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Abstract Introduction Prolonged Length of Stay (LOS) in ED (Emergency Department) has been associated with poor clinical outcomes. Prediction of ED LOS may help optimize resource utilization, clinical management, and benchmarking. This study aims to systematically review models for predicting ED LOS and to assess the reporting and methodological quality about these models. Methods The online database PubMed, Scopus, and Web of Science (10 Sep 2023) was searched for English language articles that reported prediction models of LOS in ED. Identified titles and abstracts were independently screened by two reviewers. All original papers describing either development (with or without internal validation) or external validation of a prediction model for LOS in ED were included. Results Of 12,193 uniquely identified articles, 34 studies were included (29 describe the development of new models and five describe the validation of existing models). Different statistical and machine learning methods were applied to the papers. On the 39-point reporting score and 11-point methodological quality score, the highest reporting scores for development and validation studies were 39 and 8, respectively. Conclusion Various studies on prediction models for ED LOS were published but they are fairly heterogeneous and suffer from methodological and reporting issues. Model development studies were associated with a poor to a fair level of methodological quality in terms of the predictor selection approach, the sample size, reproducibility of the results, missing imputation technique, and avoiding dichotomizing continuous variables. Moreover, it is recommended that future investigators use the confirmed checklist to improve the quality of reporting.

In the health systems field, a significant amount of data is generated and collected, which can be used to extract valuable information using machine learning techniques. The Intensive Care Unit (ICU) is one of the most critical parts of hospitals. This study investigates the ICU of cardiac surgery, where one of the critical topics is the ventilation of patients. In other words, patients’ breathing using ventilator is a crucial need in the ICU, which has become a significant issue due to the limited number of ventilators in the hospital. One of the concerns of treatment staff is determining which patients require ventilators more urgently than others. Therefore, we predicted the patients’ need for ventilators in the ICU of cardiac surgery. Among the innovative aspects of this study, we can mention the use of a two-stage hybrid model. In the first stage of modeling, we used machine learning and deep learning models to implement 15 classification models and make predictions. In the second stage, patients were prioritized using a combination of clustering and medical expert opinion. The results showed us that the Artificial Neural Network (ANN) performed better than other models in the first stage with remarkable accuracy (80%) and specificity (95%). So, the ANN predicted that out of 328 incoming patients, 69 of them needed ventilators. Then, in the second stage, these 69 patients were divided into 6 groups by clustering and prioritized based on the expert’s opinion that determined the acuteness status of the patients to assign limited ventilators to critically patients first.

Abstract Background Menopause is a period of women’s life that has the especial physical, psychological and social challenges. So provision of an effective, practical and affordable way for meeting women’s related needs is important. In addition, women should be able to incorporate such programs into their daily work. Considering the dearth of suitable services in this regard, this study will be conducted with the aim of designing, validating and evaluating the “Healthy Menopause” expert system on the management of menopausal symptoms. Methods/design A mixed methods exploratory design will be used to conduct this study in 3 phases. The first phase is a qualitative conventional content analysis study with purposes of exploring the women’s experience of menopausal symptoms and extracting their needs, and collecting data about their expectations from a healthy menopause expert system.. The purposive sampling (In his phase data will be gathered through interviewing menopaused women aged 40 to 60 years old and other persons that have rich information in this regard and will be continued until data saturation. The second phase includes designing a healthy menopause expert system in this stage, the needs will be extracted from the qualitative findings along with a comprehensive literature review. The extracted needs will be again confirmed by the participants. Then, through a participatory approach (Participatory Design) using nominal group or Delphi technique the experts’ opinion about the priority needs of menopaused women and related solutions will be explored based on the categories of identified needs. Such findings will be used to design a healthy menopause expert system at this stage. The third phase of study is a quantitative research in which the evaluation of the healthy menopause expert system will be done through a randomized controlled clinical trial with the aim of determining the effect of the healthy menopause expert system on the management of menopause symptoms by menopausal women themselves. Discussion This is the first study that uses a mixed method approach for designing, validating and evaluating of the expert system “Healthy Menopause”. This study will fill the research gap in the field of improving menopausal symptoms and designing a healthy menopause expert system based on the needs of the large group of menopause women. We hope that by applying this expert system, the menopausal women be empowered to management and improving their health with an easy and affordable manner.

