The Experts below are selected from a list of 35184 Experts worldwide ranked by ideXlab platform
Peter J. D. Andrews - One of the best experts on this subject based on the ideXlab platform.
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Predicting recovery in patients suffering from traumatic brain injury by using admission variables and physiological data: a comparison between Decision Tree Analysis and logistic regression
Journal of Neurosurgery, 2002Co-Authors: Peter J. D. Andrews, Derek Sleeman, Andrew Mcquatt, Vincent Corruble, Patricia A. Jones, Tim Howells, Carol S. A. MacmillanAbstract:Object. Decision Tree Analysis highlights patient subgroups and critical values in variables assessed. Importantly, the results are visually informative and often present clear clinical interpretation about risk factors faced by patients in these subgroups. The aim of this prospective study was to compare results of logistic regression with those of Decision Tree Analysis of an observational, head-injury data set, including a wide range of secondary insults and 12-month outcomes.Methods. One hundred twenty-four adult head-injured patients were studied during their stay in an intensive care unit by using a computerized data collection system. Verified values falling outside threshold limits were analyzed according to insult grade and duration with the aid of logistic regression. A Decision Tree was automatically produced from root node to target classes (Glasgow Outcome Scale [GOS] score).Among 69 patients, in whom eight insult categories could be assessed, outcome at 12 months was analyzed using logistic regression to determine the relative influence of patient age, admission Glasgow Coma Scale score, Injury Severity Score (ISS), pupillary response on admission, and insult duration. The most significant predictors of mortality in this patient set were duration of hypotensive, pyrexic, and hypoxemic insults. When good and poor outcomes were compared, hypotensive insults and pupillary response on admission were significant.Using Decision Tree Analysis, the authors found that hypotension and low cerebral perfusion pressure (CPP) are the best predictors of death, with a 9.2% improvement in predictive accuracy (PA) over that obtained by simply predicting the largest outcome category as the outcome for each patient. Hypotension was a significant predictor of poor outcome (GOS Score 1–3). Low CPP, patient age, hypocarbia, and pupillary response were also good predictors of outcome (good/poor), with a 5.1% improvement in PA. In certain subgroups of patients pyrexia was a predictor of good outcome.Conclusions. Decision Tree Analysis confirmed some of the results of logistic regression and challenged others. This investigation shows that there is knowledge to be gained from analyzing observational data with the aid of Decision Tree Analysis.
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predicting recovery in patients suffering from traumatic brain injury by using admission variables and physiological data a comparison between Decision Tree Analysis and logistic regression
Journal of Neurosurgery, 2002Co-Authors: Peter J. D. Andrews, Vincent Corruble, Patricia A. Jones, Derek Sleema, Patrick F X Statham, Andrew Mcqua, Timothy Howells, Carol S A MacmillaAbstract:Object. Decision Tree Analysis highlights patient subgroups and critical values in variables assessed. Importantly, the results are visually informative and often present clear clinical interpretation about risk factors faced by patients in these subgroups. The aim of this prospective study was to compare results of logistic regression with those of Decision Tree Analysis of an observational, head-injury data set, including a wide range of secondary insults and 12-month outcomes. Methods. One hundred twenty-four adult head-injured patients were studied during their stay in an intensive care unit by using a computerized data collection system. Verified values falling outside threshold limits were analyzed according to insult grade and duration with the aid of logistic regression. A Decision Tree was automatically produced from root node to target classes (Glasgow Outcome Scale [GOS] score). Among 69 patients, in whom eight insult categories could be assessed, outcome at 12 months was analyzed using logisti...
Jinsoo Hwang - One of the best experts on this subject based on the ideXlab platform.
