A methodology for customizing clinical tests for esophageal cancer based on patient preferences

Publication date: Available online 29 September 2018Source: Artificial Intelligence in MedicineAuthor(s): Asis Roy, Sourangshu Bhattacharya, Kalyan GuinAbstractBackgroundClinical tests for diagnosis of any disease may be expensive, uncomfortable, time consuming and can have side effects e.g. barium swallow test for esophageal cancer. Although we can predict non-existence of esophageal cancer with near 100% certainty just using demographics, lifestyle, medical history information, and a few basic clinical tests but our objective is to devise a general methodology for customizing tests with user preferences to avoid expensive or uncomfortable tests.MethodWe propose to use classifiers trained from electronic medical records (EMR) for selection of tests. The key idea is to design classifiers with 100% false normal rates, possibly at the cost of higher false abnormal. We find kernel logistic regression to be most suitable for the task. We propose an algorithm for finding the best probability threshold for kernel LR, based on test set accuracy tuning with help of a validation data set. Using the proposed algorithm, we describe schemes for selecting tests, which appear as features in the automatic classification algorithm, using preferences on costs and discomfort of the users i.e the proposed method is able to detect almost all true patients in the population even with user preferred clinical tests.ResultWe test our methodology with EMRs collected for more than 3000 patients, as a ...
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research