Background: Breast cancer is considered one of the most common cancers in women caused by various clinical, lifestyle, social, and economic factors. Machine learning has the potential to predict breast cancer based on features hidden in data. Objective: This study aimed to predict breast cancer using different machine-learning approaches applying demographic, laboratory, and mammographic data.Material and Methods: In this analytical study, the database, including 5,178 independent records, 25% of which belonged to breast cancer patients with 24 attributes in each record was obtained from Motamed cancer institute (ACECR), Tehran, Iran. The database contained 5,178 independent records, 25% of which belonged to breast cancer patients containing 24 attributes in each record. The random forest (RF), neural network (MLP), gradient boosting trees (GBT), and genetic algorithms (GA) were used in this study. Models were initially trained with demographic and laboratory features (20 features). The models were then trained with all demographic, laboratory, and mammographic features (24 features) to measure the effectiveness of mammography features in predicting breast cancer. Results: RF presented higher performance compared to other techniques (accuracy 80%, sensitivity 95%, specificity 80%, and the area under the curve (AUC) 0.56). Gradient boosting (AUC=0.59) showed a stronger performance compared to the neural network. Conclusion: Combining multiple risk factors in modeling for breast cancer prediction could help the early diagnosis of the disease with necessary care plans. Collection, storage, and management of different data and intelligent systems based on multiple factors for predicting breast cancer are effective in disease management.

Abstract Background and Aims Infection with Covid‐19 disease can lead to mortality in a short time. Early prediction of the mortality during an epidemic disease can save patients' lives through taking timely and necessary care interventions. Therefore, predicting the mortality of patients with Covid‐19 using machine learning techniques can be effective in reducing mortality rate in Covid‐19. The aim of this study is to compare four machine‐learning algorithm for predicting mortality in Covid‐19 disease. Methods The data of this study were collected from hospitalized patients with COVID‐19 in five hospitals settings in Tehran (Iran). Database contained 4120 records, about 25% of which belonged to patients who died due to Covid‐19. Each record contained 38 variables. Four machine‐learning techniques, including random forest (RF), regression logistic (RL), gradient boosting tree (GBT), and support vector machine (SVM) were used in modeling. Results GBT model presented higher performance compared to other models (accuracy 70%, sensitivity 77%, specificity 69%, and the ROC area under the curve 0.857). RF, RL, and SVM models with the ROC area under curve 0.836, 0.818, and 0.794 were in the second and third places. Conclusion Considering the combination of multiple influential factors affecting death Covid‐19 can help in early prediction and providing a better care plan. In addition, using different modeling on data can be useful for physician in providing appropriate care.

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

Introduction: 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. Method: 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. Results: 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. Conclusion: The optimal proposed prediction system can be used as a clinical decision support system to assist medical practitioners in the healthcare practice

Due to the development of biomedical equipment and healthcare level, especially in the Intensive Care Unit (ICU), a considerable amount of data has been collected for analysis. Mortality prediction in the ICUs is considered as one of the most important topics in the healthcare data analysis section. A precise prediction of the mortality risk for patients in ICU could provide us with valuable information about patients' lives and reduce costs at the earliest possible stage. This paper aims to introduce a new hybrid predictive model using the Genetic Algorithm as a feature selection method and a new ensemble classifier based on the combination of Stacking and Boosting ensemble methods to create an early mortality prediction model on a highly imbalanced dataset. The SVM-SMOTE method is used to solve the imbalanced data problem. This paper compares the new model with various machine learning models to validate the efficiency of the introduced model. The achieved results using the shuffle 5-fold cross-validation and random hold-out methods indicate that the new hybrid model has the best performance among other classifiers. Additionally, the Friedman test is applied as a statistical significance test to examine the differences between classifiers. The results of the statistical analysis prove that the proposed model is more effective than other classifiers. Furthermore, the proposed model is compared to APACHE and SAPS scoring systems and is benchmarked against state-of-the-art predictive models applied to the MIMIC dataset for experimental validation and achieved promising results as it outperformed the state-of-the-art models.