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Erratum to: Understanding Japanese tourists' shopping preferences using the Decision Tree Analysis method. Tourism Management, 32 (2011) 544-554
Tourism Management, 2012Co-Authors: Samuel Seongseop Kim, Dallen J. Timothy, Jinsoo HwangAbstract:Erratum to: Understanding Japanese tourists’ shopping preferences using the Decision Tree Analysis method. Tourism Management, 32 (2011) 544e554 Samuel Seongseop Kim *, Dallen J. Timothy , Jinsoo Hwang c a School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China b Tourism Development and Management, School of Community Resources and Development, Arizona State University, 411 N. Central Avenue, Suite 550, Phoenix, AZ 85004, USA Department of Hospitality Management and Dietetics, Kansas State University, KS, USA
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Understanding Japanese tourists’ shopping preferences using the Decision Tree Analysis method
Tourism Management, 2011Co-Authors: Samuel Seongseop Kim, Dallen J. Timothy, Jinsoo HwangAbstract:This study was designed to assess the factors affecting Japanese tourists’ shopping preference and intention to revisit Korea. The analytical method applied in this study was Decision Tree Analysis, which is under-utilized in tourism studies. A total of 300 questionnaires were collected on the basis of on-site survey method and used for data Analysis. Among interesting findings, three groups including ‘respondents who were satisfied, accompanied, and spent US$50–300 on shopping’, ‘respondents who were satisfied, accompanied, and had a shopping expenditure of US$300–1000’ and ‘respondents who were satisfied, accompanied, and had a shopping expenditure of US$1000–5000,’ showed a high level of intention to return to Korea for the purpose of shopping. In addition, two groups ‘those who were interested in shopping in Korea, preferred a shopping mall as a shopping destination, and had an educational level of below high school’ and ‘those who were interested in shopping in Korea, preferred a shopping mall as a shopping destination, and had an educational level of a college graduate or above’ showed a higher level interest in merchandise than in shopping venue attractiveness.
Menno R. Germans - One of the best experts on this subject based on the ideXlab platform.
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Decision Tree Analysis in subarachnoid hemorrhage prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using Decision Tree Analysis
Journal of Neurosurgery, 2018Co-Authors: Isabel Charlotte Hostettler, Carl Muroi, Johannes Konstantin Richter, Josef Schmid, Marian Christoph Neidert, Martin Seule, Oliver Boss, Athina Pangalu, Menno R. GermansAbstract:OBJECTIVEThe aim of this study was to create prediction models for outcome parameters by Decision Tree Analysis based on clinical and laboratory data in patients with aneurysmal subarachnoid hemorrhage (aSAH).METHODSThe database consisted of clinical and laboratory parameters of 548 patients with aSAH who were admitted to the Neurocritical Care Unit, University Hospital Zurich. To examine the model performance, the cohort was randomly divided into a derivation cohort (60% [n = 329]; training data set) and a validation cohort (40% [n = 219]; test data set). The classification and regression Tree prediction algorithm was applied to predict death, functional outcome, and ventriculoperitoneal (VP) shunt dependency. Chi-square automatic interaction detection was applied to predict delayed cerebral infarction on days 1, 3, and 7.RESULTSThe overall mortality was 18.4%. The accuracy of the Decision Tree models was good for survival on day 1 and favorable functional outcome at all time points, with a difference between the training and test data sets of < 5%. Prediction accuracy for survival on day 1 was 75.2%. The most important differentiating factor was the interleukin-6 (IL-6) level on day 1. Favorable functional outcome, defined as Glasgow Outcome Scale scores of 4 and 5, was observed in 68.6% of patients. Favorable functional outcome at all time points had a prediction accuracy of 71.1% in the training data set, with procalcitonin on day 1 being the most important differentiating factor at all time points. A total of 148 patients (27%) developed VP shunt dependency. The most important differentiating factor was hyperglycemia on admission.CONCLUSIONSThe multiple variable Analysis capability of Decision Trees enables exploration of dependent variables in the context of multiple changing influences over the course of an illness. The Decision Tree currently generated increases awareness of the early systemic stress response, which is seemingly pertinent for prognostication.
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Decision Tree Analysis in subarachnoid hemorrhage: prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using Decision Tree Analysis.
Journal of Neurosurgery, 2018Co-Authors: Isabel Charlotte Hostettler, Carl Muroi, Johannes Konstantin Richter, Josef Schmid, Marian Christoph Neidert, Martin Seule, Oliver Boss, Athina Pangalu, Menno R. Germans, Emanuela KellerAbstract:OBJECTIVEThe aim of this study was to create prediction models for outcome parameters by Decision Tree Analysis based on clinical and laboratory data in patients with aneurysmal subarachnoid hemorrhage (aSAH).METHODSThe database consisted of clinical and laboratory parameters of 548 patients with aSAH who were admitted to the Neurocritical Care Unit, University Hospital Zurich. To examine the model performance, the cohort was randomly divided into a derivation cohort (60% [n = 329]; training data set) and a validation cohort (40% [n = 219]; test data set). The classification and regression Tree prediction algorithm was applied to predict death, functional outcome, and ventriculoperitoneal (VP) shunt dependency. Chi-square automatic interaction detection was applied to predict delayed cerebral infarction on days 1, 3, and 7.RESULTSThe overall mortality was 18.4%. The accuracy of the Decision Tree models was good for survival on day 1 and favorable functional outcome at all time points, with a difference be...
Martin Seule - One of the best experts on this subject based on the ideXlab platform.
