Decision Tree Modeling for Ranking Data

2010 ◽  
pp. 83-106 ◽  
Author(s):  
Philip L. H. Yu ◽  
Wai Ming Wan ◽  
Paul H. Lee
Keyword(s):  
Decision Tree ◽  
Ranking Data ◽  
Tree Modeling

scholarly journals Letter: Predicting Long-Term Outcomes After Poor-Grade Aneurysmal Subarachnoid Hemorrhage Using Decision Tree Modeling

Neurosurgery ◽  
2020 ◽  
Author(s):  
Jaskaran Singh Gosal ◽  
Kuntal Kanti Das ◽  
Deepak Khatri ◽  
Kamlesh Singh Bhaisora ◽  
Sanjay Behari
Keyword(s):  
Subarachnoid Hemorrhage ◽  
Decision Tree ◽  
Aneurysmal Subarachnoid Hemorrhage ◽  
Long Term Outcomes ◽  
Poor Grade ◽  
Tree Modeling

Decision Tree Modeling Using Integrated Multilevel Stochastic Networks

2006 ◽  
Vol 132 (12) ◽  
pp. 1254-1266 ◽  
Author(s):  
Mohamed Moussa ◽  
Janaka Ruwanpura ◽  
George Jergeas
Keyword(s):  
Decision Tree ◽  
Stochastic Networks ◽  
Tree Modeling

Prediction of preterm birth in symptomatic women using decision tree modeling for biomarkers

2008 ◽  
Vol 198 (4) ◽  
pp. 468.e1-468.e9 ◽  
Author(s):  
Jacquelyn L. Hill ◽  
M. Karen Campbell ◽  
Guang Yong Zou ◽  
John R.G. Challis ◽  
Gregor Reid ◽  
...  
Keyword(s):  
Preterm Birth ◽  
Decision Tree ◽  
Tree Modeling

scholarly journals Predictors of Delirium Incidence, Time to Onset, and Recurrence in a Mixed Medical-surgical ICU Population: A Secondary Analysis Using Cox and CHIAD Decision Tree Modeling

2020 ◽  
Author(s):  
Keivan Gohari Moghadam ◽  
Andrew C Miller ◽  
Farshid Rahimibashar ◽  
Mahmood Salesi ◽  
Sara Ashtari ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Decision Tree ◽  
Nursing Care ◽  
Family Engagement ◽  
Secondary Analysis ◽  
Staff Burnout ◽  
Tree Modeling ◽  
Icu Delirium ◽  
Care Requirements

Abstract Background: To address whether in intensive care unit (ICU) patients, which factors correlate with development of delirium (primary outcome), as well as more rapid delirium onset and recurrence (secondary outcomes).Methods: A retrospective secondary analysis of 4,200 patients was collected from two academic medical centers. Delirium was assessed with the Confusion Assessment Method for the Intensive Care Unit (CAM-ICU) in all patients. Univariate and multivariate Cox models, logistic regression analysis, and Chi-square Automatic Interaction Detector (CHAID) decision tree modeling were used to explore delirium risk factors.Results: Increased delirium risk as associated with exposed only to artificial light (AL) hazard ratio (HR) 1.84 (95% CI: 1.66-2.044, P<0.001), physical restraint application 1.11 (95% CI: 1.001-1.226, P=0.049), and high nursing care requirements (>8 hours per 8-hour shift) 1.18 (95% CI: 1.048-1.338, P=0.007). Delirium incidence was inversely associated with greater family engagement 0.092 (95% CI: 0.014-0.596, P=0.012), low staff burnout and anticipated turnover scores 0.093 (95% CI: 0.014-0.600, P=0.013), non-ICU length-of-stay (LOS)<15 days 0.725 (95% CI: 0.655-0.804, P<0.001), and ICU LOS ≤15 days 0.509 (95% CI: 0.456-0.567, P<0.001). CHAID modelling indicated that AL exposure and age <65 years conveyed a high risk of delirium incidence, whereas SOFA score ≤11, APACHE IV score >15 and natural light (NL) exposure were associated with moderate risk, and female sex were associated with low risk. More rapid time to delirium onset correlated with baseline sleep disturbance (P=0.049), high nursing care requirements (P=0.019), and prolonged ICU and non-ICU hospital LOS (P<0.001). Delirium recurrence correlated with age>65 years (HR 2.198; %95 CI: 1.101-4.388, P=0.026) and high nursing care requirements (HR 1.978, 95% CI: 1.096-3.569), with CHAID modeling identifying AL exposure (P<0.001) and age >65 years (P=0.032) as predictive variables.Conclusion: Development of ICU delirium correlated with application of physical restraints, high nursing care requirements, prolonged ICU and non-ICU LOS, exposure exclusively to AL (rather than natural), less family engagement, and greater staff burnout and anticipated turnover scores. ICU delirium occurred more rapidly in patients with baseline sleep disturbance, and recurrence correlated with presence of delirium on ICU admission, exclusive AL exposure, and high nursing care requirements.

Analysis of Factors Affecting Bus Lane Operation in Shanghai, China, Based on Decision Tree Modeling

CICTP 2017 ◽  
2018 ◽  
Author(s):  
Linghui He ◽  
Weifeng Li ◽  
Chuan Chen ◽  
Dongyuan Yang
Keyword(s):  
Decision Tree ◽  
Factors Affecting ◽  
Tree Modeling ◽  
Bus Lane

Item Reduction of the Boston Carpal Tunnel Questionnaire Using Decision Tree Modeling

2019 ◽  
Vol 100 (12) ◽  
pp. 2308-2313 ◽  
Author(s):  
Miguel C. Jansen ◽  
Mark J. van der Oest ◽  
Harm P. Slijper ◽  
Jarry T. Porsius ◽  
Ruud W. Selles
Keyword(s):  
Decision Tree ◽  
Carpal Tunnel ◽  
Item Reduction ◽  
Tree Modeling ◽  
Boston Carpal Tunnel Questionnaire ◽  
Carpal Tunnel Questionnaire

Ethnographic Decision Tree Modeling

1989 ◽  
Author(s):  
Christina H. Gladwin
Keyword(s):  
Decision Tree ◽  
Tree Modeling
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