Presenter of 1 Presentation
AI-BASED PREDICTION OF IMMINENT PRIMARY STROKE ON CLAIMS DATA ENABLES ACCURATE PATIENT STRATIFICATION
Abstract
Background and Aims
Direct treatment and follow-up costs due to permanent disability after an ischemic stroke are increasing fast. Current approaches fail to identify high-risk profiles of imminent stroke and focus on mid- to long-term risk assessment, crucially impeding individualized preventative action. Claims data may support the development of new risk prediction paradigms for personalized management of disease.
Methods
We developed a data-driven paradigm to predict personalized risk of imminent primary ischemic stroke based on social health insurance data from northeast Germany. Stroke events were defined by the presence of an ischemic stroke ICD-10 diagnosis within the available insurance period. Controls (n=150,091) and strokes (n=53,047) were matched by age and insurance length, resulting in a generally aged, high-risk study population. We trained traditional and Machine Learning (ML) classifiers to predict the overall likelihood of a primary event based on 55 features including demographic parameters, ICD-10 diagnosis of diseases and dependence on care. Binary ICD-10 features were translated into temporal duration of diagnoses by counting days since the first appearance of disease in the patients’ records.
Results
The best ML model, Tree-boosting, yielded excellent performance with an area under the receiver operating characteristics curve of 0.91, sensitivity of 0.84 and specificity of 0.81. Long duration of hypertension, dyslipidemia and diabetes type-2 were most influential for predicting stroke according to feature importance ranking.
Conclusions
Our proposed data-driven ML approach provides a highly promising direction for improved, personalized prevention and management of imminent stroke, while the developed models offer direct applicability for risk stratification in northeast Germany.