Data Quality in Healthcare for the Purpose of Artificial Intelligence: A Case Study on ECG Digitalization
Arian Ranjbar, Jesper Ravn
Abstract
The quantity of data generated within healthcare is increasing exponentially. Following this development, the interest of using data driven methodologies such as machine learning is on a steady rise. However, the quality of the data also needs to be considered, since information generated for human interpretation may not be optimal for quantitative computer-based analysis. This work investigates dimensions of data quality for the purpose of artificial intelligence applications in healthcare. Particularly, ECG is studied which traditionally rely on analog prints for initial examination. A digitalization process for ECG is implemented, together with a machine learning model for heart failure prediction, to quantitatively compare results based on data quality. The digital time series data provide a significant accuracy increase, compared to scans of analog plots.