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Design and implementation of an intelligent clinical decision support system for diagnosis and prediction of chronic kidney disease
No full textIntroduction: Chronic kidney disease (CKD) is one of the most important public health concerns worldwide. The steady increase in the number of people with End-stage renal disease (ESRD) needing a kidney transplant to survive and incur high costs, highlights early diagnosis and treatment of the disease. This study aimed to design a Clinical Decision Support System (CDSS) for diagnosing CKD and predicting the advanced stage to achieve better management and treatment of the disease. Materials and Methods: In this retrospective and developmental study, we studied the records of 600 suspected CKD cases with 22 variables referred to ShahidLabbafinejad Hospital in Tehran from 2019 to 2020. Data mining algorithms such as Naïve Bayesian, Random Forest, Multilayer Perceptron neural network, and J-48 decision tree were developed based on extracted variables. Then the recital of selected models was compared by some performance indices and 10-fold cross-validation. Finally, the most appropriate prediction model in terms of performance was implemented using the C # programming language. Results: Random Forest classification algorithm with an accuracy of 99.8 and 88.66, specificity of 100 and 93.8, the sensitivity of 99.75 and 88.7, f-measure of 99.8 and 88.7, kappa score of 99.4 and 82.73, and ROC of 100 and 90.52 was identified as the best data mining model for CKD diagnosis and prediction respectively. Conclusion: The developed MC-DMK system based random Forestcan be used practically in clinical settings. © 2022, Semnan University of Medical Sciences. All rights reserved
Performance Analysis of Selected Decision Tree Algorithms for Predicting Drug Adverse Reaction among COVID-19 Hospitalized Patients
No full textIncrease in drug allergies and unpleasant adverse effects caused by COVID-19 medication therapies has doubled the need for computing technologies and intelligent systems for predicting poor medication outcomes. This study aimed to construct machine learning (ML) based prediction models to better predict adverse drug effects among COVID-19 hospitalized patients. In this retrospective and single-center study, 482 hospitalized COVID-19 patients were used for analysis. First, the Chi-square test was employed to determine the most critical factors predicting adverse drug effects at P<0.05. Second, the four selected decision tree (DT) algorithms were applied to implement the model. Finally, the best DT model was acquired for predicting adverse drug effects using various performance criteria. This study showed that the 18 variables gained the Chi-square at P<0.05 as the most important factors predicting adverse drug reactions. Besides, comparing the performance of selected algorithms demonstrated that generally, the J-48 algorithm with F-Score=94.6 and AUC=0.957 was the best classifier predicting adverse drug reactions among hospitalized COVID-19 patients. Finally, it found that the J-48 algorithm enables a reasonable level of accuracy in predicting the risk of harmful drug effects among COVID-19 hospitalized patients. It potentially facilitates identifying high-risk patients and informing proper interventions by the clinicians. © 2022 by SPC (Sami Publishing Company
Academic Burnout and Its Relationship with Employment Hope among Health Sciences Students
No full textBackground & Objective: The study of academic burnout and its predictors as one of the main challenges of the educational system is of great importance. This research was conducted to investigate and explain the role of hope for employment and its subscales in predicting academic burnout. Materials & Methods: All health sciences students at Ilam, Arak, Birjand, Semnan, and Hamedan Universities of Medical Sciences, Iran, in 2019 were examined in this descriptive-correlation study. The samples were selected using a stratified random sampling method (n=400). The required data were gathered using the Academic Burnout Questionnaire (developed by Berso et al.) and the Qureshi Rad Employment Hope Questionnaire. The collected data were analyzed in the SPSS-20 software using an independent t-test, one-way ANOVA, and simple and multiple linear regressions. Results: The results of the study showed that 2.3, 61.3, and 36.5 of students suffered from low, medium, and high levels of academic burnout, respectively. The mean ± SD of the hope for employment score was 55.69±14.61, indicating a moderate level of hope for employment among the study participants. Based on the study results, 8.4 of the variance of academic burnout could be predicted by hope for employment (F=37.58, R=0.298, R2=0.086, adjusted R2=0.084, P<0.001). Conclusion: The study findings revealed the positive effect of hope for employment on academic burnout. In addition to reducing academic burnout, interventions focused on increasing employment hope can also improve the academic performance of health sciences students. © The Authors
Comparison of the thickening of donor skin graft with conventional method (using vaseline gas) and platelet-rich plasma topical application
