AUTHOR=Lu Sheng-Chieh , Swisher Christine L. , Chung Caroline , Jaffray David , Sidey-Gibbons Chris 

TITLE=On the importance of interpretable machine learning predictions to inform clinical decision making in oncology

JOURNAL=Frontiers in Oncology

VOLUME=13

YEAR=2023

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2023.1129380

DOI=10.3389/fonc.2023.1129380

ISSN=2234-943X

ABSTRACT=<p>Machine learning-based tools are capable of guiding individualized clinical management and decision-making by providing predictions of a patient’s future health state. Through their ability to model complex nonlinear relationships, ML algorithms can often outperform traditional statistical prediction approaches, but the use of nonlinear functions can mean that ML techniques may also be less interpretable than traditional statistical methodologies. While there are benefits of intrinsic interpretability, many model-agnostic approaches now exist and can provide insight into the way in which ML systems make decisions. In this paper, we describe how different algorithms can be interpreted and introduce some techniques for interpreting complex nonlinear algorithms.</p>