The Experts below are selected from a list of 270 Experts worldwide ranked by ideXlab platform
Thomas A. Schwann - One of the best experts on this subject based on the ideXlab platform.
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Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery
Journal of Clinical Monitoring and Computing, 2013Co-Authors: Milo Engoren, Robert H. Habib, John J. Dooner, Thomas A. SchwannAbstract:As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients were divided into separate Construction and Validation populations. Using 88 variables, logistic regression, genetic programs, and artificial neural nets were used to develop predictive models. Models were first constructed and tested on the Construction populations, then validated on the Validation population. Areas under the receiver operator characteristic curves (AU ROC) were used to compare the models. Two hundred and two patients (7.6 %) in the 2,644 patient Construction Group and 216 (8.0 %) of the 2,711 patient Validation Group were re-admitted within 30 days of CABG surgery. Logistic regression predicted readmission with AU ROC = .675 ± .021 in the Construction Group. Genetic programs significantly improved the accuracy, AU ROC = .767 ± .001, p
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Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery
Journal of Clinical Monitoring and Computing, 2013Co-Authors: Milo Engoren, Robert H. Habib, John J. Dooner, Thomas A. SchwannAbstract:As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients were divided into separate Construction and Validation populations. Using 88 variables, logistic regression, genetic programs, and artificial neural nets were used to develop predictive models. Models were first constructed and tested on the Construction populations, then validated on the Validation population. Areas under the receiver operator characteristic curves (AU ROC) were used to compare the models. Two hundred and two patients (7.6 %) in the 2,644 patient Construction Group and 216 (8.0 %) of the 2,711 patient Validation Group were re-admitted within 30 days of CABG surgery. Logistic regression predicted readmission with AU ROC = .675 ± .021 in the Construction Group. Genetic programs significantly improved the accuracy, AU ROC = .767 ± .001, p < .001). Artificial neural nets were less accurate with AU ROC = 0.597 ± .001 in the Construction Group. Predictive accuracy of all three techniques fell in the Validation Group. However, the accuracy of genetic programming (AU ROC = .654 ± .001) was still trivially but statistically non-significantly better than that of the logistic regression (AU ROC = .644 ± .020, p = .61). Genetic programming and logistic regression provide alternative methods to predict readmission that are similarly accurate.
Milo Engoren - One of the best experts on this subject based on the ideXlab platform.
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Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery
Journal of Clinical Monitoring and Computing, 2013Co-Authors: Milo Engoren, Robert H. Habib, John J. Dooner, Thomas A. SchwannAbstract:As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients were divided into separate Construction and Validation populations. Using 88 variables, logistic regression, genetic programs, and artificial neural nets were used to develop predictive models. Models were first constructed and tested on the Construction populations, then validated on the Validation population. Areas under the receiver operator characteristic curves (AU ROC) were used to compare the models. Two hundred and two patients (7.6 %) in the 2,644 patient Construction Group and 216 (8.0 %) of the 2,711 patient Validation Group were re-admitted within 30 days of CABG surgery. Logistic regression predicted readmission with AU ROC = .675 ± .021 in the Construction Group. Genetic programs significantly improved the accuracy, AU ROC = .767 ± .001, p
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Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery
Journal of Clinical Monitoring and Computing, 2013Co-Authors: Milo Engoren, Robert H. Habib, John J. Dooner, Thomas A. SchwannAbstract:As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients were divided into separate Construction and Validation populations. Using 88 variables, logistic regression, genetic programs, and artificial neural nets were used to develop predictive models. Models were first constructed and tested on the Construction populations, then validated on the Validation population. Areas under the receiver operator characteristic curves (AU ROC) were used to compare the models. Two hundred and two patients (7.6 %) in the 2,644 patient Construction Group and 216 (8.0 %) of the 2,711 patient Validation Group were re-admitted within 30 days of CABG surgery. Logistic regression predicted readmission with AU ROC = .675 ± .021 in the Construction Group. Genetic programs significantly improved the accuracy, AU ROC = .767 ± .001, p < .001). Artificial neural nets were less accurate with AU ROC = 0.597 ± .001 in the Construction Group. Predictive accuracy of all three techniques fell in the Validation Group. However, the accuracy of genetic programming (AU ROC = .654 ± .001) was still trivially but statistically non-significantly better than that of the logistic regression (AU ROC = .644 ± .020, p = .61). Genetic programming and logistic regression provide alternative methods to predict readmission that are similarly accurate.
