Effect of vitamin D supplementation on inflammatory markers and total antioxidant capacity in breast cancer women using a machine learning technique.

Aim: This study aimed to establish a learning system using an artificial neural network (ANN) to predict the effects of vitamin D supplementation on the serum levels of vitamin D, inflammatory factors, and total antioxidant capacity (TAC) in women with breast cancer. Methods: The data set of the current project was created from women with breast cancer who were referred to the Shafa State Hospital of Patients with Cancers in Ahvaz city, Iran. Modeling was implemented using the data set at the serum levels of vitamin D, tumor necrosis factor-α (TNF-α), transforming growth factor ß (TGF-ß), and TAC, before and after vitamin D3 supplement therapy. A prediction ANN model was designed to detect the effects of vitamin D3 supplementation on the serum level changes of vitamin D, inflammatory factors and TAC. Results: The results showed that the ANN model could predict the effect of vitamin D3 supplementation on the serum level changes of vitamin D, TNF-α, TGF-ß1, and TAC with an accuracy average of 85%, 40%, 89.5%, and 88.1%, respectively. Conclusions: According to the findings of the study, the ANN method could accurately predict the effect of vitamin D3 supplementation on the serum levels of vitamin D, TNF-α, TGF-ß1, and TAC. The results showed that the proposed ANN method can help specialists to improve the treatment process more confidently in terms of time and accuracy of predicting the influence of vitamin D supplementation on the factors affecting the progression of breast cancer (https://www.irct.ir/ identifier: IRCT2015090623924N1).

Determining the effective factors in predicting diet adherence using an intelligent model.

Adhering to a healthy diet plays an essential role in preventing many nutrition-related diseases, such as obesity, diabetes, high blood pressure, and other cardiovascular diseases. This study aimed to predict adherence to the prescribed diets using a hybrid model of artificial neural networks (ANNs) and the genetic algorithm (GA). In this study, 26 factors affecting diet adherence were modeled using ANN and GA(ANGA). A dataset of 1528 patients, including 1116 females and 412 males, referred to a private clinic was applied. SPSS Ver.25 and MATLAB toolbox 2017 were employed to make the model and analyze the data. The results showed that the accuracy of the proposed ANN and ANGA models for predicting diet adherence was 93.22% and 93.51%, respectively. Also, the Pearson coefficient showed a significant relationship among the factors. The developed model showed the proper performance for predicting adherence to the diet. Moreover, the most effective factors were selected using GA. Some important factors that affect diet adherence include the duration of the marriage, the reason for referring to the clinic, weight, body mass index (BMI), weight satisfaction, lunch and dinner times, and sleep time. Therefore, applying the proposed model can help dietitians identify people who need more support to adhere to the diet.

Machine Learning Models to Predict In-Hospital Mortality among Inpatients with COVID-19: Underestimation and Overestimation Bias Analysis in Subgroup Populations.

Prediction of the death among COVID-19 patients can help healthcare providers manage the patients better. We aimed to develop machine learning models to predict in-hospital death among these patients. We developed different models using different feature sets and datasets developed using the data balancing method. We used demographic and clinical data from a multicenter COVID-19 registry. We extracted 10,657 records for confirmed patients with PCR or CT scans, who were hospitalized at least for 24 hours at the end of March 2021. The death rate was 16.06%. Generally, models with 60 and 40 features performed better. Among the 240 models, the C5 models with 60 and 40 features performed well. The C5 model with 60 features outperformed the rest based on all evaluation metrics; however, in external validation, C5 with 32 features performed better. This model had high accuracy (91.18%), F-score (0.916), Area under the Curve (0.96), sensitivity (94.2%), and specificity (88%). The model suggested in this study uses simple and available data and can be applied to predict death among COVID-19 patients. Furthermore, we concluded that machine learning models may perform differently in different subpopulations in terms of gender and age groups.

A machine learning technology to improve the risk of non-invasive prenatal tests.

