The Polynomial Progression Subtype Inference Algorithm
August F. Van Hout, Stiven Roytman, Giulia Carli, Travis P. Wigstrom, Prabesh Kanel, Nicolaas I. Bohnen
TL;DR
PPSI is a faster and more efficient algorithm for modeling disease progression, enabling quicker and more accurate patient stratification.
Contribution
PPSI introduces a faster, more interpretable, and scalable algorithm for subtype inference compared to existing methods like SuStaIn.
Findings
PPSI reduces runtimes from hours to seconds and can handle thousands of features.
PPSI successfully models progression in Alzheimer’s disease and breast cancer.
PPSI includes a user-friendly interface and visualization tools for broader accessibility.
Abstract
Longitudinal assessments are currently the gold standard for modeling progression of diseases, but they delay prognosis and increase burden on patients and healthcare systems. Cross-sectional inference offers a valuable alternative, enabling earlier patients’ stratification and broader accessibility. Initial success in this direction has been found with the SuStaIn algorithm (Young et al. 2018) but computational and conceptual shortcomings hamper its usefulness. Here we introduce a more effective algorithm, PPSI, which is orders of magnitude faster, easier to interpret, equally or more accurate, applicable to more complex bidirectional phenomena, and can be fitted with many more variables at once. We demonstrate PPSI’s utility using longitudinal prediction in Alzheimer’s disease (ADNI database), clinical subtype recovery in breast cancer (TCGA-BRCA), and measurement of robustness under…
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Taxonomy
TopicsDigital Filter Design and Implementation