Document Detail


Prediction of postoperative morbidity after lung resection using an artificial neural network ensemble.
MedLine Citation:
PMID:  14684265     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
OBJECTIVE: To propose an ensemble model of artificial neural networks (ANNs) to predict cardio-respiratory morbidity after pulmonary resection for non-small cell lung cancer (NSCLC). METHODS: Prospective clinical study was based on 489 NSCLC operated cases. An artificial neural network ensemble was developed using a training set of 348 patients undergoing lung resection between 1994 and 1999. Predictive variables used were: sex of the patient, age, body mass index, ischemic heart disease, cardiac arrhythmia, diabetes mellitus, induction chemotherapy, extent of resection, chest wall resection, perioperative blood transfusion, tumour staging, forced expiratory volume in 1s percent (FEV(1)%), and predicted postoperative FEV(1)% (ppoFEV(1)%). The analysed outcome was the occurrence of postoperative cardio-respiratory complications prospectively recorded and codified. The artificial neural network ensemble consists of 100 backpropagation networks combined via a simple averaging method. The probabilities of complication calculated by ensemble model were obtained to the actual occurrence of complications in 141 cases operated on between January 2000 and December 2001 and a receiver operating characteristic (ROC) curve for this method was constructed. RESULTS: The prevalence of cardio-respiratory morbidity was 0.25 in the training and 0.30 in the validation series. The accuracy for morbidity prediction (area under the ROC curve) was 0.98 by the ensemble model. CONCLUSION: In this series an artificial neural network ensemble offered a high performance to predict postoperative cardio-respiratory morbidity.
Authors:
Gustavo Santos-García; Gonzalo Varela; Nuria Novoa; Marcelo F Jiménez
Related Documents :
7512285 - Efficacy of short term versus long term tube thoracostomy drainage before tetracycline ...
9621865 - Lung volume reduction surgery for emphysema: out on a limb without a nett.
18557145 - Management of postpneumonic empyemas in children.
16045575 - Use of low dose dopamine, gabexate mesilate and ulinastatin reduces the water balance a...
23411925 - Ring influence on ponderal evolution after four years of laparoscopic roux-en-y gastric...
23146555 - Identifying poor compliance with cpap in obstructive sleep apnoea: a simple prediction ...
Publication Detail:
Type:  Evaluation Studies; Journal Article    
Journal Detail:
Title:  Artificial intelligence in medicine     Volume:  30     ISSN:  0933-3657     ISO Abbreviation:  Artif Intell Med     Publication Date:  2004 Jan 
Date Detail:
Created Date:  2003-12-19     Completed Date:  2004-04-02     Revised Date:  2004-11-17    
Medline Journal Info:
Nlm Unique ID:  8915031     Medline TA:  Artif Intell Med     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  61-9     Citation Subset:  IM    
Affiliation:
Section of Thoracic Surgery, Salamanca University Hospital, 37007, Salamanca, Spain.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Aged
Blood Transfusion
Carcinoma, Non-Small-Cell Lung / surgery*
Chronic Disease
Female
Forced Expiratory Volume
Humans
Lung Neoplasms / surgery*
Male
Middle Aged
Morbidity
Neural Networks (Computer)*
Postoperative Complications*
Predictive Value of Tests
Prognosis
Prospective Studies
Risk Factors

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  WeAidU-a decision support system for myocardial perfusion images using artificial neural networks.
Next Document:  Use of genetic algorithms for neural networks to predict community-acquired pneumonia.