BACKGROUND: Timely and accurate diagnosis of genetic diseases can lead to proper action and prevention of irreparable events. OBJECTIVE: In this work we propose an integrated genetic-neural network (GNN) to improve the prediction risk of trisomy diseases including Down's syndrome (T21), Edwards' syndrome (T18) and Patau's Syndrome (T13). METHODS: A dataset including 561 pregnant were created. In this integrated model, the structure and input parameters of the proposed multilayer feedforward network (MFN) were optimized. RESULTS: The results of execution of the GNN on the testing dataset showed that the developed model can be accurately classify the anomalies from healthy fetus with 97.58% accuracy rate, and 99.44% and 85.65% sensitivity, and specificity, respectively. In the proposed GNN model, the Levenberg Merquident (LM) algorithm, the Radial Basis (Radbas) function from various types of functions were selected by the proposed GA. Moreover, maternal age, Nuchal Translucency (NT), Crown-rump length (CRL), Pregnancy-associated plasma protein A (PAPP-A) were selected by the proposed GA as the most effective factors for classifying the healthfetuses from the cases with fetal disorders. CONCLUSION: The proposed computerized model increases the diagnostic performance of the physicians especially in the accurate detection of healthy fetus with non - invasive and low - cost treatments.

The role of ambient parameters on transmission rates of the COVID-19 outbreak: A machine learning model.

BACKGROUND: In recent years the relationship between ambient air temperature and the prevalence of viral infection has been under investigation. OBJECTIVE: The study was aimed at providing the statistical and machine learning-based analysis to investigate the influence of climatic factors on frequency of COVID-19 confirmed cases in Iran. METHOD: The data of confirmed cases of COVID-19 and some climatic factors related to 31 provinces of Iran between 04/03/2020 and 05/05/2020 was gathered from official resources. In order to investigate the important climatic factors on the frequency of confirmed cases of COVID-19 in all studied cities, a model based on an artificial neural network (ANN) was developed. RESULTS: The proposed ANN model showed accuracy rates of 87.25%and 86.4%in the training and testing stage, respectively, for classification of COVID-19 confirmed cases. The results showed that in the city of Ahvaz, despite the increase in temperature, the coefficient of determination R2 has been increasing. CONCLUSION: This study clearly showed that, with increasing outdoor temperature, the use of air conditioning systems to set a comfort zone temperature is unavoidable. Thus, the number of positive cases of COVID-19 increases. Also, this study shows the role of closed-air cycle condition in the indoor environment of tropical cities.

Regional COVID-19 registry in Khuzestan, Iran: A study protocol and lessons learned from a pilot implementation.

Disease registry systems provide a strong information infrastructure for decision-making and research. The purpose of this study is to describe the implementation method and protocol of the COVID-19 registry in Khuzestan province, Iran. We established a steering committee and formulated the purposes of the registry. Then, based on reviewing the literature, and expert panels, the minimum data set, the data collection forms and the web-based software were developed. Data collection is done retrospectively through Hospital Information Systems, Medical Care Monitoring Center system (MCMC), Management of Communicable Disease Prevention and Control system (MCDPC) as well as, patients' records. For prospective data collection, the data collection forms are compiled with patients' medical records by the medical staff and are then entered into the registry system. We collect patients' administrative and demographic data, history and physical examinations, test and imaging results, disease progression, treatment, outcomes, and follow-ups of the confirmed and suspected inpatients and outpatients. From April 20 to December 5, 2020, the data of 4,812 confirmed cases and 7,113 suspected cases were collected from two COVID-19 referral hospitals. Based on our experience, recording information along with providing care for patients and putting patients' data registration in the medical staff's routine, structuring data, having a flexible technical team and rapid software development for multiple and continuous updates, automating data collection by connecting the registry to existing information systems and having different incentives, the registration process can be strengthened.

Neural Network-Based Clinical Prediction System for Identifying the Clinical Effects of Saffron (Crocus sativus L) Supplement Therapy on Allergic Asthma: Model Evaluation Study.

