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Activity Number: 384 - Recent Statistical Advances in Diagnostic Medicine
Type: Invited
Date/Time: Wednesday, August 5, 2020 : 1:00 PM to 2:50 PM
Sponsor: Section on Medical Devices and Diagnostics
Abstract #309347
Title: Integrating Multiple-Domain Rules for Disease Classification
Author(s): Christine Mauro* and Katherine Shear and Yuanjia Wang
Companies: Columbia University and Columbia University and Columbia University
Keywords: disease classification; psychiatry; logic rules; thresholds

In Psychiatry, clinicians use criteria sets from the Diagnostic and Statistical Manual of Mental Disorders (DSM) to diagnose mental disorders. Most criteria sets have several symptom domains and in order to be diagnosed, an individual must meet the minimum number of symptoms required by each domain. Some efforts are now focused on adding biomarkers to these symptom domains to facilitate detection of and highlight the neurobiological basis of psychiatric disorders. Thus, a new criteria set may consist of both clinical symptom counts in several domains and continuous biomarkers. We propose a method to integrate classification rules from multiple data sources to estimate an optimal criteria set. Each domain-specific rule can be counts of symptoms, a linear function of symptoms, or even nonparametric. The overall classification rule is the intersection of these domain-specific rules. Based on examining the expected population loss function, we propose two iterative algorithms using either support vector machines or logistic regression to fit intersection rules consistent with the DSM. The methods are applied to the motivating study to construct a criteria set for Complicated Grief.

Authors who are presenting talks have a * after their name.

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