Clinical prediction rule for bacteremia with pyelonephritis and hospitalization judgment: chi-square automatic interaction detector (CHAID) decision tree analysis model

Objective This study was performed to identify predictive factors for bacteremia among patients with pyelonephritis using a chi-square automatic interaction detector (CHAID) decision tree analysis model. Methods This retrospective cross-sectional survey was performed at Juntendo University Nerima Hospital, Tokyo, Japan and included all patients with pyelonephritis from whom blood cultures were taken. At the time of blood culture sample collection, clinical information was extracted from the patients’ medical charts, including vital signs, symptoms, laboratory data, and culture results. Factors potentially predictive of bacteremia among patients with pyelonephritis were analyzed using Student’s t-test or the chi-square test and the CHAID decision tree analysis model. Results In total, 198 patients (60 (30.3%) men, 138 (69.7%) women; mean age, 74.69 ± 15.27 years) were included in this study, of whom 92 (46.4%) had positive blood culture results. The CHAID decision tree analysis revealed that patients with a white blood cell count of >21,000/μL had a very high risk (89.5%) of developing bacteremia. Patients with a white blood cell count of ≤21,000/μL plus chills plus an aspartate aminotransferase concentration of >19 IU/L constituted the high-risk group (69.0%). Conclusion The present results are extremely useful for predicting the results of bacteremia among patients with pyelonephritis.

Determination of Climate and Social Community Factors in Coronavirus Disease-19 Spread Distribution

BACKGROUND: Coronavirus Disease-2019 (COVID-19) is very shocking to the world. Until mid-2020, this virus has not yet found a vaccine that can be produced and can be applied en mass across the country. The spread of COVID-19 differs between regions which implie that regional characteristics have an influence on the rate of growth. Regional and social climate factors are thought to have a role in the growth rate of COVID-19. AIM: This study aims to find the role of climate and social society on the spread of COVID-19. METHODS: This research uses OLS regression analysis method, and then continued with Chi-squared Automatic Interaction Detector analysis to find the segmentation of the role of climate and social factors on the daily growth rate of COVID-19 in positive and deceased patients. RESULTS: The results of this study state that all independent variables of the study have a significant effect on the spread of COVID-19, with R-squared values in positive and deceased patients, respectively 61.1% and 70.0%. Strategic steps are needed to carry out policies that are targeted, effective and efficient. CONCLUSION: The results of this study can be a reference for the government in determining policies to reduce the growth rate of COVID-19, by focusing on areas that have poor sanitary environment and area are on Java Island.

LEVERAGING DATA MINING TECHNIQUES IN ENHANCING THE EFFICACY OF HEALTHCARE IN THIRD WORLD COUNTRIES: SPOTLIGHT ON TURKISH CASE STUDY

This paper presents a utilization of the information mining technique to decide the financial profiles of the public clinics in Turkey. The review depends on the information accumulated in 2004, covering 645 public clinics run by the Ministry of Health (MoH) as the fundamental supplier of essential and optional wellbeing administrations in Turkey. The public medical clinics, as of now financed by a combination of assets allotted from the overall spending plan and separately worked rotating reserves, need critical answers for their financial issues as a piece of a continuous public change exertion. The examination takes on the Chi-Square Automatic Interaction Detector (CHAID) choice tree calculation, as one of the most efficient and cutting-edge information digging technique utilized for division. The investigation has discovered that the public clinics could be sorted by the CHAID into 12 unique profiles as far as their financial execution. These profiles have directed us in deciding the key financial markers to be engaged upon in the public emergency clinics and present accepted procedures to work on their individual financial exhibitions. The findings have likewise permitted strategy ideas regarding the financial techniques that might be considered in working on the financial execution of the public medical clinics toward an effective wellbeing area change in Turkey.

Clinical Prediction Rule for Bacteremia Among Patients with Pyelonephritis and Hospitalization Judgment: Chi-Squared Automatic Interaction Detector (CHAID) Decision Tree Analysis Model

