Ashish Nimgaonkar1,2,3, Dilip R Karnad4, S Sudarshan5, Lucila Ohno-Machado6,7, Isaac Kohane8,6. 1. Children's Hospital Informatics Program, Ender's Building, 5th Floor, 320 Longwood Avenue, Boston, Massachusetts, USA. ashish@chip.org. 2. Division of Health Sciences and Technology, Harvard University and MIT, Cambridge, Massachusetts, USA. ashish@chip.org. 3. School of Biomedical Engineering, Indian Institute of Technology, Powai, Mumbai, India. ashish@chip.org. 4. Medical Intensive Care Unit, Department of Medicine, KEM Hospital, Parel, Mumbai, India. 5. Department of Computer Science and Engineering, Indian Institute of Technology, Powai, Mumbai, India. 6. Division of Health Sciences and Technology, Harvard University and MIT, Cambridge, Massachusetts, USA. 7. Decision Systems Group, Brigham and Women's Hospital, Boston, Massachusetts, USA. 8. Children's Hospital Informatics Program, Ender's Building, 5th Floor, 320 Longwood Avenue, Boston, Massachusetts, USA.
Abstract
OBJECTIVE: To compare hospital outcome prediction using an artificial neural network model, built on an Indian data set, with the APACHE II (Acute Physiology and Chronic Health Evaluation II) logistic regression model. DESIGN: Analysis of a database containing prospectively collected data. SETTING: Medical-neurological ICU of a university hospital in Mumbai, India. SUBJECTS: Two thousand sixty-two consecutive admissions between 1996 and 1998. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The 22 variables used to obtain day-1 APACHE II score and risk of death were recorded. Data from 1,962 patients were used to train the neural network using a back-propagation algorithm. Data from the remaining 1,000 patients were used for testing this model and comparing it with APACHE II. There were 337 deaths in these 1,000 patients; APACHE II predicted 246 deaths while the neural network predicted 336 deaths. Calibration, assessed by the Hosmer-Lemeshow statistic, was better with the neural network (H=22.4) than with APACHE II (H=123.5) and so was discrimination (area under receiver operating characteristic curve =0.87 versus 0.77, p=0.002). Analysis of information gain due to each of the 22 variables revealed that the neural network could predict outcome using only 15 variables. A new model using these 15 variables predicted 335 deaths, had calibration (H=27.7) and discrimination (area under receiver operating characteristic curve =0.88) which was comparable to the 22-variable model (p=0.87) and superior to the APACHE II equation (p<0.001). CONCLUSION: Artificial neural networks, trained on Indian patient data, used fewer variables and yet outperformed the APACHE II system in predicting hospital outcome.
OBJECTIVE: To compare hospital outcome prediction using an artificial neural network model, built on an Indian data set, with the APACHE II (Acute Physiology and Chronic Health Evaluation II) logistic regression model. DESIGN: Analysis of a database containing prospectively collected data. SETTING: Medical-neurological ICU of a university hospital in Mumbai, India. SUBJECTS: Two thousand sixty-two consecutive admissions between 1996 and 1998. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: The 22 variables used to obtain day-1 APACHE II score and risk of death were recorded. Data from 1,962 patients were used to train the neural network using a back-propagation algorithm. Data from the remaining 1,000 patients were used for testing this model and comparing it with APACHE II. There were 337 deaths in these 1,000 patients; APACHE II predicted 246 deaths while the neural network predicted 336 deaths. Calibration, assessed by the Hosmer-Lemeshow statistic, was better with the neural network (H=22.4) than with APACHE II (H=123.5) and so was discrimination (area under receiver operating characteristic curve =0.87 versus 0.77, p=0.002). Analysis of information gain due to each of the 22 variables revealed that the neural network could predict outcome using only 15 variables. A new model using these 15 variables predicted 335 deaths, had calibration (H=27.7) and discrimination (area under receiver operating characteristic curve =0.88) which was comparable to the 22-variable model (p=0.87) and superior to the APACHE II equation (p<0.001). CONCLUSION: Artificial neural networks, trained on Indian patient data, used fewer variables and yet outperformed the APACHE II system in predicting hospital outcome.
Authors: Peter Andrews; Elie Azoulay; Massimo Antonelli; Laurent Brochard; Christian Brun-Buisson; Geoffrey Dobb; Jean-Yves Fagon; Herwig Gerlach; Johan Groeneveld; Jordi Mancebo; Philipp Metnitz; Stefano Nava; Jerome Pugin; Michael Pinsky; Peter Radermacher; Christian Richard; Robert Tasker; Benoit Vallet Journal: Intensive Care Med Date: 2005-02-18 Impact factor: 17.440
Authors: Rui P Moreno; Philipp G H Metnitz; Eduardo Almeida; Barbara Jordan; Peter Bauer; Ricardo Abizanda Campos; Gaetano Iapichino; David Edbrooke; Maurizia Capuzzo; Jean-Roger Le Gall Journal: Intensive Care Med Date: 2005-08-17 Impact factor: 17.440