Application of random survival forest for competing risks in prediction of cumulative incidence function for progression to AIDS

Objective: There has remained a need to better understanding of prognostic factors that affect the survival or risk in patients with human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS), particularly in developing countries. The aim of the present study aimed to identify the prognostic factors influencing AIDS progression in HIV positive patients in Hamadan province of Iran, using random survival forest in the presence of competing risks (death from causes not related to AIDS). This method considers all interactions between variables and their nonlinear effects. Method(s): A data set of 585 HIV-infected patients extracted from 1997 to 2011 was utilized. The effect of several prognostic factors on cumulative incidence function (probability) of AIDS progression and death were investigated. Result: The used model indicated that using antiretroviral therapy tuberculosis co-infection are two top most important variables in predicting cumulative incidence function for AIDS progression in the presence of competing risks, respectively. The patients with tuberculosis had much higher predicted cumulative incidence probability. Predicted cumulative incidence probability of AIDS progression was also higher for mother to child mode of HIV transmission. Moreover, transmission type and gender were two top most important variables for the competing event. Men and those patients with IDUS transmission mode had higher predicted risk compared to others. Conclusion: Considering nonlinear effects and interaction between variables, confection with tuberculosis was the most important variable in prediction of cumulative incidence probability of AIDS progression

Identifying predictors of progression to AIDS and mortality post-HIV infection using parametric multistate model

OBJECTIVES: The human immunodeficiency virus (HIV) has already remained as a major public health problem all over the world. This study aimed to identify the prognostic factors influencing the disease progression in patients with human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) in Iran, using parametric multi-state models to take into account the intermediate event in the analysis.   METHOD(S): The data of the present retrospective cohort study was extracted in Tehran from April 2004 to March 2014. The number of 2473 HIV-infected patients in Behavioral Diseases Counseling Centers was enrolled. The outcomes of interest were the transition times from HIV diagnosis to AIDS and AIDS to death. The effect of several prognostic factors on both transitions was investigated. RESULTS: Parametric models indicated that AIDS progression was significantly associated with an increase in age (P = 0.017), low education (P = 0.026), and a decreased CD4 cell count (P = 0.001). Furthermore, the AIDS-related death was significantly associated with male sex (P = 0.010), tuberculosis coinfection (P = 0.001), antiretroviral therapy (P = 0.001) and a decreased CD4 cell count (P = 0.035). CONCLUSION: The results of this study indicated that CD4 cell count was one of the most important prognostic factors that affected and accelerated both HIV→AIDS and AIDS→DEATH transitions and antiretroviral treatment was found to be an effective measure in decelerating survival of patients with AIDS to death state. The usual Cox Model is not able to identify some of these prognostic factors.&nbsp

A Support Vector Regression Approach for Three–Level Longitudinal Data

Background: Longitudinal data structure is frequently observed in health science. This introduces correlation to the data that needs to be handled in modelling process. Recently, machine learning approaches have been introduced in the context of longitudinal data for prediction of the response variable purpose. In this paper a mixed-effects least squares support vector regression model is presented for three-level longitudinal data. In the proposed model, multiple random-effect terms are used for considering the existing correlation structures in longitudinal data. The proposed model is flexible in modelling (non-)linear and complex relationships between predictors and response, while it takes into account the hierarchical structure of data and is computationally efficient.  Methods Both random intercept and random trend models with a special correlation structure of errors are illustrated. A real data example on human Brucellosis rate is analysed and two simulation studies are performed to illustrate the proposed model. The fitting and generalisation performance of the proposed model are investigated and compared with the ordinary least squares support vector regression and linear mixed-effects models.  Results: Based on the human Brucellosis rate example and two simulation studies, the proposed models had the best performance in generalisation. Also, the fitting performances of the proposed models were better than that of the classic models.  Conclusion: Our study revealed that in the presence of nonlinear relationship between covariates and outcome, the proposed MLS-SVR model has the best fitting and generalisation performance and can capture correlation of the data

