Predictive Powered Inference for Healthcare; Relating Optical Coherence Tomography Scans to Multiple Sclerosis Disease Progression
Abstract
Predictive power inference (PPI and PPI++) is a recently developed statistical method for computing confidence intervals and tests. It combines observations with machine-learning predictions. We use this technique to measure the association between the thickness of retinal layers and the time from the onset of Multiple Sclerosis (MS) symptoms. Further, we correlate the former with the Expanded Disability Status Scale, a measure of the progression of MS. In both cases, the confidence intervals provided with PPI++ improve upon standard statistical methodology, showing the advantage of PPI++ for answering inference problems in healthcare.
Cite
Text
Schultz et al. "Predictive Powered Inference for Healthcare; Relating Optical Coherence Tomography Scans to Multiple Sclerosis Disease Progression." Proceedings of the 9th Machine Learning for Healthcare Conference, 2024.Markdown
[Schultz et al. "Predictive Powered Inference for Healthcare; Relating Optical Coherence Tomography Scans to Multiple Sclerosis Disease Progression." Proceedings of the 9th Machine Learning for Healthcare Conference, 2024.](https://mlanthology.org/mlhc/2024/schultz2024mlhc-predictive/)BibTeX
@inproceedings{schultz2024mlhc-predictive,
title = {{Predictive Powered Inference for Healthcare; Relating Optical Coherence Tomography Scans to Multiple Sclerosis Disease Progression}},
author = {Schultz, Jacob and Prince, Jerry L and Jedynak, Bruno Michel},
booktitle = {Proceedings of the 9th Machine Learning for Healthcare Conference},
year = {2024},
volume = {252},
url = {https://mlanthology.org/mlhc/2024/schultz2024mlhc-predictive/}
}