Predicting the Reasons of Customer Complaints: A First Step Toward Anticipating Quality Issues of In Vitro Diagnostics Assays with Machine Learning

JMIR Medical Informatics
Stephane Aris-BrosouHui Liu

Abstract

Vendors in the health care industry produce diagnostic systems that, through a secured connection, allow them to monitor performance almost in real time. However, challenges exist in analyzing and interpreting large volumes of noisy quality control (QC) data. As a result, some QC shifts may not be detected early enough by the vendor, but lead a customer to complain. The aim of this study was to hypothesize that a more proactive response could be designed by utilizing the collected QC data more efficiently. Our aim is therefore to help prevent customer complaints by predicting them based on the QC data collected by in vitro diagnostic systems. QC data from five select in vitro diagnostic assays were combined with the corresponding database of customer complaints over a period of 90 days. A subset of these data over the last 45 days was also analyzed to assess how the length of the training period affects predictions. We defined a set of features used to train two classifiers, one based on decision trees and the other based on adaptive boosting, and assessed model performance by cross-validation. The cross-validations showed classification error rates close to zero for some assays with adaptive boosting when predicting the potent...Continue Reading

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Citations

Jun 12, 2019·BMC Genomics·George S LongStéphane Aris-Brosou

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Methods Mentioned

BETA
Assay

Software Mentioned

R
UnityConnect
VITROS
tree
CART
Connectivity

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