Induction and evaluation of acute inflammatory model of sulfur mustard analogue (CEES) in C57BL/6 mice

The acute threat model with mustard analogs has been produced and synthesized due to the dangers that sulfur mustard can pose to users in experimental work conditions and the creation of sulfur and numerous analogs to prevent its dangers. To evaluate the confirm of 2- Chloroethyl Ethyl Sulfide (CEES) as an analog of SM, we set up a new model of CEES systemic injection & exposure to be as close as possible role of this toxin analog effects on innate immune inflammation. Female C57BL/6 mice, 8-10- week old at the onset of the study, were exposed to CEES (10 mg / kg). The administration route was Intraperitoneal (IP) injection. At the end of the study, the mice’s lung fluid, and peritoneal lavage, spleen lymphocyte and lung tissue were extracted for future histopathological assessments

PENGEMBANGAN BISNIS JASA PEMASARAN DIGITAL "DIGITALEAD" DENGAN BUSINESS MODEL CANVAS

This research aims to analyze the business development of digital marketing services using the Business Model Canvas. The Business Model Canvas provides a comprehensive framework to understand a business, including its Customer Segments, Value Proposition, Channels, Customer Relationships, Revenue Streams, Key Resources, Key Activities, Key Partners, and Cost Structure. The study utilizes the Business Model Canvas as a tool to analyze a case study of a digital marketing services company and identify its strengths and opportunities for growth. The findings suggest that a well-designed business model can lead to successful business development and growth in the digital marketing industry. The research concludes by highlighting the importance of using a systematic approach such as the Business Model Canvas for analyzing business development in digital marketing services. The conclusion is that the development of a digital marketing services business is strongly influenced by internal and external factors of the company. Therefore, companies must continuously monitor and improve these factors to increase business competitivenes

Predictors of mortality among hemodialysis patients in Hamadan Province using random survival forests

Background: Hemodialysis patients are at the high risk for morbidity and mortality. The aim of this study was to find out the predictors of mortality and survival in hemodialysis patients in Hamadan province. Methods: The numbers of 785 patients during the entire 11 years were enrolled into this historical cohort study. Data were gathered by a checklist of hospital records. The survival time was the time between start of Hemodialysis treatment to patients death as the end point. Random survival forests (RSF) method was used to identify main predictors of survival among the patients. Results: The median survival time was 613 days. The number of 376 death was occurred. The three most important predictors of survival were hemoglobin, CRP and albumin. RSF method predicted survival better than the conventional Cox-proportional hazards model (out-of-bag C-index of 0.808 for RSF vs. 0.727 for Cox model). Conclusions: We found that higher levels of CRP, low serum albumin and low serum hemoglobin were the top three most important predictors of survival for HD patients

Modeling the trajectory of CD4 cell count and its effect on the risk of AIDS progression and TB infection among HIV-infected patients using a joint model of competing risks and longitudinal ordinal data

Background: This study was conducted to better understand the influence of prognostic factors and the trend of CD4 cell count on the risk of progression to acquired immunodeficiency syndrome (AIDS) and tuberculosis (TB) infection among patients with human immunodeficiency virus (HIV) in a developing country.  Methods: The information of 1530 HIV-infected patients admitted in Behavioral Diseases Counseling Centers, Tehran, Iran, (2004-2014) was analyzed in this study. A joint model of ordinal longitudinal outcome and competing events is used to model longitudinal measurements of CD4 cell count and the risk of TB-infection and AIDS-progression among HIV patients, simultaneously.  Results: The results revealed that the trend of CD4 cell count had a significant association with the risk of TB-infection and AIDS-progression (p<0.001). Higher ages (p<0.001), the history of being in prison (p=0.013), receiving antiretroviral therapy (ART) (p<0.001) and isoniazid preventive therapy (IPT) (p<0.001) were associated with the positive trend of CD4 cell count. Higher ages were also associated with higher risks of TB (p<0.001) and AIDS-progression (p<0.001). Furthermore, ART (p=.0009) and IPT (p<0.001) were associated with a lower risk of TB-infection. In addition, ART (p<0.001) was associated with a lower risk of AIDS-progression. Moreover, individuals being imprisoned (p=0.001) and abusing alcohol (p=0.012) were more likely to have TB-co-infection.  Conclusions: The used joint model provided a flexible framework for simultaneous studying of the effects of covariates on the level of CD4 cell count and the risk of progression to TB and AIDS. This model also assessed the effect of CD4 trajectory on the hazards of competing events.&nbsp