Introduction: The new coronavirus (COVID-19) has posed many new challenges to the health care and the timing of surgical care. At the beginning of the pandemic many guidelines recommended postponing elective surgical procedures to reallocate resources. As regards, delay in cancer treatment could be effective on cancer progression. The aim of this systematic review was to outline a guideline for preoperative screening before cancer surgeries and protecting health care workers during the pandemic.Materials and Methods: This study was conducted through a search in electronic databases up to August 2020. PubMed, EMBASE, Web of Science, Scopus, Science Direct, and Google Scholar databases were searched without time limitation. The keywords were a combination of preoperative, cancer surgery, COVID-19, and their synonyms.Results: The most commonly used ways to triage preoperatively were telephone pre-assessment for suspicious symptoms and history of contact or travel, 14-day self-isolation, in- hospital queries at admission, temperature monitoring, and isolation in a single room COVID-free ward or physical distancing. Reverse transcription-polymerase chain reaction (RT-PCR) test 24–72 h before operation was recommended commonly, except in inaccessible centers, but non-contrast chest-CT scan is not routinely advised for elective surgeries to salvage medical resources. Recommended personal protective equipment (PPE) for staffs were wearing N95 mask in addition to gown, gloves, eye protection in aerosol-generating procedures (AGPs), and wearing gloves, hats, and disposable surgical masks, practice distancing, and hand hygiene for all staffs. Meanwhile team separation of hospital staffs caring for COVID-19 patients, segregated areas for COVID-19 clean and contact, restriction of visitors and family members, and personal distancing are mostly recommended.Conclusion: We hope this review would be a guidance for triage, preoperative testing, and summarizing safety principles during COVID-19 pandemic alongside with surgical reintegration.

Introduction: Breast cancer rates have been increasing worldwide, particularly among young women, suggesting important interactions between genes and health behaviors. At the same time, mobile technology, including smartphones applications (apps), has emerged as a new tool for delivering healthcare and health-related services. In 2019, there were nearly 670 publicly available breast cancer apps designed to provide disease and treatment information, to manage disease, and raise overall awareness. Material and Methods: In order to conduct a review, the Medline, Scopus and PubMed databases were searched with the keywords "mobile health", "mobile health in electronics health", "breast cancer and electronic health"," mobile health and breast cancer"," mobile health and breast cancer qualify life" and their equivalent. Out of the 60 articles found, after the depth of the criteria Inclusion in the study, 16 articles remained, which were reviewed and given using PRISMA 2020 checklist. SPSS software v.22 was used for description analysis. Results: A total of 16 articles met the inclusion criteria and were included in this review. All studies have determined the positive impact of applications on cancer detection and clinical health outcomes. In addition, more than half of mobile applications have multiple functions, such as providing information, planning and education. Furthermore, most studies examining patient satisfaction and quality improvement have shown that users of healthcare applications are significantly more satisfied with life, leading to better quality. Conclusion: The evidence of the studies which are included in this systematic review is currently limited, but it suggests that the mobile apps might be an acceptable information source for women with breast cancer and lead to improved patient well-being.

Introduction: Early detection breast cancer Causes it most curable cancer in among other types of cancer, early detection and accurate examination for breast cancer ensures an extended survival rate of the patients. Risk factors are an important parameter in breast cancer has an important effect on breast cancer. Data mining techniques have a growing reputation in the medical field because of high predictive capability and useful classification. These methods can help practitioners to develop tools that allow detecting the early stages of breast cancer. Material and Methods: The database used in this paper is provided by Motamed Cancer Institute, ACECR Tehran, Iran. It contains of 7834 records of breast cancer patients clinical and risk factors data. There were 4008 patients (52.4%) with breast cancers (malignant) and the remaining 3617 patients (47.6%) without breast cancers (benign). Support vector machine, multi-layer perceptron, decision tree, K nearest neighbor, random forest, naïve Bayesian models were developed using 20 fields (risk factor) of the database because database feature was restrictions. Used 10-fold crossover for models evaluate. Ultimately, the comparison of the models was made based on sensitivity, specificity and accuracy indicators. Results: Naïve Bayesian and artificial neural network are better models for the prediction of breast cancer risks. Naïve Bayesian had accuracy of 93%, specificity of 93.32%, sensitivity of 95056%, ROC of 0.95 and artificial neural network had accuracy of 93.23%, specificity of 91.98%, sensitivity of 92.69%, and ROC of 0.8. Conclusion: Strangely the different artificial intelligent calculations utilized in this examination yielded close precision subsequently these techniques could be utilized as option prescient instruments in the bosom malignancy risk considers. The significant prognostic components affecting risk pace of bosom disease distinguished in this investigation, which were approved by risk, are helpful and could be converted into choice help devices in the clinical area.

Background: 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. Methods: 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. Results: 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%. Conclusion: 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.

Introduction: 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. Methods: 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. Results: 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. Conclusion: 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.

