Abstract P421: Google Auto ML Based Prediction of Outcomes in Intracerebral Hemorrhage - Bringing Machine Learning to Clinicians

Background: Application of Machine Learning (ML) techniques in the prediction of Neurological Disorders has been growing rapidly. ML techniques require novel computer programming skills along with domain knowledge to produce a useful tool. We used Google Auto ML and Google ML Tables, a Cloud based front-end tools to develop, validate and deploy a prognostic model for Intracerebral Hemorrhage (ICH) without a single line of programming code. Methods: Data from Kasturba Medical College (KMC) Hospital ICH Stroke Registry between January 2015 and May 2019 was accessed. Google Auto ML Tables splits the dataset into training, validation and testing sets. 80% of the dataset was used for training, 10% for validation, and 10% for testing. Cramers V correlation statistic ( ϕ c ) between 30 variables and outcome was used to select 20 variables to predict the dichotomous outcome of good (mRS 0-3) vs bad outcome (mRS 4-6). The ML model generated was evaluated using Area Under the Curve (AUC) and Receiver Operating Characteristic (ROC) measures and by confusion/error matrix. Logistic Regression model was developed in parallel using Wizard Pro with the same set of 20 variables. Results: 1000 patients from KMC Registry were analysed. Cramers V correlation was highest for Glasgow Coma Scale (GCS) on admission ( ϕ c =0.6), followed by hematoma volume ( ϕ c =0.57). The AUC ROC of the model for good and bad outcome was 0.89 with an accuracy of 85.6%. Confusion matrix predicted 84% patients with good outcome and 87% with poor outcome. Feature importance chart showed that GCS had the highest relative importance at 25.7, followed by hematoma volume and age. A similar model created with simple logistic regression yielded a ROC of 0.87 with comparable results to the machine learning platform. Conclusion: Useful ML models can be easily developed and implemented by clinicians possessing domain expertise using Google Auto ML without computer programming experience.