was utilized. This study used Mamdani fuzzy inference system, Takagi-Sugeno fuzzy inference system and adaptive neuro-fuzzy inference system (ANFIS) to predict breast cancer metastasis at early stages.

The best prediction error was obtained using adaptive neuro-fuzzy inference system based on fuzzy c-means approach. The opinion of the experts at Breast Cancer Research Center of Motamed Cancer Institute and the prediction error of the assessed model indicated that this prediction system is well-formed.

The optimal proposed prediction system can be used as a clinical decision support system to assist medical practitioners in the healthcare practice

Intensive Care Unit (ICU) is a ward that is critical to improving the health status of critical conditions. Data mining seems to be a good way to optimize the use of resources. Identifying and analyzing the risk factors associated with mortality will lead to more efficient and accurate planning of hospitalization and interventions. In this study, the prediction of mortality of patients in the intensive care unit of Imam Hossein Hospital in Tehran with data mining techniques is discussed.

Based on patient records and hospital information system, 838 patients admitted to the General intensive care unit between 2013 and 2019 in Imam Hossein Hospital in Tehran, the data is needed to collect this research. Algorithms used to classify patients include support vector machines, k nearest neighbor, decision tree, logistic regression and random forest that was reported based on the precision, accuracy, sensitivity, specificity, and roc under the curve.

The results of this study showed, identified 26 factors affecting specific data and pre-processing of data. Among five of the algorithms used in the study, logistic regression algorithm based on the level of roc curve (0.76), accuracy percentage (75.62),precision (68.39),sensitivity (38.65) and specificity (94.53) had better performance in predicting mortality compared to other techniques of study. The variables of Glucose and Partial Thromboplastin time were the most significant effects on mortality based on the logistic regression model.

Data analysis in intensive care unit patients can be an appropriate and practical tool for predicting mortality and its related factors, but according to the quality of data, results are different. And the results extracted from logistic regression can be used as a model to predict the status of mortality in the intensive care unit.

Various prediction models have been developed aiming to estimate risk-adjusted mortality, morbidity and length of intensive care unit stay following cardiac surgeries. The European system for cardiac operative risk evaluation II (EuroSCORE II) is a prediction model which maps 18 predictors to a 30-day post-operative risk of death. The objective of this study was to evaluate the performance of the EuroSCORE II risk-analysis predictions among patients who underwent heart surgeries.
A prospective cross-sectional study was conducted to collect required variables for all consecutive patients underwent heart surgeries in Emam Reza hospital, Mashhad, Iran, from March 2014 to March 2015. Once the patient was hospitalized a cardiologist or a general physician evaluated pre- peri- and post-operative state to fill out the pre-designed structural paper form. Comparing the observed and expected mortality, the sensitivity, specificity, area under the receiver operating characteristic curve (AUC) and finally the discrimination power of the model for our patients were calculated and reported. The model value was calculated using the online tool.
A total of 1337 patients (60% males) were included, the observed mortality rate was 3.2%. Although the overall performance was acceptable, the model showed poor discriminatory power (AUC=0.667, CI 95%: 0.648-0.685) and accuracy with sensitivity=61.88% and specificity=66.23%.
Our single-center study, based on consecutive patients who underwent cardiac surgery showed that EuroSCORE II demonstrated a moderate statistical overall performance with poor discrimination and calibration measures remain as concerning issues regarding 30-day post-operative mortality prediction after adult cardiac surgery. Poor performance measures for this system show the need for reformulating this risk stratification tool aiming to improve post cardiac surgery outcome predictions in Iran.

Psychological anomalies have a significant prevalence in the world, especially in developing countries, such as Iran, and require the development of various interventions for diagnosis, treatment, monitoring and care services. Regarding the nature of psychological abnormalities monitoring, and especially the self-inventory testing method, it is recommended to use telemedicine capabilities for remote monitoring of patients with these abnormalities. The purpose of this study was to design and model a self-reported psychiatric system for depression.
In this study, firstly, by reviewing the texts and observing similar systems, interviewing the psychiatric specialists of Shahid Beheshti Hospital of Kerman, nurses and patients of this center, and presenting and analyzing the questionnaire, the requirements of such a system were elicited and presented in the form of diagrams such as Class and business use Case in UML2.0. In order to provide a general view of prospective view, context diagram was presented. Scenario-based and simulation strategies were used to evaluate the model.
By reviewing 33 similar studies and 45 websites related to similar systems, seven subsets of account, education, counseling, treatment, monitoring, financial affairs and communications were defined and listed with the full definition of relevant items, and communication types were presented by UML diagrams. Also, in the first step of the evaluation of 20 scenarios, the final scenario was approved by experts. Simulation indicators represent the high efficiency of the system.
The results of the evaluation of the proposed system show that the proposed system is satisfactory in terms of qualitative criteria and covers all expected functional requirements.

