Computer-assisted prediction of outcome of severely head-injured patients

Barlow, Philip; Teasdale, Graham M.; Jennett, Bryan; Murray, Lauren; Duff, Charles; Murray, Gordon D.

doi:10.1016/0745-7138(84)90059-9

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…The program uses a Bayesian model which has the ability to cope with missing data, a common problem in patients with head injury,9 but still be able to provide an outcome prediction. In practice, a minimum of three items of data were needed to calculate a set of predictions.…”

Section: Methodsmentioning

confidence: 99%

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Glasgow Head Injury Outcome Prediction Program: an independent assessment

Nissen

Jones

Signorini

et al. 1999

Journal of Neurology, Neurosurgery & Psychiatry

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Using an independent data set, the utility of the Glasgow Head Injury Outcome Prediction Program was investigated in terms of possible frequency of use and reliability of outcome prediction in patients with severe head injury, or haematoma requiring evacuation, or coma lasting 6 hours or more, in whom outcome had been reliably assessed at 6 to 24 months after injury. Predictions were calculated on admission, before evacuation of a haematoma, or 24 hours, 3 days, and 7 days after onset of coma lasting 6 hours or more. Three hundred and twenty four patients provided 426 predictions which were possible in 76%, 97%, 19%, 34%, and 53% of patients on admission, before operation, 24 hours, 3 days, and 7 days respectively. Major reasons for non-feasible predictions were that patients were paralysed/ventilated as part of resuscitation or management. Overall, 75.8% of predictions were correct, 14.6% were pessimistic (outcome better than predicted), and 9.6% optimistic (outcome worse than predicted). Of 197 patients (267 predictions) whose eventual outcome was good or moderate, 84.3% of predictions were correct. For death or vegetative survival (96 patients with 110 predictions), 83.6% of predictions were correct but for severe disability (31 patients with 49 predictions), only 12.2% were correctly predicted. The utility of the Glasgow Head Injury Outcome Prediction Program compares favourably with other outcome prediction algorithms for patients with head injury. (J Neurol Neurosurg Psychiatry 1999;67:796-799)

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Section: Methodsmentioning

confidence: 99%

“…The Glasgow Head Injury Outcome Prediction Program is probably the best known model8 9 and we report on an independent assessment of this program in a series of patients with head injury admitted to the Department of Clinical Neurosciences, Western General Hospital, Edinburgh.…”

mentioning

confidence: 99%

Glasgow Head Injury Outcome Prediction Program: an independent assessment

Nissen

Jones

Signorini

et al. 1999

Journal of Neurology, Neurosurgery & Psychiatry

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“…Mathematical and statistical methods have been used in the past to develop models for prediction [12][13][14][15][16][17][18] , including Bayesian network, logistic regression, and artificial neural network (ANN). ANN has been used in solving many problems in engineering processes and recently Yu and Kang [19][20][21] used ANN in their study on urban soundscape to predict acoustic comfort and subjective loudness, and good results have been obtained.…”

Section: Introductionmentioning

confidence: 99%

Prediction of subjective loudness in underground shopping streets using artificial neural networks

2012

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This paper presents artificial neural network (ANN) models which, considering physical variables (e.g. air temperature, relative humidity, horizontal luminance, sound pressure level, and density of people), qualitative and quantitative objective variables (e.g. gender, income level, age, education level and occupation), and subjective variables (e.g. behavior of respondent, brightness evaluation, acoustic comfort), are capable of predicting the subjective loudness in underground shopping streets. All models were properly trained for the prediction by determining the parameters for ANN, such as hidden layers, nodes, iterations, and training functions. Validation tests were conducted to ensure the accuracy of all the ANN models. Different ANN models have been developed, named general, group and individual models depending on their applicability. Prediction results indicate that the general model has the best generality, but its prediction correlation coefficient between outputs and desired targets is approximately 0.60. The individual model gives good predictions, with a correlation coefficient of approximately over 0.70 between outputs and desired targets. However, its applicability is limited. The group model has good generality, with a correlation coefficient of over 0.65 between outputs and desired targets. In conclusion, the group model is suitable for practical use in the design process. © 2012 Institute of Noise Control Engineering.

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Assessing the performance and clinical impact of a computerized prognostic system in severe head injury

Murray

Barlow

et al. 1986

Statistics in Medicine

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The ability to predict outcome in the early stages of severe head injury would have several uses. A computerized system which has been developed in Glasgow to assist clinicians in this task, based on information from over 2500 severely head-injured patients, is described, with particular attention to the testing of the statistical properties of the system, and, more importantly, the assessment of its clinical impact.

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Computer-assisted prediction of outcome of severely head-injured patients

Cited by 8 publications

References 6 publications

Glasgow Head Injury Outcome Prediction Program: an independent assessment

Glasgow Head Injury Outcome Prediction Program: an independent assessment

Prediction of subjective loudness in underground shopping streets using artificial neural networks

Assessing the performance and clinical impact of a computerized prognostic system in severe head injury

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