Predicting the Individual Treatment Effect of Neurosurgery for Patients with Traumatic Brain Injury in the Low-Resource Setting: A Machine Learning Approach in Uganda

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

Health Economics ◽

10.1002/hec.3189 ◽

2015 ◽

Vol 24 (9) ◽

pp. 1213-1228 ◽

Cited By ~ 15

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

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Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: Machine learning approach

PLoS ONE ◽

10.1371/journal.pone.0235231 ◽

2020 ◽

Vol 15 (7) ◽

pp. e0235231 ◽

Cited By ~ 1

Author(s):

Ahmad Abujaber ◽

Adam Fadlalla ◽

Diala Gammoh ◽

Husham Abdelrahman ◽

Monira Mollazehi ◽

...

Keyword(s):

Machine Learning ◽

Traumatic Brain Injury ◽

Mechanical Ventilation ◽

Brain Injury ◽

Trauma Registry ◽

Prolonged Mechanical Ventilation ◽

Registry Data ◽

Learning Approach ◽

Machine Learning Approach

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

Brain and Behavior ◽

10.1002/brb3.541 ◽

2016 ◽

Vol 6 (11) ◽

Cited By ~ 15

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

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Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

10.26434/chemrxiv.5513581.v1 ◽

2017 ◽

Author(s):

Sabrina Jaeger ◽

Simone Fulle ◽

Samo Turk

Keyword(s):

Machine Learning ◽

Language Processing ◽

Supervised Machine Learning ◽

Learning Approach ◽

Learning Approaches ◽

Unsupervised Machine Learning ◽

Feature Representations ◽

Machine Learning Approach ◽

The Individual ◽

Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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Predicting Future Service Use in Dutch Mental Healthcare: A Machine Learning Approach

Administration and Policy in Mental Health and Mental Health Services Research ◽

10.1007/s10488-021-01150-6 ◽

2021 ◽

Author(s):

Kasper van Mens ◽

Sascha Kwakernaak ◽

Richard Janssen ◽

Wiepke Cahn ◽

Joran Lokkerbol ◽

...

Keyword(s):

Machine Learning ◽

Service Use ◽

Visual Analysis ◽

Mental Healthcare ◽

Machine Learning Techniques ◽

Individual Treatment ◽

Learning Approach ◽

Group Level ◽

Starting Point ◽

Machine Learning Approach

AbstractA mental healthcare system in which the scarce resources are equitably and efficiently allocated, benefits from a predictive model about expected service use. The skewness in service use is a challenge for such models. In this study, we applied a machine learning approach to forecast expected service use, as a starting point for agreements between financiers and suppliers of mental healthcare. This study used administrative data from a large mental healthcare organization in the Netherlands. A training set was selected using records from 2017 (N = 10,911), and a test set was selected using records from 2018 (N = 10,201). A baseline model and three random forest models were created from different types of input data to predict (the remainder of) numeric individual treatment hours. A visual analysis was performed on the individual predictions. Patients consumed 62 h of mental healthcare on average in 2018. The model that best predicted service use had a mean error of 21 min at the insurance group level and an average absolute error of 28 h at the patient level. There was a systematic under prediction of service use for high service use patients. The application of machine learning techniques on mental healthcare data is useful for predicting expected service on group level. The results indicate that these models could support financiers and suppliers of healthcare in the planning and allocation of resources. Nevertheless, uncertainty in the prediction of high-cost patients remains a challenge.

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