BACKGROUND: Asthma is commonly associated with chronic airway inflammation and is the underlying cause of over a million deaths each year. Crocus sativus L, commonly known as saffron, when used in the form of traditional medicines, has demonstrated anti-inflammatory effects which may be beneficial to individuals with asthma. OBJECTIVE: The objective of this study was to develop a clinical prediction system using an artificial neural network to detect the effects of C sativus L supplements on patients with allergic asthma. METHODS: A genetic algorithm-modified neural network predictor system was developed to detect the level of effectiveness of C sativus L using features extracted from the clinical, immunologic, hematologic, and demographic information of patients with asthma. The study included data from men (n=40) and women (n=40) individuals with mild or moderate allergic asthma from 18 to 65 years of age. The aim of the model was to estimate and predict the level of effect of C sativus L supplements on each asthma risk factor and to predict the level of alleviation in patients with asthma. A genetic algorithm was used to extract input features for the clinical prediction system to improve its predictive performance. Moreover, an optimization model was developed for the artificial neural network component that classifies the patients with asthma using C sativus L supplement therapy. RESULTS: The best overall performance of the clinical prediction system was an accuracy greater than 99% for training and testing data. The genetic algorithm-modified neural network predicted the level of effect with high accuracy for anti-heat shock protein (anti-HSP), high sensitivity C-reactive protein (hs-CRP), forced expiratory volume in the first second of expiration (FEV1), forced vital capacity (FVC), the ratio of FEV1/FVC, and forced expiratory flow (FEF25%-75%) for testing data (anti-HSP: 96.5%; hs-CRP: 98.9%; FEV1: 98.1%; FVC: 97.5%; FEV1/FVC ratio: 97%; and FEF25%-75%: 96.7%, respectively). CONCLUSIONS: The clinical prediction system developed in this study was effective in predicting the effect of C sativus L supplements on patients with allergic asthma. This clinical prediction system may help clinicians to identify early on which clinical factors in asthma will improve over the course of treatment and, in doing so, help clinicians to develop effective treatment plans for patients with asthma.

Face masks vs. COVID-19: a systematic review

The coronavirus disease (COVID-19) spread rapidly around the world. Two types of approaches have been applied to use of face masks as a tool to prevent the spread this disease in society. The aim of the systematic review was to assess the effectiveness of face masks against the novel coronavirus. A literature search was performed using different databases until April 30, 2020. Search terms were 'facemasks', 'novel coronavirus', and 'healthcare workers'. Five studies were included in the systematic review. A study stated that no difference between surgical and cotton masks. Also, two studies have emphasized the use of surgical masks or N95 respirators by medical staff, and two other studies emphasized the use of any type of face mask by general public. More studies in controlled contexts and studies of infections in healthcare and community places are needed for better definition of the effectiveness of face masks in preventing coronavirus.

La enfermedad por coronavirus (COVID-19) se propagó rápidamente por todo el mundo. Se han aplicado dos tipos de enfoques al uso de máscaras faciales como herramienta para prevenir la propagación de la enfermedad en la sociedad. El objetivo de la revisión sistemática fue evaluar la efectividad de las máscaras faciales contra el nuevo coronavirus. Se realizó una búsqueda bibliográfica utilizando diferentes bases de datos hasta el 30 de abril de 2020. Los términos de búsqueda fueron: 'máscaras faciales', 'nuevo coronavirus' y 'trabajadores de la salud'. Se incluyeron cinco estudios en la revisión sistemática. Un estudio indicó que no hay diferencia entre las máscaras quirúrgicas y las de algodón. Además, dos estudios han enfatizado el uso de máscaras quirúrgicas o respiradores N95 por parte del personal médico, y otros dos estudios enfatizaron el uso de cualquier tipo de mascarilla por parte del público en general. Se necesitan más estudios en contextos controlados y estudios de infecciones en el cuidado de la salud y en lugares comunitarios para una mejor aclaración de la efectividad de las mascarillas para prevenir el coronavirus.

