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Background and Aims: Data mining methods are effective and well‐known tools for developing predictive models and extracting useful information from various data of patients. The present study aimed to predict the severity of patients with COVID‐19 by applying the rule mining method using characteristics of medical images. Methods: This retrospective study has analyzed the radiological data from 104 COVID‐19 hospitalized patients diagnosed with COVID‐19 in a hospital in Iran. A data set containing 75 binary features was generated. Apriori method is utilized for association rule mining on this data set. Only rules with confidence equal to one were generated. The performance of rules is calculated by support, coverage, and lift indexes. Results: Ten rules were extracted with only X‐ray‐related features on cases referred to ICU. The Support and Coverage index of all of these rules was 0.087, and the Lift index of them was 1.58. Thirteen rules were extracted from only CT scan‐related features on cases referred to ICU. The CXR_Pleural effusion feature has appeared in all the rules. The CXR_Left upper zone feature appears in 9 rules out of 10. The Support and Coverage index of all rules was 0.15, and the Lift index of all rules was 1.63. the CT_Adjacent pleura thickening feature has appeared in all rules, and the CT_Right middle lobe appeared in 9 rules out of 13. Conclusion: This study could reveal the application and efficacy of CXR and CT scan imaging modalities in predicting ICU admission to a major COVID‐19 infection via data mining methods. The findings of this study could help data scientists, radiologists, and clinicians in the future development and implementation of these methods in similar conditions and timely and appropriately save patients from adverse disease outcomes. [ABSTRACT FROM AUTHOR]

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 machinelearning 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 FROM AUTHOR]

Background: Evidence showed that partial or complete loss of smell and taste might be a possible primary symptom of the 2019 novel coronavirus (COVID-19). This study aimed to systematically review and pool all available evidence on the olfactory and gustatory dysfunction in COVID-19 patients. Methods: In this systematic review, a comprehensive search was carried out systematically through e-databases including PubMed, EMBASE, Scopus, and Web of Science (WoS); that was limited to English-language studies published from 2019 up to 6th May 2020. Afterward, all studies reported the taste and smell dysfunction in the COVID-19 patients were included. The quality of the studies was assessed by the Mixed Methods Appraisal Tool (MMAT). The pooled prevalence of olfactory and gustatory dysfunction was estimated using the random effects meta-analysis method. Results: Among 28 eligible included studies in this systematic review, finally, 22 studies met the eligibility criteria and were included in the meta-analysis. According to the random effect meta-analysis, the global pooled prevalence (95% confidence interval) of any olfactory dysfunction, anosmia, and hyposmia was 55% (40%-70%), 40% (22%-57%), and 40% (20%-61%) respectively. The pooled estimated prevalence of any gustatory dysfunction, ageusia, and dysgeusia was 41% (23%-59%), 31% (3%-59%), and 34% (19%-48%) respectively. Conclusions: Olfactory and gustatory dysfunction is prevalent among COVID-19 patients. Therefore, olfactory and gustatory dysfunction seems to be part of important symptoms and notify for the diagnosis of COVID-19, especially in the early phase of the infection. [ABSTRACT FROM AUTHOR]

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

Introduction: Severe nausea and vomiting associated with weight loss, ketonemia, and electrolyte imbalance in pregnancy is called hyperemesis gravidarum (HG). Its cause is unknown but there are some hypotheses such as hormonal mechanisms, psychological and emotional factors and Helicobacter pylori (H.pylori) infection. The aim of this study was to consideration of the relationship between H.pylori infection and HG using serology and stool antigen tests for detection of H.pylori infection. Methods: 44 pregnant women who had presented in Gorji Zadeh clinics with the diagnosis of HG and 44 normal pregnant women of matched gestational age were included in this prospective study. Two groups with respect to age, gestational age, body mass index, history of abortion, parity, history of coffee consumption, smoking were not significantly different. The infection of H.pylori was analyzed in the sera of patients by serology IgG and IgM tests and by as stool antigen test in fecal samples with ELISA method. Statistical analysis was performed by t test and p = 0.05 considered significant. Results: The rates of serology-specific H.pylori IgG positivity were in case and control groups respectively, 84.1% (37 of 44) and 61.34% (27 of 44) (P = 0.01) also the rates of serology-specific H.pylori IgM positivity were, respectively, in case and control groups were in case and control groups respectively 79.54% (35 of 44) and 63.64% (28 of 44) (P = 0.025). Furthermore the rates of H.pylori stool antigen test positivity were, in case and control respectively 72.27% (34 of 44) and 54.55% (24 of 44) (P = 0.038). Finally the rates of serology-specific H.pylori in 88 pregnant women were 70.08% against 26.92 %. Conclusion: The association severe nausea and vomiting of pregnancy and H.pylori infection was significant. It seems that H.pylori infection is significantly associated with hypermesis gravidarum. [ABSTRACT FROM AUTHOR]

Recent studies have shown that several children diagnosed with COVID-19 have developed Kawasaki Disease (KD)-like symptoms. This systematic review aims to assess the demographic, laboratory, and clinical characteristics of children with KD-like syndrome during the COVID-19 pandemic and evaluate efficacy of treatments and patients' outcome. A comprehensive search was carried out systematically through PubMed, Scopus, and Web of Science (WoS), medRxiv, and bioRxiv by two reviewers independently for all studies or preprints data on the demographic, laboratory, and clinical characteristics of children with K.D-like signs during the COVID-19 outbreak. Overall, 378 studies were identified by the systematic search, of which 25 studies were included in the study. The included studies involved 599 patients in total. Thirteen studies (52%) were case reports or case series, and the rest (48%) were cohort studies. In 19 studies, patients were diagnosed with Multisystem inflammatory syndrome in children (MIS-C). In 16 studies COVID-19 was diagnosed in all patients based on their polymerase chain reaction result, serological findings, and computed tomography results. Higher C-reactive protein and erythrocyte sedimentation rate level were the most prevalent laboratory findings. In most studies, patients had leucopenia with marked lymphopenia, hypoalbuminemia, and increased ferritin, as well as hyponatremia. Abnormal echocardiography and respiratory outcomes were the most common clinical outcomes. In 11 studies, all patients required intensive care unit admission. Findings of the present systematic review show that the incidence of KD-like syndrome in the COVID-19 pandemic increased significantly. Moreover, this study offers new insights in the KD-like syndrome pathogenesis and clinical spectrum during COVID-19 pandemic., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Mardi, Esmaeili, Iravani, Abdar, Pourrostami and Qorbani.)