A prehospital diagnostic algorithm for strokes using machine learning: a prospective observational study

Abstract

High precision is optimal in prehospital diagnostic algorithms for strokes and large vessel occlusions. We hypothesized that prehospital diagnostic algorithms for strokes and their subcategories using machine learning could have high predictive value. Consecutive adult patients with suspected stroke as per emergency medical service personnel were enrolled in a prospective multicenter observational study in 12 hospitals in Japan. Five diagnostic algorithms using machine learning, including logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting, were evaluated for stroke and subcategories including acute ischemic stroke with/without large vessel occlusions, intracranial hemorrhage, and subarachnoid hemorrhage. Of the 1446 patients in the analysis, 1156 (80%) were randomly included in the training (derivation) cohort and cohorts, and 290 (20%) were included in the test (validation) cohort. In the diagnostic algorithms for strokes using eXtreme Gradient Boosting had the highest diagnostic value (test data, area under the receiver operating curve 0.980). In the diagnostic algorithms for the subcategories using eXtreme Gradient Boosting had a high predictive value (test data, area under the receiver operating curve, acute ischemic stroke with/without large vessel occlusions 0.898/0.882, intracranial hemorrhage 0.866, subarachnoid hemorrhage 0.926). Prehospital diagnostic algorithms using machine learning had high predictive value for strokes and their subcategories.

Artificial intelligence and machine learning in prehospital emergency care: A scoping review

“…After excluding 4,072 papers on title and abstract screening, we identified 278 studies for full-text screening, of which 106 studies were included for data extraction and subsequent analysis. 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 ,…”

Innovative Visualization Approach for Biomechanical Time Series in Stroke Diagnosis Using Explainable Machine Learning Methods: A Proof-of-Concept Study

“…Hayashi et al [16] investigate the significance of prehospital stroke diagnosis, emphasizing the need for precision, especially in identifying large vessel occlusions. Using machine learning, the researchers assessed five diagnostic algorithms and found that Extreme Gradient Boosting-based models achieved the highest diagnostic value, with impressive results across stroke subcategories.…”

The effect of social alienation on stigma among in-patients with stroke: A cross-sectional study

“…Stroke is the second-leading Level 3 cause of death after ischaemic heart disease worldside [2] . Stroke is one of the most common chronic and disabling neurological diseases in China [3] , which contains two main subcategories: ischemic stroke (that is, cerebral infarction) and hemorrhagic stroke (that is, cerebral hemorrhage) [4] .…”

“…Stroke is the second-leading Level 3 cause of death after ischaemic heart disease worldside [2] . Stroke is one of the most common chronic and disabling neurological diseases in China [3] , which contains two main subcategories: ischemic stroke (that is, cerebral infarction) and hemorrhagic stroke (that is, cerebral hemorrhage) [4] .According to research, more than half of individuals who have experienced a stroke will suffer from cognitive impairment, aphasia, dysphagia, hemiplegia, emotional instability, and other related conditions [5] . Consequently, this leads to a feeling of social disgrace among stroke patients.Stigma is a common phenomenon that most stroke survivors struggle with [6] .…”