Objective: To define a core dataset for ICU Patients Outcome Prediction in Iran. This core data set will lead us to design ICU outcome prediction models with the most effective parameters. Methods: A combination of literature review, national survey and expert consensus meetings were used. First, a literature review was performed by a general search in PubMed to find the most appropriate models for intensive care mortality prediction and their parameters. Secondly, in a national survey, experts from a couple of medical centers in all parts of Iran were asked to comment on a list of items retrieved from the earlier literature review study. In the next step, a multi-disciplinary committee of experts was installed. In 4 meetings each data item was examined separately and included/excluded by committee consensus. Results: The combination of the literature review findings and experts’ consensus resulted in a draft dataset including 26 data items. 92% percent of data items in the draft dataset were retrieved from the literature study and the others were suggested by the experts. The final dataset of 24 data items covers patient history and physical examination, chemistry, vital signs, oxygenations and some more specific parameters. Conclusions: This dataset was designed to develop a nationwide prognostic model for predicting ICU mortality and length of stay. This dataset opens the door for creating standardized approaches in data collection in the Iranian intensive care unit estimation of resource utility.

Introduction: 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. Methods: 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. Results: The results indicated the importance of all studied factors with the order of management, educational content, facilities, teachers, rules and learner. Conclusion: 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

Background: 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. Methods: 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. Results: 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. Conclusion: 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.

The pandemic of COVID-19 is the second one in the 21st century after H1N1 Swine Flu in 2009-2010, which infected more than 4.9 million and caused more than 327000 death as of 23 May 2020 worldwide.1 This pandemic has overwhelmed the health systems, specially hospitals, and the need for ICU beds and ventilators increased while many of patients with mild symptom of COVID-19 could be recovered at home.2 In addition, early discharge of hospitalized patient and referring for Home Health Care (HHC) would be a suitable strategy for decreasing the overload of patient in hospitals and its consequences such as nosocomial infections. On the other side, many patients who receive HHC for other conditions (usually old people with chronic disease) need special attention during the pandemic for ensuring continuation of care and prevention of COVID-19 threat. The first patient in Iran was officially reported on February 19, 2020 in Qom city. According to World Health Organization about 129 341 Iranians were infected by Novel Corona Virus and 7249 died till 23 May 2020.1 As the nurses are frontline health worker who provide services in hospitals as well as in home settings, Deputy for Nursing of MOHME published the guideline for Home care for COVID-19 patient and about 405 institute for Home Care services were active to care of patient in their homes.3 Regardless of the availability of detailed information about hospitalized COVID-19 patients by means of integrated Hospital Information Systems (HIS), there is no information about the number, characteristics and health outcomes of people who receive professional HHC in Iran during this pandemic. It is not only the problem of Iran’s Health system, but European countries have also encounter with lack of home care data.4 The history of HHC, as an industry in Iran, dates back to 1999 when the first bylaw for HHC was ratified by Health Minister. In recent years, Act on the Sixth Five-Year Economic, Cultural and Social Development Plan of Iran for 2017-2022 enforced the MOHME to expand the HHC in community and insurance coverage for HHC is on the High Council of Health Insurance’s agenda. Thus, nowadays home health care is becoming integrated in the health system of Iran more than ever. One of the problems of HHC during the COVID-19 in Iran (at least in the beginning of epidemic) was lack of appropriate and adequate Personal Protective Equipment (PPE) for the staff who worked in HHC settings directly with patients and families and raised fear of the risk of cross-infections by the staff and patients. The lack of information and data about home care in Iran’s healthcare system, lack of adequate and accurate monitoring, and inability of supervising have been among the challenges of HHC in Iran,5 which during COVID-19 epidemic became more challenging. The lack of data has weakened the ability of policymakers for evidence-based and timely decisions to combat the disease. Using Information Technology (IT) in HHC has many advantages such as gathering and sharing information, supervising and providing telenursing. The HHC teams and patients as well as policymakers can benefit from IT implementation in HHC. This issue is especially a priority in the disasters like COVID-19 pandemic when the health system needs integrated and real-time data. On the other hand, to minimize the contacts between nurses and patients aiming to decrease the cross-contamination, Telenursing can be used for frequent contact between the nurses and patients to manage the condition by telephone or video call without direct contact. According to lessons learned from COVID-19 pandemic, using IT in Home Health Care, especially implementation of Home Health Care Information System and its integration into national Electronic Health Record (EHR) and using Telenursing are among the important factors for developing the role of HHC in the health system of Iran; this could help to better manage the outbreaks. It is not a simple policy to implement and needs a national determination and plan with multi-sectoral cooperation.