Background: Positive risk factors for bacteremia among patients with pyelonephritis have not been defined using a Chi-Squared Automatic Interaction Detector (CHAID) Decision Tree Analysis Model. Purpose: We sought to identify predictive factors for bacteremia among patients with pyelonephritis and therefore which patients need hospitalization.Methods: This retrospective cross-sectional survey was performed at the Juntendo University Nerima Hospital, Tokyo, Japan and comprised all patients with pyelonephritis from whom blood cultures were taken from January 1, 2010 to July 31, 2020. At the time of blood culture sample collection, clinical information was obtained from medical charts, along with vital signs, quick Sequential Organ Failure Assessment (qSOFA), subjective symptoms, objective physical findings, laboratory findings, and results of blood and urine cultures. Factors potentially predictive of bacteremia among patients with pyelonephritis were analyzed using the Student’s t-test or chi-squared test and the CHAID decision tree analysis model.Results: A total of 198 patients (male:female, 60 (30.3%):138 (69.7%), ages (mean±SD) 74.69±15.27 years) were included in this study, of whom 92 (46.4%) had positive blood culture results. The CHAID decision tree analysis revealed that patients with White blood cell >21,000/μL had a quite-high-risk (89.5%) of developing bacteremia. Patients with White blood cell ≤21,000/μL plus Chill plus Aspartate aminotransferase >19 IU/L represented a high-risk group (69.0%). Conversely, patients with White blood cell ≤21,000/μL plus non-Chill plus Albumin >3.60 g/dL were at a low risk (16.3%) of developing bacteremia.Conclusion: Our results emphasize the importance of hospitalization among high-risk and quite-high-risk groups of pyelonephritis patients.

709 Healthy Sleep as an important resource and buffer for intersectional discrimination: Results from Covid 19 study

Introduction Our previous studies have highlighted sleep disparities for this underserved population, including how Grandparents Raising Grandchildren (GRG) experience troubled and disruptive sleep. Intersectional types of discrimination facing these families during COVID 19, include: race/ethnicity of self and children, income, age, essential workforce status, and impairments (mobility, vision, and hearing). This current study intends to explore how healthy sleep is an important resource (potential buffer) for GRG experiencing intersectional discrimination during COVID 19. Methods We used community partnerships to recruit 600 GRG from all fifty states in USA and several tribes to complete an online survey on their experiences with caregiving and intersectional discrimination during COVID 19. We developed an index on intersectional discrimination based on GRG lived experiences to inform the survey and used descriptive and bivariate statistics to profile this group. Chi-square Automatic Interaction Detector (CHAID) analysis was used to build a predictive model to help determine how variables in our study best merge to explain intersectional discrimination. Results Of the GRGs’, 37% were between 54–65 years and 33% cared for children 6 to 10 years for at least 5 years. The types of discrimination that were more likely to be included in intersectional discrimination include: Black or African American [83.8% (31)], my child’s race [59.5% (22)], my lower economic status [56.8% (21)], and my status as a caregiver [56.8% (21)]. The resource needs that showed the most disparity (higher rate showed higher priority/extreme concern) between those with ID and those without included: Information on how COVID impacts race and ethnicity differently (6.0 vs. 3.61), ability to pay utilities (3.7 vs. 1.99), and information on how to achieve healthy sleep (4.19 vs. 2.64). Conclusion This study suggests that GRG facing intersectional discrimination identify the importance of attaining information on how to achieve healthy sleep as an important resource to them during COVID 19. These results can be used to help mobilize resources and disseminate information for this underserved group to improve healthy sleep and also model for their extended families and communities. Support (if any) This study was conducted by the Grandfamilies Outcome Workgroup, (GrOW), with support from Generations United and Collaborative Solutions.

Estimation of the spawning ground of some fish species in the Nha Trang bay

The paper presents the results of determining the spawning grounds of some fish species in Nha Trang bay MPA, which were sampled in 9/2018, 11/2018, 5/2019 and 7/2019. The analysis results are based on the distribution of the general density and the developmental stages, analysis of decision trees from egg density, location of stations and months by CHAID (Chi-square Automatic Interaction Detector), allowing a relatively accurate estimate of the spawning ground of the red anchovy (Encrasicholina punctifer Fowler, 1938) and the species of the genus Scarus. As a result, the main spawning grounds for red anchovy were the east of Hon Rua and the northeast of Hon Tam and that of Scarus is the southwest of Hon Mun island.

Diverse pathological lesions of primary aldosteronism and their clinical significance

Primary aldosteronism (PA) is mainly clinically classified as unilateral aldosterone-producing adenoma (APA) or bilateral idiopathic hyperaldosteronism. Immunohistochemistry for aldosterone synthase reveals a diverse PA pathology, including pathological APA and aldosterone-producing cell clusters. The relationship between PA pathology and adrenalectomy outcomes was examined herein. Data from 219 unilaterally adrenalectomized PA cases were analyzed. Pathological analyses revealed diverse putative aldosterone-producing lesions. Postoperative biochemical outcomes in 114 cases (test cohort) were classified as complete success (n = 85), partial success (n = 19), and absent success (n = 10). Outcomes in the large and small PA lesion groups, rather than between PA lesion types, were compared at five threshold values for PA lesion sizes (2–6 mm with 1-mm increments) to streamline the results. The proportion of complete success was significantly higher in the large PA lesion group than in the small PA lesion group at the 5-mm threshold only. The proportion of absent success was significantly higher in the small PA lesion group than in the large PA lesion group at all thresholds. Univariate and multivariate analyses of the test cohort identified serum K as an independent predictive factor for the small PA lesion group, which was confirmed in the 105-case validation cohort. Chi-squared automatic interaction detector analysis revealed that the best threshold of serum K for predicting large PA lesions was 2.82 mEq/L. These results will be beneficial for treating PA in clinical settings because patients with low serum K levels and apparent adrenal masses on CT may be subjected to adrenalectomy even if the adrenal venous sampling test is unavailable.