Alienation and the novice university student a replication of Seeman\u27s dimensions of alienation.

Source: Masters Abstracts International, Volume: 40-07, page: . Thesis (M.A.)--University of Windsor (Canada), 1989

Predicting preeclampsia and related risk factors using data mining approaches: A cross-sectional study

Background: Preeclampsia is a type of pregnancy hypertension disorder that has adverse effects on both the mother and the fetus. Despite recent advances in the etiology of preeclampsia, no adequate clinical screening tests have been identified to diagnose the disorder. Objective: We aimed to provide a model based on data mining approaches that can be used as a screening tool to identify patients with this syndrome and also to identify the risk factors associated with it. Materials and Methods: The data used to perform this cross-sectional study were extracted from the clinical records of 726 mothers with preeclampsia and 726 mothers without preeclampsia who were referred to Fatemieh Hospital in Hamadan City during April 2005–March 2015. In this study, six data mining methods were adopted, including logistic regression, k-nearest neighborhood, C5.0 decision tree, discriminant analysis, random forest, and support vector machine, and their performance was compared using the criteria of accuracy, sensitivity, and specificity. Results: Underlying condition, age, pregnancy season and the number of pregnancies were the most important risk factors for diagnosing preeclampsia. The accuracy of the models were as follows: logistic regression (0.713), k-nearest neighborhood (0.742), C5.0 decision tree (0.788), discriminant analysis (0.687), random forest (0.758) and support vector machine (0.791). Conclusion: Among the data mining methods employed in this study, support vector machine was the most accurate in predicting preeclampsia. Therefore, this model can be considered as a screening tool to diagnose this disorder. Key words: Preeclampsia, Random forest, C5.0 decision tree, Support vector machine, Logistic regression

Usefulness of the SF-36 Health Survey questionnaire in screening for health-related quality of life among parents of children with cancer: Latent profile analysis

Background: Poor health-related quality of life (HRQOL) of parents of children with cancer as their main caregivers can adversely affect child’s HRQOL. Short Form-36 Health Survey (SF-36) is a widely used instrument to measure HRQOL. However, there are no clearly defined cut-off points for screening for parents with poor HRQOL. This study aimed to find appropriate cut-off points for the SF-36 questionnaire in a sample of parents of children with cancer using latent profile analysis to add another possibility to use it. Methods: In this cross-sectional study, 110 couples (110 mothers and 110 fathers) of children diagnosed with cancer selected by simple random sampling method from the patients' files were included. The study was conducted at two settings, pediatric hematology ward and pediatric hematology clinic of a university hospital in 2016-2017. Latent Profile analysis method was used to determine appropriate cut-off points for the SF-36 questionnaire. Data was analyzed by Mplus and R3.3.0 software. Results:  Based on the results, scores ≤44, 45-63 and ≥64 for mental health, and scores ≤43, 44-59 and ≥60 for physical health classes indicate weak, medium, and good, respectively. These cut-off points showed acceptable accuracy in classification of individuals. For the total quality of life, correct classification rates were 88%, 65% and 53% for each class respectively. For mental health (physical health), they were 79 (63), 50 (62) and 52 (63) for each class respectively.    Conclusion: The cut-off points for the classes identified here can be useful in screening parents of children with cancer in clinical setting to provide clinical interventions to protect vulnerable parents from negative outcomes