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Evaluation of Capnography Using a Genetic Algorithm To Predict Paco2
Chest, 2005Co-Authors: Milo Engoren, Michael C. Plewa, David O'hara, Jeffrey A. KlineAbstract:Introduction Noninvasive estimates of Paco 2 are usually done by measuring exhaled carbon dioxide at end-expiration (Petco 2 ). While commonly used in studies involving healthy patients, it is less useful in sicker patients. Conditions that affect the terminal dead space and hence the accuracy of Petco 2 as a surrogate for Paco 2 may also affect other components of the capnogram. A genetic algorithm is a computer technique for discovering relationships between variables. The purpose of this study was to use a genetic algorithm to improve the precision of Paco 2 prediction in comparison to Petco 2 . Methods Inspiratory and expiratory volumes were measured and analyzed by the computerized capnogram. Data were recorded for 2 min. Within 5 min of recording the capnograms, arterial blood gases were obtained. After excluding artifact and incomplete capnograms, five of the remaining breaths from each patient were selected. A genetic algorithm, constructed in postfix notation, consisted of 1,000 chromosomes with genes randomly selected from the 11 capnographic data fields and mathematical operators. The algorithm was constructed on 400 breaths from 83 randomly selected patients (Construction Group) and tested on 160 breaths from the remaining 32 patients (test Group). Results For the Construction Group, the bias and precision between Petco 2 and Paco 2 were 4.3 ± 4.9 mm Hg (mean ± SD). For the 160 breaths in the test Group, Petco 2 predicted Paco 2 with bias and precision of 2.9 ± 4.2 mm Hg. The best chromosome found by the genetic algorithm was (10 × 5 + 5 × 5 × 5)/(10 × 10) × Petco 2 – (5 × 5 × 10 + 5 × 5)/(10 × 10) × int time + 2 × 2 × 2 × 2 + (2 × 2)/10, which reduces to 0.65 × Petco 2 – 2.75 × int time + 16.4. This produced a bias and precision of 0.9 ± 4.1 mm Hg in the Construction Group and 0 ± 3.7 mm Hg in the test Group (p Conclusions In this study of nonintubated emergency department patients, a genetic algorithm produced an improvement in bias and precision of Paco 2 prediction.
Robert H. Habib - One of the best experts on this subject based on the ideXlab platform.
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Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery
Journal of Clinical Monitoring and Computing, 2013Co-Authors: Milo Engoren, Robert H. Habib, John J. Dooner, Thomas A. SchwannAbstract:As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients were divided into separate Construction and Validation populations. Using 88 variables, logistic regression, genetic programs, and artificial neural nets were used to develop predictive models. Models were first constructed and tested on the Construction populations, then validated on the Validation population. Areas under the receiver operator characteristic curves (AU ROC) were used to compare the models. Two hundred and two patients (7.6 %) in the 2,644 patient Construction Group and 216 (8.0 %) of the 2,711 patient Validation Group were re-admitted within 30 days of CABG surgery. Logistic regression predicted readmission with AU ROC = .675 ± .021 in the Construction Group. Genetic programs significantly improved the accuracy, AU ROC = .767 ± .001, p
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Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery
Journal of Clinical Monitoring and Computing, 2013Co-Authors: Milo Engoren, Robert H. Habib, John J. Dooner, Thomas A. SchwannAbstract:As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients were divided into separate Construction and Validation populations. Using 88 variables, logistic regression, genetic programs, and artificial neural nets were used to develop predictive models. Models were first constructed and tested on the Construction populations, then validated on the Validation population. Areas under the receiver operator characteristic curves (AU ROC) were used to compare the models. Two hundred and two patients (7.6 %) in the 2,644 patient Construction Group and 216 (8.0 %) of the 2,711 patient Validation Group were re-admitted within 30 days of CABG surgery. Logistic regression predicted readmission with AU ROC = .675 ± .021 in the Construction Group. Genetic programs significantly improved the accuracy, AU ROC = .767 ± .001, p < .001). Artificial neural nets were less accurate with AU ROC = 0.597 ± .001 in the Construction Group. Predictive accuracy of all three techniques fell in the Validation Group. However, the accuracy of genetic programming (AU ROC = .654 ± .001) was still trivially but statistically non-significantly better than that of the logistic regression (AU ROC = .644 ± .020, p = .61). Genetic programming and logistic regression provide alternative methods to predict readmission that are similarly accurate.
John J. Dooner - One of the best experts on this subject based on the ideXlab platform.