Chemical characterization of oil from four Avocado varieties cultivated in Morocco

The notable growth in the use of avocado oil in the nutritional and cosmetic field was the main objective to valorize the oil production of important varieties of avocados existing in Morocco by analyzing its chemical composition in fatty acids, sterols, tocopherols and its physico-chemical properties. Oleic acid is the main fatty acid in the oil; they constitute between 50 and 65% of the total fatty acids. The study of the unsaponifiable fraction revealed that avocado oil contains 3259.9–5378.8 mg/kg sterols and 113.13–332.17 mg/kg tocopherols. Chemo-metric tools were employed in manner optimization, such as principal component analysis, agglomerative hierarchical clustering, analysis of variance, and classification trees using Chi-squared Automatic Interaction Detector. Chemo-metric tools revealed a difference in the composition of fatty acid, sterols, and tocopherol of avocado oil samples. This difference resulted from a variety of avocado fruits. Agglomerative Hierarchical Clustering (AHC) method was efficient distinguishing avocado oil samples based on fruit variety using fatty acids, tocopherols, sterol compositions and total sterol. Principal component analysis (PCA) method allowed the distinction the set avocado oil dataset based on fruit varieties, supplied a correct discrimination rate of 95.44% for avocado fruit varieties using the fatty acid. Chi-squared Automatic Interaction Detector (CHAID) carried out using the same variables, also provided an acceptable classification rate of 50% for avocado fruit varieties using the total tocopherol content. Besides, a comparative study of the physico-chemical properties in terms of acidity index, saponification index, iodine index, chlorophylls, carotenoids, and methyl and ethyl esters was performed.

Analysis of merchant vessel accidents in Istanbul strait through decision tree method

In this study merchant vessel accidents which occured between 2001 and 2016 in the sectors of Türkeli, Kandilli, Kadıköy, Marmara which constitutes Istanbul Strait region under Istanbul Vessel Traffic Services scope have been examined. Data was obtained from database of Ministry of Transport and Infrastructure Main Search and Rescue Coordination Center, and after data cleansing process, 535 vessel accidents which involve merchant cargo vessels of above 500 grosston have been analyzed. Merchant cargo vessel accidents which were taken place in the specified sectors have been examined with CHAID (Chi-square Automatic Interaction Detector) Decision Tree method. CHAID Decision Tree method is one of the most common used decision tree algorithms in extracting meaningful rules from big datasets and for classification. Through conducting CHAID Decision Tree method for merchant vessel accidents relationship between accident type (collision/contact, grounding and other) and vessel factors (vessel type, Length overall (LOA), vessel gross tonnage, vessel age, flag, loading condition), time factors (accident time, season of accident) and other factors (sector where accident occured, pilot on board or not) has been analyzed. Accident occuring sector, pilot on board/not, vessel type and accident time have been found as the most important input variables. Based on the result of the Decision Tree method applied to the data set, it was observed that the accidents occurring in the Kadıköy sector were collision / contact with 86% probability, the accidents occurring in the Kandilli or Marmara sectors were collision / contact with 48% probability and in the Türkeli sector, both collision / contact and other accident types had 36% occurring probability.

Los sesgos de elección al rescate de la retención en los MOOCs

Luego del éxito de los MOOCS en los últimos años, la baja retención, pone en duda su efectividad. La presente investigación analiza los datos de diferentes MOOCs con los objetivos de determinar los estudiantes y MOOCs con perfiles desertores y encontrar patrones de estudiantes finalizadores, a través de distorsiones de la realidad (sesgos). Se utilizó la técnica de estratificación y predicción, árbol de decisión de tipo CHAID (Chi-square automatic interaction detector). Los resultados indican que las variables interés por el certificado, sesgos de elección y edad son las que mejor predicen los perfiles de los estudiantes desertores. Para el caso de los perfiles de los cursos que favorecen la deserción; la duración del MOOC, los sesgos de elección, la cantidad de módulos y el número de profesores muestran el curso con mayor probabilidad de abandono. Los mayores predictores en el interés el certificado final se encuentran descritos por los estudiantes con estudios de licenciatura y del área de interés de negocios. Contrario a lo esperado, se encontró como mayor predictor de la deserción el número incremental de preguntas a lo largo de las diferentes evaluaciones durante el MOOC. La discusión presenta estrategias pedagógicas que benefician directamente la supervivencia de los MOOCs.