No full textBackground: Healing involves complex processes that are not yet fully known. The wound healing process consists of three stages. In all these stages, normal wound healing requires platelet activation, release of cytokines and growth hormones, and chemotaxis and cell differentiation. Platelets play a key role in homeostasis and wound healing and growth factor production of more than 30 carried out by them. Platelets regulate the healing process with their chemotactic effect. Antilogous PRP platelet count in about 3 to 5 times increase and consequently also increases the number of growth factors, for this reason, they are being used in surgical procedures and clinical therapy. Methods: At Ilam Medical Center in Imam Khomeini Hospital, 20 patients with two similar donor graft sites were gradually selected to participate in a clinical trial from January to March 2017. The two regions have the same skin graft patients, an area of Honor conventional and other areas with the topical administration of platelet-rich plasma That immediately after surgery and in the days after the fifth and eleventh, eightieth and after washing the wound with a topical serum Physiology rubbed on the wound and thus treated The rate of wound healing clinically and using X-ray photochecked and compared. Results: Seven are male and thirteen are female and the age range of patients is between 17 and 67 years. After collecting wound healing times in two groups, we used the means comparison method to evaluate the effect of PRP on wound healing rate and analyzed the results (T-Test). Because the data followed a normal distribution, we used the Independent T-test method, which resulted in 0.416, which was higher than the alpha level equal to 0.05. Conclusion: In this study, we found that PRP had a positive effect on wound healing time and increased the speed of wound healing. It is suggested that the effect of the PRP method on various organs that have not been tested before, be discussed in future studies. © 2022 Tehran University of Medical Sciences. All rights reserved
Development and evaluation of an electronic nursing documentation system
No full textBackground Nursing documentation is a critical aspect of the nursing care workflow. There is a varying degree in how detailed nursing reports are described in scientific literature and care practice, and no uniform structured documentation is provided. This study aimed to describe the process of designing and evaluating the content of an electronic clinical nursing documentation system (ECNDS) to provide consistent and unified reporting in this context. Methods A four-step sequential methodological approach was utilized. The Minimum Data Set (MDS) development process consisted of two phases, as follows: First, a literature review was performed to attain an exhaustive overview of the relevant elements of nursing and map the available evidence underpinning the development of the MDS. Then, the data included from the literature review were analyzed using a two-round Delphi study with content validation by an expert panel. Afterward, the ECNDS was developed according to the finalized MDS, and eventually, its performance was evaluated by involving the end-users. Results The proposed MDS was divided into administrative and clinical sections; including nursing assessment and the nursing diagnosis process. Then, a web-based system with modular and layered architecture was developed based on the derived MDS. Finally, to evaluate the developed system, a survey of 150 registered nurses (RNs) was conducted to identify the positive and negative impacts of the system. Conclusions The developed system is suitable for the documentation of patient care in nursing care plans within a legal, ethical, and professional framework. However, nurses need further training in documenting patient care according to the nursing process, and in using the standard reporting templates to increase patient safety and improve documentation
Comparison of machine-learning algorithms efficiency to build a predictive model for mortality risk in COVID-19 hospitalized patients
No full textIntroduction: The rapid worldwide outbreak of SARS-CoV-2 has posed serious and unprecedented challenges to healthcare systems in predicting disease behavior and outcomes. To overcome these challenges or ambiguities, this study aimed to create and validate several predictive models using of selected ML algorithms to stratify the mortality risk in COVID-19 hospitalized patients and choice the best performing algorithm. Materials and Methods: Data of 1224 hospitalized patients with COVID-19 diagnosis based on the findings of the confirmed-laboratory test were extracted from the Ilam COVID-19 registry (Ilam CoV reg) database. Then the most important clinical parameters in the COVID-19 mortality were identified and used as inputs of the selected ML algorithms, including K-Nearest Network (KNN), Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF). Finally, the performance of the developed models was compared based on different confusion matrix evaluation criteria and the most appropriate predictive model was determined. Results: A total of 17 parameters were identified as the most influential clinical variables in the mortality of COVID-19. By comparing the performance of ML algorithms according to various evaluation criteria, the KNN algorithm with precision of 94.21, accuracy of 93.74, recall of 100, F-measure of 93.2 and ROC of 92.23, yielded better performance than other developed algorithms. Conclusion: KNN enables a reasonable level of accuracy and certainty in predicting the mortality of patients with COVID-19 and potentially facilitates identifing high risk patients and, inform proper interventions by the clinicians. © 2022, Semnan University of Medical Sciences. All rights reserved
The role of NK and NKT cells in the pathogenesis and improvement of multiple sclerosis following disease-modifying therapies
