Open AccessA clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis
Author(s) -
Xia Jiang,
Alan Wells,
Adam Brufsky,
Richard E. Neapolitan
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0213292
Subject(s) - breast cancer , medicine , bayesian network , medical physics , metastasis , computer science , cancer , oncology , machine learning
Objective A Clinical Decision Support System ( CDSS ) that can amass Electronic Health Record ( EHR ) and other patient data holds promise to provide accurate classification and guide treatment choices. Our objective is to develop the D ecision Support System for Making P ersonalized A ssessments and Recommendations Concerning Breast C ancer Patients ( DPAC ), which is a CDSS learned from data that recommends the optimal treatment decisions based on a patient’s features. Method We developed a Bayesian network architecture called Causal Modeling with Internal Layers ( CAMIL ), and an algorithm called Treatment Feature Interactions ( TFI ), which learns from data the interactions needed in a CAMIL model. Using the TFI algorithm, we learned interactions for six treatments from the LSDS-5YDM dataset. We created a CAMIL model using these interactions, resulting in a DPAC which recommends treatments towards preventing 5-year breast cancer metastasis. Results In a 5-fold cross-validation analysis, we compared the probability of being metastasis free in 5 years for patients who made decisions recommended by DPAC to those who did not. These probabilities are (the probability for those making the decisions appears first): chemotherapy (.938, .872); breast/chest wall radiation (.939, .902); nodal field radiation (.940, .784); antihormone (.941, .906); HER2 inhibitors (.934, .880); neadjuvant therapy (.931, .837). In an application of DPAC to the independent METABRIC dataset, the probabilities for chemotherapy were (.845, .788). Discussion Patients who took the advice of DPAC had, as a group, notably better outcomes than those who did not. We conclude that DPAC is effective at amassing and analyzing data towards treatment recommendations. Some of the findings in DPAC are controversial. For example, DPAC says that chemotherapy increases the chances of metastasis for many node negative patients. This controversy shows the importance of developing a conclusive version of DPAC to ensure we provide patients with the best patient-specific treatment recommendations.