An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests.

Background: The introduction and rapid transmission of SARS CoV2 in the United States resulted in implementation of methods to assess, mitigate and contain the resulting COVID-19 disease based on limited knowledge. Screening for testing has been based on symptoms typically observed in inpatients, yet outpatient symptom complexes may differ. Methods: Classification and regression trees (CART) recursive partitioning created a decision tree classifying enrollees into laboratory-confirmed cases and non-cases. Demographic and symptom data from patients ages 18-87 years who were enrolled from March 29-April 26, 2020 were included. Presence or absence of SARSCoV2 was the target variable. Results: Of 736 tested, 55 were positive for SARS-CoV2. Cases significantly more often reported chills, loss of taste/smell, diarrhea, fever, nausea/vomiting and contact with a COVID-19 case, but less frequently reported shortness of breath and sore throat. A 7-terminal node tree with a sensitivity of 96% and specificity of 53%, and an AUC of 78% was developed. The positive predictive value for this tree was 14% while the negative predictive value was 99%. Almost half (44%) of the participants could be ruled out as likely non-cases without testing. Discussion: Among those referred for testing, negative responses to three questions could classify about half of tested persons with low risk for SARS-CoV2 and would save limited testing resources. These questions are: was the patient in contact with a COVID-19 case? Has the patient experienced 1) a loss of taste or smell; or 2) nausea or vomiting? The outpatient symptoms of COVID-19 appear to be broader than the well-known inpatient syndrome.

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Surveying the Forests and Sampling the Trees: An overview of Classification and Regression Trees and Random Forests with applications in Survey Research

Survey Practice ◽

10.29115/sp-2018-0003 ◽

2018 ◽

Vol 11 (1) ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Trent D. Buskirk

Keyword(s):

Survey Research ◽

Random Forests ◽

Regression Trees ◽

Classification And Regression Trees ◽

Classification And Regression

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Preoperative hematocrit and platelet count are associated with blood loss during spinal fusion for children with neuromuscular scoliosis

Journal of Perioperative Practice ◽

10.1177/1750458920962634 ◽

2021 ◽

pp. 175045892096263

Author(s):

Margaret O Lewen ◽

Jay Berry ◽

Connor Johnson ◽

Rachael Grace ◽

Laurie Glader ◽

...

Keyword(s):

Platelet Count ◽

Blood Loss ◽

Spinal Fusion ◽

Regression Trees ◽

Classification And Regression Trees ◽

Neuromuscular Scoliosis ◽

Independent Variables ◽

Estimated Blood Loss ◽

Laboratory Results ◽

Classification And Regression

Aim To assess the relationship of preoperative hematology laboratory results with intraoperative estimated blood loss and transfusion volumes during posterior spinal fusion for pediatric neuromuscular scoliosis. Methods Retrospective chart review of 179 children with neuromuscular scoliosis undergoing spinal fusion at a tertiary children’s hospital between 2012 and 2017. The main outcome measure was estimated blood loss. Secondary outcomes were volumes of packed red blood cells, fresh frozen plasma, and platelets transfused intraoperatively. Independent variables were preoperative blood counts, coagulation studies, and demographic and surgical characteristics. Relationships between estimated blood loss, transfusion volumes, and independent variables were assessed using bivariable analyses. Classification and Regression Trees were used to identify variables most strongly correlated with outcomes. Results In bivariable analyses, increased estimated blood loss was significantly associated with higher preoperative hematocrit and lower preoperative platelet count but not with abnormal coagulation studies. Preoperative laboratory results were not associated with intraoperative transfusion volumes. In Classification and Regression Trees analysis, binary splits associated with the largest increase in estimated blood loss were hematocrit ≥44% vs. <44% and platelets ≥308 vs. <308 × 109/L. Conclusions Preoperative blood counts may identify patients at risk of increased bleeding, though do not predict intraoperative transfusion requirements. Abnormal coagulation studies often prompted preoperative intervention but were not associated with increased intraoperative bleeding or transfusion needs.

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Regional Mapping of Groundwater Potential in Ar Rub Al Khali, Arabian Peninsula Using the Classification and Regression Trees Model

Remote Sensing ◽

10.3390/rs13122300 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2300

Author(s):

Samy Elmahdy ◽

Tarig Ali ◽

Mohamed Mohamed

Keyword(s):

Machine Learning ◽

Regional Scale ◽

Regression Trees ◽

Classification And Regression Trees ◽

Groundwater Potential ◽

Machine Learning Algorithms ◽

Conditioning Factors ◽

Potential Mapping ◽

Classification And Regression ◽

Groundwater Potential Mapping

Mapping of groundwater potential in remote arid and semi-arid regions underneath sand sheets over a very regional scale is a challenge and requires an accurate classifier. The Classification and Regression Trees (CART) model is a robust machine learning classifier used in groundwater potential mapping over a very regional scale. Ten essential groundwater conditioning factors (GWCFs) were constructed using remote sensing data. The spatial relationship between these conditioning factors and the observed groundwater wells locations was optimized and identified by using the chi-square method. A total of 185 groundwater well locations were randomly divided into 129 (70%) for training the model and 56 (30%) for validation. The model was applied for groundwater potential mapping by using optimal parameters values for additive trees were 186, the value for the learning rate was 0.1, and the maximum size of the tree was five. The validation result demonstrated that the area under the curve (AUC) of the CART was 0.920, which represents a predictive accuracy of 92%. The resulting map demonstrated that the depressions of Mondafan, Khujaymah and Wajid Mutaridah depression and the southern gulf salt basin (SGSB) near Saudi Arabia, Oman and the United Arab Emirates (UAE) borders reserve fresh fossil groundwater as indicated from the observed lakes and recovered paleolakes. The proposed model and the new maps are effective at enhancing the mapping of groundwater potential over a very regional scale obtained using machine learning algorithms, which are used rarely in the literature and can be applied to the Sahara and the Kalahari Desert.

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