ArticleJournal of stroke and cerebrovascular diseases : the official journal of National Stroke Association2018
Improving the Accuracy of Scores to Predict Gastrostomy after Intracerebral Hemorrhage with Machine Learning.
Article in Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 9 citations in OpenAlex.
- Patient and clinician perspectives in the use of machine learning and artificial intelligence in the context of acute neurology.JAMIA open · 2025Article
- Utilizing prior-data-fitted networks and in-context learning: a transformer-based tabular foundation model for predicting symptomatic intracranial hemorrhage after successful recanalization.Frontiers in neurology · 2025Article
- Predicting 6-Month Unfavorable Outcome of Acute Ischemic Stroke Using Machine Learning.Frontiers in neurology · 2020Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundGastrostomy placement after intracerebral hemorrhage indicates the need for continued medical care and predicts patient dependence. Our objective was to determine the optimal machine learning technique to predict gastrostomy.
methodsWe included 531 patients in a derivation cohort and 189 patients from another institution for testing. We derived and tested predictions of the likelihood of gastrostomy placement with logistic regression using the GRAVo score (composed of Glasgow Coma Scale ≤12, age >50 years, black race, and hematoma volume >30 mL), compared to other machine learning techniques (kth nearest neighbor, support vector machines, random forests, extreme gradient boosting, gradient boosting machine, stacking). Receiver Operating Curves (Area Under the Curve, [AUC]) between logistic regression (the technique used in GRAVo score development) and other machine learning techniques were compared. Another institution provided an external test data set.
resultsIn the external test data set, logistic regression using the GRAVo score components predicted gastrostomy (P < 0.001), however, with a lower AUC (0.66) than kth nearest neighbors (AUC 0.73), random forests (AUC 0.74), Gradient boosting machine (AUC 0.77), extreme gradient boosting (AUC 0.77), (P < 0.01 for all compared to logistic regression). Results from the internal test set were similar.
conclusionsMachine learning techniques other than logistic regression (eg, random forests, extreme gradient boost, and kth nearest neighbors) were significantly more accurate for predicting gastrostomy using the same independent variables. Machine learning techniques may assist clinicians in identifying patients likely to need interventions.
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Registered trials
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