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Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery
Journal of Clinical Monitoring and Computing, 2013Co-Authors: Milo Engoren, Robert H. Habib, John J. Dooner, Thomas A. SchwannAbstract:As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients were divided into separate Construction and Validation populations. Using 88 variables, logistic regression, genetic programs, and artificial neural nets were used to develop predictive models. Models were first constructed and tested on the Construction populations, then validated on the Validation population. Areas under the receiver operator characteristic curves (AU ROC) were used to compare the models. Two hundred and two patients (7.6 %) in the 2,644 patient Construction Group and 216 (8.0 %) of the 2,711 patient Validation Group were re-admitted within 30 days of CABG surgery. Logistic regression predicted readmission with AU ROC = .675 ± .021 in the Construction Group. Genetic programs significantly improved the accuracy, AU ROC = .767 ± .001, p
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Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery
Journal of Clinical Monitoring and Computing, 2013Co-Authors: Milo Engoren, Robert H. Habib, John J. Dooner, Thomas A. SchwannAbstract:As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients were divided into separate Construction and Validation populations. Using 88 variables, logistic regression, genetic programs, and artificial neural nets were used to develop predictive models. Models were first constructed and tested on the Construction populations, then validated on the Validation population. Areas under the receiver operator characteristic curves (AU ROC) were used to compare the models. Two hundred and two patients (7.6 %) in the 2,644 patient Construction Group and 216 (8.0 %) of the 2,711 patient Validation Group were re-admitted within 30 days of CABG surgery. Logistic regression predicted readmission with AU ROC = .675 ± .021 in the Construction Group. Genetic programs significantly improved the accuracy, AU ROC = .767 ± .001, p < .001). Artificial neural nets were less accurate with AU ROC = 0.597 ± .001 in the Construction Group. Predictive accuracy of all three techniques fell in the Validation Group. However, the accuracy of genetic programming (AU ROC = .654 ± .001) was still trivially but statistically non-significantly better than that of the logistic regression (AU ROC = .644 ± .020, p = .61). Genetic programming and logistic regression provide alternative methods to predict readmission that are similarly accurate.
H. H. M. Boer - One of the best experts on this subject based on the ideXlab platform.
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Intestinal anastomotic healing in the absence of suture material: an experimental study in rats
International Journal of Colorectal Disease, 1991Co-Authors: W. J. B. Mastboom, T. Hendriks, H. H. M. BoerAbstract:In order to investigate the influence of sutures on intestinal anastomotic healing, 48 rats underwent both ileal and colonic resection. In 24 rats all intestinal sutures were removed 30 min after anastomotic Construction (Group 1), while in the remaining animals (Group 2) the sutures were left in place. Bursting pressures and collagen (hydroxyproline) levels in anastomotic segments were measured 1, 3, and 7 days after operation. Two lethal ileal dehiscences and 9 anastomotic abscesses (5 ileal and 4 colonic) occurred in Group 1, while in Group 2 there were 3 ileal anastomotic abscesses. On the first day after operation, bursting pressures were significantly lower in sutureless ileal and colonic than in sutured anastomoses. During the post-operative course, changes in collagen concentrations in ileal and colonic segments did not differ between the Groups. Thus, sutures are only essential in providing anastomotic strength during the immediate post-operative period, but do not seem to affect post-operative collagen metabolism. Dans le but d'étudier l'influence des sutures sur la cicatrisation des anastomoses intestinales, 48 rats ont subi une résection à la fois iléale et colique. 24 rats ont eu une résection de leur suture intestinale 30 minutes après la réalisation de l'anastomose, tandis que chez les animaux restants les sutures étaient laissé en place. 3 à 7 jours après l'opération la pression de rupture et les taux de collagène dans les segments anastomotiques ont été mesurés. Des fistules iléales mortelles et 9 abcés anastomotiques (5 iléaux et 4 coliques) sont survenus dans le Groupe expℰimental contre trois abcés anastomotiques dans le Groupe de contrôle. La pression de rupture était significativement plus basse au niveau des anastomoses à la fois iléales et coliques sans suture mais seulement le premier jour après l'opération. Les modifications postopératoires des concentrations de collagène dans les segments iléaux ou coliques ne différaient pas entre les deux Groupes. Ainsi les sutures ont seulement un rôle essentiel en renforçant l'anastomose durant la période post-opératoire tout à fait initiale mais ne semblent pas affecter le métabolisme post-opératoire du collagène.