No full textBackground Multiple sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system (CNS) that T cells become autoreactive by recognizing CNS antigens. Both innate and adaptive immune systems are involved in the pathogenesis of MS. In recent years, the impact of innate immune cells on MS pathogenesis has received more attention. CD56(bright) NK cells, as an immunoregulatory subset of NK cells, can increase the production of cytokines that modulate adaptive immune responses, whereas CD56(dim) NK cells are more active in cytolysis functions. These two main subsets of NK cells may have different effects on the onset or progression of MS. Invariant NKT (iNKT) cells are other immune cells involved in the control of autoimmune diseases; however, variant NKT (vNKT) cells, despite limited information, could play a role in MS remission via an immunoregulatory pathway. Aim We aimed to evaluate the influence of MS therapeutic agents on NK and NKT cells and NK cell subtypes. Materials and Methods The possible mechanism of each MS therapeutic agent has been presented here, focusing on the effects of different disease-modifying therapies on the number of NK and NKT subtypes. Results Expansion of CD56(bright) NK cells, reduction in the CD56(dim) cells, and enhancement in NKT cells are the more important innate immune cells alterations following the disease-modifying therapies. Conclusion Expansion of CD56(bright) NK cells or reduction in the CD56(dim) cells has been associated with a successful response to different treatments in MS. iNKT and vNKT cells could have beneficial effects on MS improving. It seems that they are enhanced due to some of MS drugs, leading to disease improvement. However, a reduction in the number of NKT cells could be due to the adverse effects of some of MS drugs on the bone marrow
Pooled Prevalence Estimate of Ocular Manifestations in COVID-19 Patients: A Systematic Review and Meta-Analysis
No full textBackground: There are reports of ocular tropism due to respiratory viruses such as severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Various studies have shown ocular manifestation in coronavirus disease-2019 (COVID-19) patients. We aimed to identify ophthalmic manifestations in COVID-19 patients and establish an association between ocular symptoms and SARS-CoV-2 infection. Methods: A systematic search of Medline, Scopus, Web of Science, Embase, and Cochrane Library was conducted for publications from December 2019 to April 2021. The search included MeSH terms such as SARS-CoV-2 and ocular manifestations. The pooled prevalence estimate (PPE) with 95 confidence interval (CI) was calculated using binomial distribution and random effects. The meta-regression method was used to examine factors affecting heterogeneity between studies. Results: Of the 412 retrieved articles, 23 studies with a total of 3,650 COVID-19 patients were analyzed. The PPE for any ocular manifestations was 23.77 (95 CI: 15.73-31.81). The most prevalent symptom was dry eyes with a PPE of 13.66 (95 CI: 5.01-25.51). The PPE with 95 CI for conjunctival hyperemia, conjunctival congestion/conjunctivitis, and ocular pain was 13.41(4.65-25.51), 9.14(6.13-12.15), and 10.34 (4.90-15.78), respectively. Only two studies reported ocular discomfort and diplopia. The results of meta-regression analysis showed that age and sample size had no significant effect on the prevalence of any ocular manifestations. There was no significant publication bias in our meta-analysis. Conclusion: There is a high prevalence of ocular manifestations in COVID-19 patients. The most common symptoms are dry eyes, conjunctival hyperemia, conjunctival congestion/conjunctivitis, ocular pain, irritation/itching/burning sensation, and foreign body sensation
Years of Life Lost (YLLs) Due to Suicide and Homicide in Ilam Province: Iran, 2014-2018
No full textObjective: To provide detailed of suicide and homicide mortality and calculate of years of life lost (YLLs) in Ilam province Iran, during 2014-2018. Methods: In this cross-sectional study, all deaths due to suicide and homicide were enrolled to estimate YLLs, in Ilam province between 2014-2018. The source of data was legal medicine organization (LMO). All analysis was performed at 0.05 significant levels using statistical software package STATA for Windows version 11.2 and SPSS 21 software. Results: The total YLLs of suicide and homicide were 15685 and 5317, respectively. 522 per 100,000 populations were suicide and 117 for homicide. The YLL and 95 confidence interval form suicide was 34.4 (32.8-36.1) for both sexes that 33.7 (31.6-35.8) for men, and 35.5 (32.7-38.3) for women. In this study period, YLLs rate began to increase over the years in both injury-related in 2016. Conclusion: Results of this study disclosed the most prominent contribution of men and peoples aged 15-29 to the YLLs. Also our results indicate a recent increase in suicide and homicide YLLs for both genders
Body Image, Quality of Life, and Their Predicting Factors in Pregnant Women: A Cross-Sectional Study
No full textPregnancy can influence women's psychological health, including body image and quality of life. This study aimed to examine the relationship between body image and quality of life and their predicting factors in pregnant women. This cross-sectional study was conducted on 250 pregnant women referred to health centers in Ilam City, Iran. Participants were selected using a random sampling method. Data collection tools comprised a sociodemographic questionnaire, Body Image Concern Inventory (BICI), and quality of life questionnaire (Short Form-12). Data were analyzed using statistical software. The mean +/- SD of body image concern and quality of life was estimated at 31.77 +/- 9.86 and 54.62 +/- 15.71, respectively. There was a significant and negative correlation between body image and quality of life (p-value = 0.001, r = -0.313). Also, the most important predictors of body image were vitality, body mass index (BMI), general health, and unintended pregnancy, respectively, and body dissatisfaction was the most significant predictor of quality of life. This study revealed some variables affecting pregnant women's body image and quality of life. Further studies are required to consider other factors influencing body image and quality of life among pregnant women