Adaptive data-driven selection of sequences of biological and cognitive markers in clinical diagnosis of dementia

Abstract

AbstractCombining the right—potentially invasive and expensive, markers at the appropriate time is critical to obtain reliable yet economically sustainable decisions in the preclinical diagnosis of dementia. We propose a data-driven analytical framework to individualize the selection of prognostic biomarkers that balance accuracy, costs of opportunity due to delaying the decision, and cost of acquisition depending to prescribed cost parameters. We compared sequential and non-sequential decision strategies based on a linear mixed-effects classification model that integrates irregular, multi-variate longitudinal data. The framework was applied to separate participants that progress to Alzheimer’s disease from the ones that do not within a time interval of three years. As expected, the highest accuracy was obtained by combining all available data from 20.9 measurements per subject on average that were acquired over 4.8 years on average. The proposed sequential algorithm empirically outperformed alternative methods by having lowest costs for a range of tested cost parameters. With the default cost parameters, the sequential algorithm reached an accuracy of 0.84, specificity of 0.86, and sensitivity of 0.82 (0.89, 0.91, and 0.88 with all available data, respectively) while requiring only 2.9 measurements on average (86 percent less observations than all available data) and a time interval of half a year on average (89 percent shorter than all time points). Our sequential algorithms established the decision based on individualized sequences of measurements with reduced process costs compared to non-sequential classification strategies while maintaining competitive accuracy.