Expert-Augmented Machine Learning
This addresses the need for high-performance and dependable machine learning in critical applications like healthcare, though it appears incremental as it builds on existing human-machine collaboration concepts.
The paper tackles the problem of limited data quality and trust in machine learning by proposing Expert-Augmented Machine Learning (EAML), a method that integrates expert knowledge into models, resulting in improved generalizability and reduced data requirements for mortality prediction in intensive care.
Machine Learning is proving invaluable across disciplines. However, its success is often limited by the quality and quantity of available data, while its adoption by the level of trust that models afford users. Human vs. machine performance is commonly compared empirically to decide whether a certain task should be performed by a computer or an expert. In reality, the optimal learning strategy may involve combining the complementary strengths of man and machine. Here we present Expert-Augmented Machine Learning (EAML), an automated method that guides the extraction of expert knowledge and its integration into machine-learned models. We use a large dataset of intensive care patient data to predict mortality and show that we can extract expert knowledge using an online platform, help reveal hidden confounders, improve generalizability on a different population and learn using less data. EAML presents a novel framework for high performance and dependable machine learning in critical applications.