scholarly journals A machine learning approach to identify functional biomarkers in human prefrontal cortex for individuals with traumatic brain injury using functional near‐infrared spectroscopy

2016 ◽  
Vol 6 (11) ◽  
Author(s):  
Nader Karamzadeh ◽  
Franck Amyot ◽  
Kimbra Kenney ◽  
Afrouz Anderson ◽  
Fatima Chowdhry ◽  
...  
Keyword(s):  
Machine Learning ◽  
Traumatic Brain Injury ◽  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Brain Injury ◽  
Near Infrared ◽  
Learning Approach ◽  
Functional Near Infrared Spectroscopy ◽  
Machine Learning Approach ◽  
Functional Biomarkers

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

FUNCTIONAL NEAR-INFRARED SPECTROSCOPY–BASED ASSESSMENT OF ATTENTION IMPAIRMENTS AFTER TRAUMATIC BRAIN INJURY

2011 ◽  
Vol 04 (03) ◽  
pp. 251-260 ◽  
Author(s):  
ANNA C. MERZAGORA ◽  
MARIA T. SCHULTHEIS ◽  
BANU ONARAL ◽  
MELTEM IZZETOGLU
Keyword(s):  
Traumatic Brain Injury ◽  
Infrared Spectroscopy ◽  
Brain Injury ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Quantitative Information ◽  
Functional Brain Imaging ◽  
Functional Near Infrared Spectroscopy ◽  
Attention Task ◽  
Attention Impairments

A frequent consequence of traumatic brain injury (TBI) is cognitive impairment, which results in significant disruption of an individual's everyday living. To date, most clinical rehabilitation interventions still rely on behavioral observation, with little or no quantitative information about physiological changes produced at the brain level. Functional brain imaging has been extensively used in the study of cognitive impairments following TBI. However, its applications to rehabilitation have been limited. This is due in part to the expensive or invasive nature of these modalities. The objective of this study is to apply functional near-infrared spectroscopy (fNIR) to the assessment of attention impairments following TBI. fNIR provides a localized measure of prefrontal hemodynamic activation, which is susceptible to TBI, and it does so in a noninvasive, affordable and wearable way, thus partially overcoming the limitations of other modalities. Participants included 5 TBI subjects and 11 healthy controls. Brain activation measurements were collected during a target categorization task. Significant differences were found in the hemodynamic response between healthy and TBI subjects. In particular, the elicited responses exhibited reduced amplitude in the TBI group. Overall, the results provide first evidence of the ability of fNIR to reveal differences between TBI and healthy subjects in an attention task. fNIR is therefore a promising neuroimaging technique in the field of neurorehabilitation. The use of fNIR in neurorehabilitation applications would benefit from its noninvasiveness and cost-effectiveness and the neurophysiological information obtained through the evaluation of the hemodynamic activation could provide invaluable information to guide the choice of intervention.

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.

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