scholarly journals Unusual localization of blood-borne Loa loa microfilariae in the skin depends on microfilarial density in the blood: Implications for onchocerciasis diagnosis in co-endemic areas

2021 ◽  
Author(s):  
Yannick Niamsi-Emalio ◽  
Hugues C Nana-Djeunga ◽  
Cédric B Chesnais ◽  
Sébastien D S Pion ◽  
Jules B Tchatchueng-Mbougua ◽  
...  
Keyword(s):  
Regression Tree ◽  
Classification And Regression Tree ◽  
Loa Loa ◽  
Immunoglobulin G4 ◽  
East Region ◽  
Antibody Positivity ◽  
Endemic Areas ◽  
Cart Model ◽  
The Relationship

Abstract Background The diagnostic gold standard for onchocerciasis relies on identification and enumeration of (skin-dwelling) Onchocerca volvulus microfilariae (mf) using the skin snip technique (SST). In a recent study, blood-borne Loa loa mf were found by SST in individuals heavily infected with L. loa, and microscopically misidentified as O. volvulus due to their superficially similar morphology. This study investigates the relationship between L. loa microfilarial density (Loa MFD) and the probability of testing SST positive. Methods A total of 1053 participants from the (onchocerciasis and loiasis co-endemic) East Region in Cameroon were tested for: i) Loa MFD in blood samples; ii) O. volvulus presence by SST, and iii) Ig[immunoglobulin]G4 antibody positivity to Ov16 by rapid diagnostic test (RDT). A Classification and Regression Tree (CART) model was used to perform a supervised classification of SST status and identify a Loa MFD threshold above which it is highly likely to find L. loa mf in skin snips. Results Of 1011 Ov16-negative individuals, 28 (2.8%) tested SST positive and 150 (14.8%) were L. loa positive. The range of Loa MFD was 0–85200mf/mL. The CART model subdivided the sample into two Loa MFD classes with a discrimination threshold of 4080 (95% CI: 2180–12240) mf/mL. The probability of being SST positive exceeded 27% when Loa MFD was >4080mf/mL. Conclusions The probability of finding L. loa mf by SST increases significantly with Loa MFD. Skin-snip polymerase chain reaction (PCR) would be useful when monitoring onchocerciasis prevalence by SST in onchocerciasis–loiasis co-endemic areas.

scholarly journals Artificial liver support system therapy in acute-on-chronic hepatitis B liver failure: Classification and regression tree analysis

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kaizhou Huang ◽  
Feiyang Ji ◽  
Zhongyang Xie ◽  
Daxian Wu ◽  
Xiaowei Xu ◽  
...  
Keyword(s):  
High Risk ◽  
Hepatitis B ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Artificial Liver ◽  
Liver Support ◽  
Artificial Liver Support ◽  
Cart Analysis ◽  
Classification And Regression ◽  
Cart Model

Abstract Artificial liver support systems (ALSS) are widely used to treat patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). The aims of the present study were to investigate the subgroups of patients with HBV-ACLF who may benefit from ALSS therapy, and the relevant patient-specific factors. 489 ALSS-treated HBV-ACLF patients were enrolled, and served as derivation and validation cohorts for classification and regression tree (CART) analysis. CART analysis identified three factors prognostic of survival: hepatic encephalopathy (HE), prothrombin time (PT), and total bilirubin (TBil) level; and two distinct risk groups: low (28-day mortality 10.2–39.5%) and high risk (63.8–91.1%). The CART model showed that patients lacking HE and with a PT ≤ 27.8 s and a TBil level ≤455 μmol/L experienced less 28-day mortality after ALSS therapy. For HBV-ACLF patients with HE and a PT > 27.8 s, mortality remained high after such therapy. Patients lacking HE with a PT ≤ 27.8 s and TBil level ≤ 455 μmol/L may benefit markedly from ALSS therapy. For HBV-ACLF patients at high risk, unnecessary ALSS therapy should be avoided. The CART model is a novel user-friendly tool for screening HBV-ACLF patient eligibility for ALSS therapy, and will aid clinicians via ACLF risk stratification and therapeutic guidance.

scholarly journals Analysis of Factors Affecting Hit-and-Run and Non-Hit-and-Run in Vehicle-Bicycle Crashes: A Non-Parametric Approach Incorporating Data Imbalance Treatment

2019 ◽  
Vol 11 (5) ◽  
pp. 1327 ◽  
Author(s):  
Bei Zhou ◽  
Zongzhi Li ◽  
Shengrui Zhang ◽  
Xinfen Zhang ◽  
Xin Liu ◽  
...  
Keyword(s):  
Geometric Mean ◽  
Regression Tree ◽  
Classification Performance ◽  
Classification And Regression Tree ◽  
Factors Affecting ◽  
Data Imbalance ◽  
Imbalance Problem ◽  
Hit And Run ◽  
Reported Data ◽  
Cart Model