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Decision Tree Analysis in subarachnoid hemorrhage prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using Decision Tree Analysis
Journal of Neurosurgery, 2018Co-Authors: Isabel Charlotte Hostettler, Carl Muroi, Johannes Konstantin Richter, Josef Schmid, Marian Christoph Neidert, Martin Seule, Oliver Boss, Athina Pangalu, Menno R. GermansAbstract:OBJECTIVEThe aim of this study was to create prediction models for outcome parameters by Decision Tree Analysis based on clinical and laboratory data in patients with aneurysmal subarachnoid hemorrhage (aSAH).METHODSThe database consisted of clinical and laboratory parameters of 548 patients with aSAH who were admitted to the Neurocritical Care Unit, University Hospital Zurich. To examine the model performance, the cohort was randomly divided into a derivation cohort (60% [n = 329]; training data set) and a validation cohort (40% [n = 219]; test data set). The classification and regression Tree prediction algorithm was applied to predict death, functional outcome, and ventriculoperitoneal (VP) shunt dependency. Chi-square automatic interaction detection was applied to predict delayed cerebral infarction on days 1, 3, and 7.RESULTSThe overall mortality was 18.4%. The accuracy of the Decision Tree models was good for survival on day 1 and favorable functional outcome at all time points, with a difference between the training and test data sets of < 5%. Prediction accuracy for survival on day 1 was 75.2%. The most important differentiating factor was the interleukin-6 (IL-6) level on day 1. Favorable functional outcome, defined as Glasgow Outcome Scale scores of 4 and 5, was observed in 68.6% of patients. Favorable functional outcome at all time points had a prediction accuracy of 71.1% in the training data set, with procalcitonin on day 1 being the most important differentiating factor at all time points. A total of 148 patients (27%) developed VP shunt dependency. The most important differentiating factor was hyperglycemia on admission.CONCLUSIONSThe multiple variable Analysis capability of Decision Trees enables exploration of dependent variables in the context of multiple changing influences over the course of an illness. The Decision Tree currently generated increases awareness of the early systemic stress response, which is seemingly pertinent for prognostication.
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Decision Tree Analysis in subarachnoid hemorrhage: prediction of outcome parameters during the course of aneurysmal subarachnoid hemorrhage using Decision Tree Analysis.
Journal of Neurosurgery, 2018Co-Authors: Isabel Charlotte Hostettler, Carl Muroi, Johannes Konstantin Richter, Josef Schmid, Marian Christoph Neidert, Martin Seule, Oliver Boss, Athina Pangalu, Menno R. Germans, Emanuela KellerAbstract:OBJECTIVEThe aim of this study was to create prediction models for outcome parameters by Decision Tree Analysis based on clinical and laboratory data in patients with aneurysmal subarachnoid hemorrhage (aSAH).METHODSThe database consisted of clinical and laboratory parameters of 548 patients with aSAH who were admitted to the Neurocritical Care Unit, University Hospital Zurich. To examine the model performance, the cohort was randomly divided into a derivation cohort (60% [n = 329]; training data set) and a validation cohort (40% [n = 219]; test data set). The classification and regression Tree prediction algorithm was applied to predict death, functional outcome, and ventriculoperitoneal (VP) shunt dependency. Chi-square automatic interaction detection was applied to predict delayed cerebral infarction on days 1, 3, and 7.RESULTSThe overall mortality was 18.4%. The accuracy of the Decision Tree models was good for survival on day 1 and favorable functional outcome at all time points, with a difference be...
David X Cifu - One of the best experts on this subject based on the ideXlab platform.
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clinical elements that predict outcome after traumatic brain injury a prospective multicenter recursive partitioning Decision Tree Analysis
Journal of Neurotrauma, 2005Co-Authors: Allen W Brown, James F Malec, Robyn L Mcclelland, Nancy N Diehl, Jeffrey Englander, David X CifuAbstract:Traumatic brain injury (TBI) often presents clinicians with a complex combination of clinical elements that can confound treatment and make outcome prediction challenging. Predictive models have commonly used acute physiological variables and gross clinical measures to predict mortality and basic outcome endpoints. The primary goal of this study was to consider all clinical elements available concerning a survivor of TBI admitted for inpatient rehabilitation, and identify those factors that predict disability, need for supervision, and productive activity one year after injury. The Traumatic Brain Injury Model Systems (TBIMS) database was used for Decision Tree Analysis using recursive partitioning (n = 3463). Outcome measures included the Functional Independence Measure™, the Disability Rating Scale, the Supervision Rating Scale, and a measure of productive activity. Predictor variables included all physical examination elements, measures of injury severity (initial Glasgow Coma Scale score, duration of ...
