TY - JOUR
T1 - On the importance of interpretable machine learning predictions to inform clinical decision making in oncology
AU - Lu, Sheng Chieh
AU - Swisher, Christine L.
AU - Chung, Caroline
AU - Jaffray, David
AU - Sidey-Gibbons, Chris
N1 - Publisher Copyright:
Copyright © 2023 Lu, Swisher, Chung, Jaffray and Sidey-Gibbons.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - decision-making support
KW - high-stakes prediction
KW - interpretability and explainability
KW - opaque machine learning models
KW - precision medicine
UR - http://www.scopus.com/inward/record.url?scp=85150216025&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85150216025&partnerID=8YFLogxK
U2 - 10.3389/fonc.2023.1129380
DO - 10.3389/fonc.2023.1129380
M3 - Review article
C2 - 36925929
AN - SCOPUS:85150216025
SN - 2234-943X
VL - 13
JO - Frontiers in Oncology
JF - Frontiers in Oncology
M1 - 1129380
ER -