Hit-and-run (HR) crashes refer to crashes involving drivers of the offending vehicle fleeing incident scenes without aiding the possible victims or informing authorities for emergency medical services. This paper aims at identifying significant predictors of HR and non-hit-and-run (NHR) in vehicle-bicycle crashes based on the classification and regression tree (CART) method. An oversampling technique is applied to deal with the data imbalance problem, where the number of minority instances (HR crash) is much lower than that of the majority instances (NHR crash). The police-reported data within City of Chicago from September 2017 to August 2018 is collected. The G-mean (geometric mean) is used to evaluate the classification performance. Results indicate that, compared with original CART model, the G-mean of CART model incorporating data imbalance treatment is increased from 23% to 61% by 171%. The decision tree reveals that the following five variables play the most important roles in classifying HR and NHR in vehicle-bicycle crashes: Driver age, bicyclist safety equipment, driver action, trafficway type, and gender of drivers. Several countermeasures are recommended accordingly. The current study demonstrates that, by incorporating data imbalance treatment, the CART method could provide much more robust classification results.

scholarly journals Relation between carotid vulnerable plaques and peripheral leukocyte: A case-control study of comparison utilizing multi-parametric CEUS

2019 ◽  
Author(s):  
Xianghong Luo ◽  
Wanbin Li ◽  
Yun Bai ◽  
Lianfang Du ◽  
Rong Wu ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Peripheral Leukocyte ◽  
Regression Tree Analysis ◽  
Vulnerable Plaques ◽  
Asymptomatic Patients ◽  
Contrast Enhanced ◽  
The Relationship ◽  
Control Study

Abstract Background: This study evaluates carotid vulnerable plaques using contrast-enhanced ultrasound (CEUS) and explores the relationship between vulnerable plaques and leukocytes.Methods: Sixty-two symptomatic and 54 asymptomatic patients underwent CEUS. The images were analyzed using time-intensity and fitting curves, and peak (PTIC), mean (MTIC), peak (PFC), sharpness (SFC), and area under the curve (AUCFC) were obtained. The relations between CEUS parameters and leukocytes were analyzed.Results: In the symptomatic group, total leukocytes and neutrophils were higher, while lymphocyte was decreased; PTIC, MTIC, PFC, SFC,, and AUCFC were significantly higher; MTIC and AUCFC were negatively correlated with lymphocytes, and MTIC was positively correlated with neutrophils. Classification and regression tree analysis showed that MTIC at a cutoff of 20.8 and AUCFC at a cutoff of 8.8 resulted in a predictive of acute cerebral infarction, accuracy of 84.3%, sensitivity of 87.1%, and specificity of 81.5%.Conclusions: The variation in the perivascular leucocyte is significantly related to intraplaque inflammatory activities, CEUS is a feasible monitor of intraplaque neovascularization, so CEUS combined with perivascular leucocyte could be helpful as a warning for vulnerable plaques.

scholarly journals Detection of Subclinical Keratoconus Using a Novel Combined Tomographic and Biomechanical Model Based on an Automated Decision Tree

2021 ◽  
Author(s):  
Peng Song ◽  
Shengwei Ren ◽  
Yu Liu ◽  
Pei Li ◽  
Qingyan Zeng
Keyword(s):  
Decision Tree ◽  
Amplitude Ratio ◽  
Regression Tree ◽  
Biomechanical Model ◽  
Study Data ◽  
Classification And Regression Tree ◽  
Stiffness Parameter ◽  
Chi Square ◽  
Interaction Detection ◽  
Cart Model

Abstract The aim of this study was to develop a predictive model for subclinical keratoconus (SKC) based on decision tree (DT) algorithms. A total of 194 eyes (including 105 normal eyes and 89 SKC) were included in the double-center retrospective study. Data were separately used for training and validation databases. The baseline variables were derived from tomography and biomechanical imaging. DT models were generated in the training database using Chi-square automatic interaction detection (CHAID) and classification and regression tree (CART) algorithms. The discriminating rules of the CART model selected variables of the Belin/Ambrósio deviation (BAD-D), stiffness parameter at first applanation (SPA1), back eccentricity (Becc), and maximum pachymetric progression index in order, while the CHAID model selected BAD-D, deformation amplitude ratio, SPA1, and Becc. The CART model allowed discrimination between normal and SKC eyes with 92.2% accuracy, which was higher than that of the CHAID model (88.3%), BAD-D (82.0%), Corvis biomechanical index (CBI, 77.3%), and tomographic and biomechanical index (TBI, 78.1%). The discriminating performance of the CART model was validated with 92.4% accuracy, while the CHAID model was validated with 86.4% accuracy in the validation database. Thus, the CART model using tomography and biomechanical imaging was an excellent model for SKC screening and provided easy-to-understand discriminating rules.

scholarly journals SME Default Prediction Framework with the Effective Use of External Public Credit Data

2020 ◽  
Vol 12 (18) ◽  
pp. 7575
Author(s):  
Zhichao Luo ◽  
Pingyu Hsu ◽  
Ni Xu
Keyword(s):  
Prediction Models ◽  
Regression Tree ◽  
Financial Data ◽  
Classification And Regression Tree ◽  
Default Prediction ◽  
Public Credit ◽  
The Government ◽  
Credit Data ◽  
Effective Use ◽  
Cart Model