A doença de coronavírus (COVID-19) se espalhou rapidamente por todo o mundo. Dois tipos de abordagens foram aplicados ao uso de máscaras faciais como uma ferramenta para impedir a propagação da doença na sociedade. O objetivo da revisão sistemática foi avaliar a eficácia das máscaras faciais contra o novo coronavírus. Uma pesquisa bibliográfica foi realizada usando diferentes bancos de dados até 30 de abril de 2020. Os termos de pesquisa foram: máscaras faciais ',' novo coronavírus 'e' profissionais de saúde '. Cinco estudos foram incluídos na revisão sistemática. Um estudo indicou que não há diferença entre máscaras cirúrgicas e máscaras de algodão. Além disso, dois estudos enfatizaram o uso de máscaras cirúrgicas ou respiradores N95 pelo pessoal médico e dois outros estudos enfatizaram o uso de qualquer tipo de máscara pelo público em geral. É necessário mais estudos em ambientes controlados e estudos de infecções nos serviços de saúde e na comunidade para esclarecer melhor a eficácia das máscaras na prevenção do coronavírus

Differential Diagnosis of Iron-Deficiency Anemia from ß-Thalassemia Trait Using an Intelligent Model in Comparison with Discriminant Indexes.

INTRODUCTION: Iron deficiency anemia (IDA) and ß-thalassemia trait (ß-TT) are the most common types of microcytic hypochromic anemias. The similarity and the nature of anemia-related symptoms pose a foremost challenge for discriminating between IDA and ß-TT. Currently, advances in technology have gave rise to computer-based decision-making systems. Therefore, advances in artificial intelligence have led to the emergence of intelligent systems and the development of tools that can assist physicians in the diagnosis and decision-making. AIM: The aim of the present study was to develop a neural network based model (Artificial Neural Network) for accurate and timely manner of differential diagnosis of IDA and ß-TT in comparison with traditional methods. METHODS: In this study, an artificial neural network (ANN) model as the first precise intelligent method was developed for differential diagnosis of IDA and ß-TT. Data set was retrieved from Complete Blood Count (CBC) test factors of 268 individuals referred to Padad private clinical laboratory at Ahvaz, Iran in 2018. ANN models with different topologies were developed and CBC indices were examined for diagnosis of IDA and ß-TT. The proposed model was simulated using MATLAB software package version 2018. The results showed the best network architecture based on the advanced multilayer algorithm (4 input factors, 70 neurons with acceptable sensitivity, specificity, and accuracy). Finally, the results obtained from ANN diagnostic model was compared to existing discriminating indexes. RESULT: The results of this model showed that the specificity, sensitivity, and accuracy of the proposed diagnostic system were 92.33%, 93.13%, and 92.5%, respectably; i.e. the model could diagnose frequent occurrence of IDA in patients with ß-TT. CONCLUSION: The results and evaluation of the developed model showed that the proposed neural network model has a proper accuracy and generalizability based on the initial factors of CBC testing compared to existing methods. This model can replace the high-cost methods and discriminating indices to distinguish IDA from ß-TT and assist in accurate and timely manner diagnosis.

Top Mobile Applications in Pediatrics and Children's Health: Assessment and Intelligent Analysis Tools for a Systematic Investigation.

The development of intelligent software in recent years has grown rapidly. Mobile health has become a field of interest as a tool for childcare, especially as a means for parents of children with diverse diseases and a resource to promote their health conditions. Current systematic review was conducted to survey the functionalities of available applications on the mobile platform to support pediatrics intelligent diagnosis and children healthcare. Results which met the inclusion criteria (such as patient monitoring, decision support, diagnosis support) were obtained, assessed and organised into a checklist. In this study, 379 potential apps were identified using the search feature in Apple App Store and Google Play Store. After careful consideration of the selected apps, only three (Google Play Store) and one (iTunes Store), fulfilled all the general inclusion criteria and special criteria, such as intelligence tools. The results showed that Artificial Intelligence (AI) was used minimally in diagnostic apps due to a limited amount of mobile hardware and software, such as the reliable programming of intelligent algorithms.