Objective: Bone is the most common site of metastasis in breast cancer. Prognostic factors for predicting bone metastases in breast cancer are controversial yet. In this study, we investigated clinical factors associated with secondary bone metastasis of breast cancer. Methods: In total, 1690 patients with breast cancer recorded between 2002 and 2012 in Motamed Cancer Institute, Tehran, Iran entered in the retrospective study. We studied age, menopausal status, histologic type, tumor size, number of cancerous axillary lymph nodes, serum concentrations of alkaline phosphatase (ALP), carcinogenicity antigen (CEA), cancer antigen (CA)-153, and hemoglobin (HB) in 2 groups with bone metastases (n = 123) and without it, respectively. We applied logistic regression to identify bone metastasis prognostic factors in breast cancer patients and calculated the cut-off value, sensitivity, and characteristics of independent prognostic factors using receiver operating characteristic (ROC) curve analysis. Results: Menopause, larger tumor size, and the greater number of cancerous axillary lymph nodes increased the chance of bone metastases significantly ( P .05). Logistic regression showed that age (odds ratio (OR) = 1.021), menopausal status (OR = 1.854), number of cancerous axillary lymph nodes (OR = 1.065), a tumor size between 2 and 5 cm diameter (OR = 2.002) and more than 5 cm diameter (OR = 4.009), and ALP (OR = 1.005) are independent prognostic factors associated with bone metastases. The ROC curve showed that the abovementioned factors have comparable predictive accuracy for bone metastases. Conclusions: Age, menopausal status, number of axillary lymph node metastases, tumor size, and ALP were identified as prognostic factors for bone metastasis in patients with breast cancer. So patients with these characteristics should be monitored more precisely with regular follow-ups.

Background: Diagnosing breast cancer at an early stage can have a great impact on cancer mortality. One of the fundamental problems in cancer treatment is the lack of a proper method for early detection, which may lead to diagnostic errors. Using data analysis techniques can significantly help in early diagnosis of the disease. The purpose of this study was to evaluate and compare the efficacy of two data mining techniques, i.e., multilayer neural network and C4.5, in early diagnosis of breast cancer. Methods: A data set from Motamed Cancer Institute's breast cancer research clinic, Tehran, containing 2860 records related to breast cancer risk factors were used. Of the records, 1141 (40%) were related to malignant changes and breast cancer and 1719 (60%) to benign tumors. The data set was analyzed using perceptron neural network and decision tree algorithms, and was split into two a training data set (70%) and a testing data set (30%) using Rapid Miner 5.2. Results: For neural networks, accuracy was 80.52%, precision 88.91%, and sensitivity 90.88%; and for decision tree, accuracy was 80.98%, precision 80.97%, and sensitivity 89.32%. Results indicated that both algorithms have acceptable capabilities for analyzing breast cancer data. Conclusion: Although both models provided good results, neural network showed more reliable diagnosis for positive cases. Data set type and analysis method affect results. On the other hand, information about more powerful risk factors of breast cancer, such as genetic mutations, can provide models with high coverage.

Introduction: 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 Method: 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. Results: 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. Conclusion: 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.

Abstract Introduction: 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 concentrating on accurate stratification of candidate patients for cardiac surgery. Objective: The objective of this study was to determine the performance of the EuroSCORE II risk-analysis predictions among patients who underwent heart surgeries in one area of Iran. Methods: A retrospective cohort study was conducted to collect the required variables for all consecutive patients who underwent heart surgeries at Emam Reza hospital, Northeast Iran between 2014 and 2015. Univariate and multivariate analysis were performed to identify covariates which significantly contribute to higher EuroSCORE II in our population. External validation was performed by comparing the real and expected mortality using area under the receiver operating characteristic curve (AUC) for discrimination assessment. Also, Brier Score and Hosmer-Lemeshow goodness-of-fit test were used to show the overall performance and calibration level, respectively. Results: Two thousand five hundred eight one (59.6% males) were included. The observed mortality rate was 3.3%, but EuroSCORE II had a prediction of 4.7%. Although the overall performance was acceptable (Brier score=0.047), the model showed poor discriminatory power by AUC=0.667 (sensitivity=61.90, and specificity=66.24) and calibration (Hosmer-Lemeshow test, P