Today, the use of information technology in education, is one of the most important goals of many educational institutions around the world and Iran, due to its advantages including the possibility to be used at any time and place. ACECR branch of Tehran University of Medical Sciences, as one of the organizers of specialized training courses, has already organized some e-learning courses. In this research, we tried to examine the role of each of the effective factors in e-learning in the success rate of this type of education.
In this descriptive study, the effect of six factors, from the perspective of the four groups of participants, professors and ACECR (Iranian academic center for education, culture and research) staff and managers, was measured in 2010-2011, through using a researcher-made questionnaire including 39 questions on Likert scale. The questionnaire was sent via e-mail to 420 people and a total of 188 people participated in this study. The collected data were analyzed through SPSS software and using descriptive statistics and inferential statistics.
The results indicated the importance of all studied factors with the order of management, educational content, facilities, teachers, rules and learner.
Although the difference between the effective factors is negligible, the extra effort of authorities to remove the shortcomings and weaknesses of the three main and effective factors of management, educational content and facilities needed for the success of e-earning is required

Defining the metastasis processes and what are the most effecting factors on improve the survival of patients and hopefully treating them. We aim to investigate and defining the factors predict breast cancer metastasis using data mining techniques. Data mining is the technique and tool of knowledge discovery from the big data. Nowadays data mining is spreading rapidly in several areas of research and business. In medicine, diagnosis of diseases is one of the fruitful and highly spreading filed of data mining.
There were 2025 usable records in ACECR Breast Disease Center’s data base after data preparation. We try to uncover the patterns that would help the prediction of metastasis factors using CHAID and Artificial Neural Network.
After implementing mentioned algorithms, the tumor stage, surgery type and pathology results, the most important variables in metastasis prediction.
Comparing the algorithms execution revealed that, Artificial Neural Network, CHAID are convenient prediction models for breast cancer metastasis.

There is no need to explain the importance of collection, recording and analyzing the information of disease in any health organization. In this regard, systematic design of standard data sets can be helpful to record uniform and consistent information. It can create interoperability between health care systems. The main purpose of this study was design the core dataset to record colorectal cancer information in Iran.
For the design of the colorectal cancer core data set, a combination of literature review and expert consensus were used. In the first phase, the draft of the data set was designed based on colorectal cancer literature review and comparative studies. Then, in the second phase, this data set was evaluated by experts from different discipline such as medical informatics, oncology and surgery. Their comments and opinion were taken. In the third phase refined data set, was evaluated again by experts and eventually data set was proposed.
In first phase, based on the literature review, a draft set of 85 data elements was designed. In the second phase this data set was evaluated by experts and supplementary information was offered by professionals in subgroups especially in treatment part. In this phase the number of elements totally were arrived to 93 numbers. In the third phase, evaluation was conducted by experts and finally this dataset was designed in five main parts including: demographic information, diagnostic information, treatment information, clinical status assessment information, and clinical trial information.
In this study the comprehensive core data set of colorectal cancer was designed. This dataset in the field of collecting colorectal cancer information can be useful through facilitating exchange of health information. Designing such data set for similar disease can help providers to collect standard data from patients and can accelerate retrieval from storage systems.

Breast cancer is one of the most common cancers, and also it is the most common type of malignancy in Iranian women that has been growing in recent years. The risk of recurrence is usual in patient. Many factors may increase or decrease the recurrence rate. Data mining methods have been used to diagnose or predict cancer and one of the most application of data mining approaches is prediction of breast cancer recurrence This is a retrospective study. Collected data on 809 patients with breast cancer with 18 fields for each patient were used. Due to excessive missing data only about 665 cases have been used. Since the number of fields in the remaining records with null values have been observed, as a preprocessing and data preparation phases, these values have been estimated by the EM algorithm and using SPSS.v20 software. In this study, a model for prognosis of breast cancer recurrence among patients using J48 tree has been developed. The specificity and sensitivity of the developed model are 53% and 85%, respectively. Moreover, only 14% of patients who have relapsed are known as false negative with developed model. Creating a predictive model with appropriate specificity and sensitivity can warn patients about recurrence and timely preventive measures to prevent progression of the cancer. The False Negative rate is very important in medical prediction models that can make serious results/consequences. In present study this rate is about 14% that seems reasonable amount in term of modeling.

Owing to t he critical importance of emergency department activities in saving lives، information systems of this department should be free of fault to prevent the incidence of errors. Usability evaluation methods are used to assess this quality measure. Heuristic evaluation is one of the methods which identifies usability problems with minimum amount of time، cost and resources. The objective of this research was to evaluate the usability of emergency department admission subsystem of health information systems in Mashhad University of Medical Sciences. Using Nielsen’s 10 usability principles، three trained evaluators، independent of each other، evaluated the corresponding subsystem and determined the severity of identified problems. All the problems، identified by independent evaluators، were collected in a single list and the mean severity of each problem was calculated. A total of 163 usability problems were identified. The lowest mismatch with usability principles was related to “error prevention” (3 %) and the highest was related to “consistency and standards” (27 %). The average severity of problems varied from 2. 3 (minor problem) concerning “system''s visibility” to 2. 9 (major problem) concerning “help users recognize، diagnose، and recover from errors”. Heuristic Evaluation can be used to identify a high number of usability problems related to health information applications in health care systems. If remain unsolved، these problems may waste users’ and patients’ time، increase errors، reduce data quality، and in general، threaten patient’s safety.