Poster: Towards Understanding Root Causes of Real Failures in Healthcare Machine Learning Applications

Abstract

Machine learning (ML) is widely used in healthcare applications to diagnose diseases, forecast disease progression, develop personalized treatment plans, and aid in drug discovery and development [1]. The development of ML applications is inherently different from other applications. Instead of explicitly coding the program's logic, ML applications learn this logic using a machine learning algorithm and provided data. Thus, faults in ML applications, as opposed to in others, can manifest in all these components, such as the application itself, incorrect use of the machine learning algorithms or libraries, and issues with data used for training. Thus, understanding these various root causes of real faults would help to develop effective testing techniques for these applications. Therefore, we analyzed 50 real-life faults from four ML healthcare applications to better understand the faults presented in this domain.