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

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.

scholarly journals Influence of Wind Turbines on Farmlands’ Value: Exploring the Behaviour of a Rural Community through the Decision Tree

2021 ◽  
Vol 13 (17) ◽  
pp. 9630
Author(s):  
Giovanni Ottomano Palmisano ◽  
Annalisa De Boni ◽  
Rocco Roma ◽  
Claudio Acciani
Keyword(s):  
Rural Communities ◽  
Decision Tree ◽  
Rural Community ◽  
Wind Turbines ◽  
Rural Areas ◽  
Wind Farm ◽  
Wind Farms ◽  
Market Value ◽  
Factors Affecting ◽  
Main Factors

The relationship between wind energy and rural areas leads to the controversial debate on the effects declared by rural communities after wind farms or single turbines are operative. The literature on this topic lacks dedicated studies analysing how the behaviour of rural communities towards wind turbines can affect the market value of farmlands. This research aims to examine to the extent to which the easement of wind turbines can influence the market value of farmlands in terms of willingness to pay (WTP) by a small rural community, and to identify the main factors affecting the WTP. Starting from data collected via face-to-face interviews, a decision tree is then applied to investigate the WTP for seven types of farmland in a rural town of Puglia Region (Southern Italy) hosting a wind farm. Results of the interviews show a broad acceptance of the wind farm, while the decision tree classification shows a significant reduction of WTP for all farmlands. The main factors influencing the WTP are the education level, the possibility to increase the income, the concerns for impacts on human health and for maintenance workmen. National and local policy measures have to be put in place to inform rural communities about the ‘magnitude’ of the effects they identified as crucial, so that policy-makers and private bodies will contribute to make the farmland market more equitable.

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