Feature engineering with clinical expert knowledge: A case study assessment of machine learning model complexity and performance.

ABSTRACT: Incorporating expert knowledge at the time machine learning models are trained holds promise for producing models that are easier to interpret. The main objectives of this study were to use a feature engineering approach to incorporate clinical expert knowledge prior to applying machine learning techniques, and to assess the impact of the approach on model complexity and performance. Four machine learning models were trained to predict mortality with a severe asthma case study. Experiments to select fewer input features based on a discriminative score showed low to moderate precision for discovering clinically meaningful triplets, indicating that discriminative score alone cannot replace clinical input. When compared to baseline machine learning models, we found a decrease in model complex

SUBMITTER: Roe KD 

PROVIDER: S-EPMC7179831 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature