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dc.contributor.author Jiang, Xia
dc.contributor.author Wells, Alan
dc.contributor.author Brufsky, Adam
dc.contributor.author Neapolitan, Richard
dc.coverage.spatial Pennsylvania
dc.coverage.spatial Illinois
dc.coverage.temporal Holocene
dc.date.accessioned 2019-03-14T20:50:53Z
dc.date.available 2019-03-14T20:50:53Z
dc.date.issued 2019-03-08
dc.identifier doi:10.5061/dryad.64964m0
dc.identifier.citation Jiang X, Wells A, Brufsky A, Neapolitan R (2019) A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis. PLOS ONE 14(3): e0213292.
dc.identifier.uri http://hdl.handle.net/10255/dryad.208497
dc.description 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 Decision Support System for Making Personalized Assessments and Recommendations Concerning Breast Cancer 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 Lynn Sage Data Set (LSDS). 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.
dc.relation.haspart doi:10.5061/dryad.64964m0/1
dc.relation.isreferencedby doi:10.1371/journal.pone.0213292
dc.subject breast cancer
dc.subject clinical
dc.subject metastasis
dc.subject Decision Support
dc.subject Bayesian Network
dc.subject Electronic Health Records
dc.subject EHR
dc.subject treatment
dc.title Data from: A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis
dc.type Article
dwc.ScientificName H. sapiens
dc.contributor.correspondingAuthor Neapolitan, Richard
prism.publicationName PLOS ONE

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Title LSDS-5YDM
Downloaded 10 times
Description The Lynn Sage Data Base (LSDB) contains information about patients who came to the Lynn Sage Comprehensive Breast Center at Northwestern Memorial Hospital for care. The Northwestern Medicine Enterprise Data Warehouse (NMEDW) is a joint initiative across the Northwestern University Feinberg School of Medicine and Northwestern Memorial HealthCare, which maintains comprehensive data obtain from EHRs. Using the LSDB and the NMEDW, we curated a dataset named as the Lynn Sage Data Set with 5-year distant metastasis (LSDS-5YDM), which includes records on 6726 breast cancer patients including clinical features and distant metastasis. The records span 03/02/1990 to 07/28/2015.
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