The Effects of Smartphone Applications on Patients Self-care with Hypertension: A Systematic Review Study.

INTRODUCTION: High blood pressure or hypertension is one of the chronic diseases causing other serious diseases and syndromes. Active involvement of the patient in the management of the disease is crucial in improving self-care and clinical outcomes. Mobile technology is nowadays used widely to improve the self-care process in people with chronic diseases such as hypertension. AIM: The objective of this study was to provide an overview of the existing research evaluating the impact of mobile applications on the self-care of patients with hypertension. METHODS: The Scopus and PubMed databases were investigated using a comprehensive search strategy from the beginning of 2010 to 2019. All controlled clinical trial studies as well as quasi-experimental studies used mobile as a device for improving the self-care and conducted on patients with hypertension were included in the study. The studies were reviewed by two independent individuals. RESULTS: Out of 1032 studies found, 6 studies were finally reviewed after applying the inclusion criteria. Out of 6 studies reviewed, three studies confirmed the effect of using mobile applications on lowering blood pressure. Other studies reported a decline in blood pressure, while statistically significant were not shown. CONCLUSION: The results showed that mobile apps have positive potential on improving the self-care behavior of patients with hypertension, but the evidences presenting their impact are varied. Different reports for efficiency of mobile phone apps for the self-care modification was due to diverse condition of studies for mobile intervention on the patients with hypertension.

A Computer Based Model in Comparison with Sonography Imaging to Diagnosis of Acute Appendicitis in Iran.

INTRODUCTION: Acute appendicitis overlaps with conditions of other diseases in terms of Symptoms and signs in the first hours of presentation. Ultrasound imaging and laboratory tests are usually used to decrease the diagnosis errors in the case of abdominal pain. However, same results may be happened using the mentioned examination tools for a string of diseases with abdominal pain. Moreover, those tests raise the medical costs for hospitals and patients. Clinical Decision Support Systems (CDSSs) can be used to assist the physicians to make the proper health care decisions particularly in the unreliable conditions. OBJECTIVES: To improve the decision making process by physicians in diagnosis of acute appendicitis, an optimizing model was developed. The main objective is to discover a diagnostic model using the minimum clinical factors available in the first hours of abdominal pain. METHODS: Fuzzy-rule based classifier is a known technique in the Decision Support Systems (DSSs). In this article thus the useful clinical factors were explored and the diagnosis knowledge was discovered using Honey Bee Reproduction Cycle (HRBC) algorithm in the Fuzzy-rule based system. In this model, the proposed algorithm created the Fuzzy rules as the diagnosis knowledge in an optimizing process. To evaluate the accuracy of the proposed model for diagnosing of appendicitis, a collection of data was gathered from abdominal patients who referred to the educational general hospitals in Ahvaz, Iran in 2014 to 2015 years. In this process, the proposed model was optimized first in a training phase using a training dataset, and then it was tested with the testing dataset. Then, the achieved results from the computer base model were compared with ultrasound imaging findings before surgery as well as other detection methods in the previous studies. RESULTS: The comparison results illustrated that the proposed hybrid classification model as a CDSS improves considerably the accuracy of acute appendicitis diagnosis. Experimental outcomes illustrated that the proposed algorithm improves considerably the optimization performance in the diagnostic problem with the accuracy rate of 89.9%. The mentioned rate was achieved while a limited range of factors as the input parameters were used in the hybrid model. CONCLUSION: The proposed differential diagnostic model can be used as a CDSS especially conditions in which access to costly equipment such as CT scans and Sonography tools are limited. The developed model improves the diagnosis time as well as the treatment costs for the patients with acute abdomen suspicious of acute appendicitis.