Traditional default prediction models mainly rely on financial data. However, financial data on small and medium-sized enterprises (SMEs) are difficult to obtain, and even when they are available, their opaqueness may hinder analysis. Therefore, traditional prediction models encounter serious problems when being utilized to predict the defaulting of SMEs. In this paper, a novel prediction framework utilizing only external public credit data is proposed. The external public credit data used include SMEs’ basic information (BI), credit information from the government (CIG), and court verdict information (CVI), which can be collected from publicly accessible websites. Records on 15,605 sample companies were collected from approximately 300,000 companies. Among them, 8183 have defaulted. The empirical data were applied to construct prediction models using logistic regression, the classification and regression tree (CART) model, and LightGBM. The best results achieved 0.87 accuracy and 0.92 area under receiver operating characteristic (AUC). The results show that the model only uses the external credit data proven to have significant predict ability, and CIG variables offer the best prediction capacities.

scholarly journals Multicenter Study of the Relationship between Carbapenem MIC Values and Clinical Outcome of Patients with Acinetobacter Bacteremia

2017 ◽  
Vol 61 (9) ◽  
Author(s):  
Ya-Sung Yang ◽  
Yung-Chih Wang ◽  
Shu-Chen Kuo ◽  
Chung-Ting Chen ◽  
Chang-Pan Liu ◽  
...  
Keyword(s):  
Susceptibility Testing ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Rank Test ◽  
Bivariate Analysis ◽  
Content Type ◽  
Medical Centers ◽  
Regression Tree Analysis ◽  
Log Rank Test ◽  
The Relationship

ABSTRACT The Clinical and Laboratory Standards Institute (CLSI) and the European Committee on Antimicrobial Susceptibility Testing (EUCAST) offer different recommendations for carbapenem MIC susceptibility breakpoints for Acinetobacter species. In addition, the clinical efficacy of the intermediate category remains uncertain. This study was designed to determine the optimal predictive breakpoints based on the survival of patients with Acinetobacter bacteremia treated with a carbapenem. We analyzed the 30-day mortality rates of 224 adults who received initial carbapenem monotherapy for the treatment of Acinetobacter bacteremia at 4 medical centers over a 5-year period, according to the carbapenem MICs of the initial isolates. The 30-day mortality was about 2-fold greater in patients whose isolates had carbapenem MICs of ≥8 mg/liter than in those with isolates with MICs of ≤4 mg/liter. The differences were significant by bivariate analysis (53.1% [60/113] versus 25.2% [28/111], respectively; P < 0.001) and on survival analysis by the log rank test (P < 0.001). Classification and regression tree analysis revealed a split between MICs of 4 and 8 mg/liter and predicted the same difference in mortality, with a P value of <0.001. Carbapenem treatment for Acinetobacter bacteremia caused by isolates with carbapenem MICs of ≥8 mg/liter was an independent predictor of 30-day mortality (odds ratio, 4.218; 95% confidence interval, 2.213 to 8.039; P < 0.001). This study revealed that patients with Acinetobacter bacteremia treated with a carbapenem had a more favorable outcome when the carbapenem MICs of their isolates were ≤4 mg/liter than those with MICs of ≥8 mg/liter.

scholarly journals Classification of Fatigued and Drunk Driving Based on Decision Tree Methods: A Simulator Study

2019 ◽  
Vol 16 (11) ◽  
pp. 1935 ◽  
Author(s):  
Ying Yao ◽  
Xiaohua Zhao ◽  
Hongji Du ◽  
Yunlong Zhang ◽  
Guohui Zhang ◽  
...  
Keyword(s):  
Decision Tree ◽  
Regression Tree ◽  
Drunk Driving ◽  
Driving Performance ◽  
Classification And Regression Tree ◽  
Vehicle Speed ◽  
Road Geometry ◽  
Lane Position ◽  
Straight Segments

It is a commonly known fact that both alcohol and fatigue impair driving performance. Therefore, the identification of fatigue and drinking status is very important. In this study, each of the 22 participants finished five driving tests in total. The control condition, serving as the benchmark in the five driving tests, refers to alert driving. The other four test conditions include driving with three blood alcohol content (BAC) levels (0.02%, 0.05%, and 0.08%) and driving in a fatigued state. The driving scenario included straight and curved roads. The straight roads connected the curved ones with radii of 200 m, 500 m, and 800 m with two turning directions (left and right). Driving performance indicators such as the average and standard deviation of longitudinal speed and lane position were selected to identify drunk driving and fatigued driving. In the process of identification, road geometry (straight segments, radius, and direction of curves) was also taken into account. Alert vs. abnormal and fatigued vs. drunk driving with various BAC levels were analyzed separately using the Classification and Regression Tree (CART) model, and the significance of the variables on the binary response variable was determined. The results showed that the decision tree could be used to distinguish normal driving from abnormal driving, fatigued driving, and drunk driving based on the indexes of vehicle speed and lane position at curves with different radii. The overall accuracy of classification of “alert” and “abnormal” driving was 90.9%, and that of “fatigued” and “drunk” driving was 94.4%. The accuracy was relatively low in identifying different BAC degrees. This experiment is designed to provide a reference for detecting dangerous driving states.

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