scholarly journals Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury

2015 ◽  
Vol 24 (9) ◽  
pp. 1213-1228 ◽  
Author(s):  
Noémi Kreif ◽  
Richard Grieve ◽  
Iván Díaz ◽  
David Harrison
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Continuous Treatment ◽  
Learning Approach ◽  
Machine Learning Approach

A Machine Learning Approach for Predicting Real-time Risk of Intraoperative Hypotension in Traumatic Brain Injury

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Shara I. Feld ◽  
Daniel S. Hippe ◽  
Ljubomir Miljacic ◽  
Nayak L. Polissar ◽  
Shu-Fang Newman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Real Time ◽  
Learning Approach ◽  
Intraoperative Hypotension ◽  
Machine Learning Approach

scholarly journals Predicting need for hospital admission in patients with traumatic brain injury or skull fractures identified on CT imaging: a machine learning approach

2021 ◽  
pp. emermed-2020-210776
Author(s):  
Carl Marincowitz ◽  
Lewis Paton ◽  
Fiona Lecky ◽  
Paul Tiffin
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Hospital Admission ◽  
Decision Trees ◽  
Decision Rule ◽  
Predictive Value ◽  
Learning Approach ◽  
Skull Fractures ◽  
Machine Learning Approach

BackgroundPatients with mild traumatic brain injury on CT scan are routinely admitted for inpatient observation. Only a small proportion of patients require clinical intervention. We recently developed a decision rule using traditional statistical techniques that found neurologically intact patients with isolated simple skull fractures or single bleeds <5 mm with no preinjury antiplatelet or anticoagulant use may be safely discharged from the emergency department. The decision rule achieved a sensitivity of 99.5% (95% CI 98.1% to 99.9%) and specificity of 7.4% (95% CI 6.0% to 9.1%) to clinical deterioration. We aimed to transparently report a machine learning approach to assess if predictive accuracy could be improved.MethodsWe used data from the same retrospective cohort of 1699 initial Glasgow Coma Scale (GCS) 13–15 patients with injuries identified by CT who presented to three English Major Trauma Centres between 2010 and 2017 as in our original study. We assessed the ability of machine learning to predict the same composite outcome measure of deterioration (indicating need for hospital admission). Predictive models were built using gradient boosted decision trees which consisted of an ensemble of decision trees to optimise model performance.ResultsThe final algorithm reported a mean positive predictive value of 29%, mean negative predictive value of 94%, mean area under the curve (C-statistic) of 0.75, mean sensitivity of 99% and mean specificity of 7%. As with logistic regression, GCS, severity and number of brain injuries were found to be important predictors of deterioration.ConclusionWe found no clear advantages over the traditional prediction methods, although the models were, effectively, developed using a smaller data set, due to the need to divide it into training, calibration and validation sets. Future research should focus on developing models that provide clear advantages over existing classical techniques in predicting outcomes in this population.

scholarly journals Modelling outcomes after paediatric brain injury with admission laboratory values: a machine-learning approach

2019 ◽  
Vol 86 (5) ◽  
pp. 641-645
Author(s):  
Saeed Kayhanian ◽  
Adam M. H. Young ◽  
Chaitanya Mangla ◽  
Ibrahim Jalloh ◽  
Helen M. Fernandes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Injury ◽  
Learning Approach ◽  
Laboratory Values ◽  
Machine Learning Approach

scholarly journals Correction: Modelling outcomes after paediatric brain injury with admission laboratory values: a machine-learning approach

2019 ◽  
Vol 86 (5) ◽  
pp. 675-675
Author(s):  
Saeed Kayhanian ◽  
Adam M. H. Young ◽  
Chaitanya Mangla ◽  
Ibrahim Jalloh ◽  
Helen M. Fernandes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Injury ◽  
Learning Approach ◽  
Laboratory Values ◽  
Machine Learning Approach
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