Stroke Risk Prediction Using Artificial Intelligence Techniques Through Electronic Health Records
Song Jiang, Yuan Gu, Ela Kumar
Abstract
Nowadays, Electronic Health Records (EHR) include critical information in the text format. In order to make medical decisions more efficient, the text should be processed and code deliberated. In this report, we applied Artificial Intelligence (AI) techniques to improve stroke risk prediction based on the EHR text. The system based on Natural Language Processing (NLP) generates structured text from EHR, followed by applying Machine Learning (ML) techniques to classify the text as a "good" or "bad" indicator, which is used for prediction. The ML models here we used include logistic regression and Support Vector Machine (SVM). Our results show that both models can classify the text precisely and make predictions accurately.