TY - JOUR
T1 - RegressionExplorer
T2 - Interactive exploration of logistic regression models with subgroup analysis
AU - Dingen, Dennis
AU - van 't Veer, Marcel
AU - Houthuizen, Patrick
AU - Mestrom, Eveline H.J.
AU - Korsten, Erik H.H.M.
AU - Bouwman, R.A.
AU - van Wijk, Jarke
PY - 2019/1/1
Y1 - 2019/1/1
N2 - We present RegressionExplorer, a Visual Analytics tool for the interactive exploration of logistic regression models. Our application domain is Clinical Biostatistics, where models are derived from patient data with the aim to obtain clinically meaningful insights and consequences. Development and interpretation of a proper model requires domain expertise and insight into model characteristics. Because of time constraints, often a limited number of candidate models is evaluated. RegressionExplorer enables experts to quickly generate, evaluate, and compare many different models, taking the workflow for model development as starting point. Global patterns in parameter values of candidate models can be explored effectively. In addition, experts are enabled to compare candidate models across multiple subpopulations. The insights obtained can be used to formulate new hypotheses or to steer model development. The effectiveness of the tool is demonstrated for two uses cases: prediction of a cardiac conduction disorder in patients after receiving a heart valve implant and prediction of hypernatremia in critically ill patients.
AB - We present RegressionExplorer, a Visual Analytics tool for the interactive exploration of logistic regression models. Our application domain is Clinical Biostatistics, where models are derived from patient data with the aim to obtain clinically meaningful insights and consequences. Development and interpretation of a proper model requires domain expertise and insight into model characteristics. Because of time constraints, often a limited number of candidate models is evaluated. RegressionExplorer enables experts to quickly generate, evaluate, and compare many different models, taking the workflow for model development as starting point. Global patterns in parameter values of candidate models can be explored effectively. In addition, experts are enabled to compare candidate models across multiple subpopulations. The insights obtained can be used to formulate new hypotheses or to steer model development. The effectiveness of the tool is demonstrated for two uses cases: prediction of a cardiac conduction disorder in patients after receiving a heart valve implant and prediction of hypernatremia in critically ill patients.
KW - Exploratory data analysis
KW - Multivariate statistics
KW - Predictive visual analytics
KW - Regression analysis
KW - Subgroup analysis
KW - Variable selection
KW - Visual analytics
UR - http://www.scopus.com/inward/record.url?scp=85053315604&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2018.2865043
DO - 10.1109/TVCG.2018.2865043
M3 - Article
C2 - 30222573
AN - SCOPUS:85053315604
VL - 25
SP - 246
EP - 255
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
SN - 1077-2626
IS - 1
M1 - 8464305
ER -