Predictive Powered Inference for Healthcare; Relating Optical Coherence Tomography Scans to Multiple Sclerosis Disease Progression

Jacob Schultz, Jerry L Prince, Bruno Michel Jedynak
Proceedings of the 9th Machine Learning for Healthcare Conference, PMLR 252, 2024.

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 this Paper

BibTeX
@InProceedings{pmlr-v252-schultz24a, 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}, editor = {Deshpande, Kaivalya and Fiterau, Madalina and Joshi, Shalmali and Lipton, Zachary and Ranganath, Rajesh and Urteaga, Iñigo}, volume = {252}, series = {Proceedings of Machine Learning Research}, month = {16--17 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v252/main/assets/schultz24a/schultz24a.pdf}, url = {https://proceedings.mlr.press/v252/schultz24a.html}, 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.} }
Endnote
%0 Conference Paper %T Predictive Powered Inference for Healthcare; Relating Optical Coherence Tomography Scans to Multiple Sclerosis Disease Progression %A Jacob Schultz %A Jerry L Prince %A Bruno Michel Jedynak %B Proceedings of the 9th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2024 %E Kaivalya Deshpande %E Madalina Fiterau %E Shalmali Joshi %E Zachary Lipton %E Rajesh Ranganath %E Iñigo Urteaga %F pmlr-v252-schultz24a %I PMLR %U https://proceedings.mlr.press/v252/schultz24a.html %V 252 %X 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.
APA
Schultz, J., Prince, J.L. & Jedynak, B.M.. (2024). Predictive Powered Inference for Healthcare; Relating Optical Coherence Tomography Scans to Multiple Sclerosis Disease Progression. Proceedings of the 9th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 252 Available from https://proceedings.mlr.press/v252/schultz24a.html.

Related Material