A comparison of prediction approaches for identifying prodromal Parkinson disease.

Identifying people with Parkinson disease during the prodromal period, including via algorithms in administrative claims data, is an important research and clinical priority. We sought to improve upon an existing penalized logistic regression model, based on diagnosis and procedure codes, by adding...

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Autores principales:	Mark N Warden, Susan Searles Nielsen, Alejandra Camacho-Soto, Roman Garnett, Brad A Racette
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4f17074530b341eaa642d598bcba33fe
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spelling	oai:doaj.org-article:4f17074530b341eaa642d598bcba33fe2021-12-02T20:19:31ZA comparison of prediction approaches for identifying prodromal Parkinson disease.1932-620310.1371/journal.pone.0256592https://doaj.org/article/4f17074530b341eaa642d598bcba33fe2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0256592https://doaj.org/toc/1932-6203Identifying people with Parkinson disease during the prodromal period, including via algorithms in administrative claims data, is an important research and clinical priority. We sought to improve upon an existing penalized logistic regression model, based on diagnosis and procedure codes, by adding prescription medication data or using machine learning. Using Medicare Part D beneficiaries age 66-90 from a population-based case-control study of incident Parkinson disease, we fit a penalized logistic regression both with and without Part D data. We also built a predictive algorithm using a random forest classifier for comparison. In a combined approach, we introduced the probability of Parkinson disease from the random forest, as a predictor in the penalized regression model. We calculated the receiver operator characteristic area under the curve (AUC) for each model. All models performed well, with AUCs ranging from 0.824 (simplest model) to 0.835 (combined approach). We conclude that medication data and random forests improve Parkinson disease prediction, but are not essential.Mark N WardenSusan Searles NielsenAlejandra Camacho-SotoRoman GarnettBrad A RacettePublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0256592 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mark N Warden Susan Searles Nielsen Alejandra Camacho-Soto Roman Garnett Brad A Racette A comparison of prediction approaches for identifying prodromal Parkinson disease.
description	Identifying people with Parkinson disease during the prodromal period, including via algorithms in administrative claims data, is an important research and clinical priority. We sought to improve upon an existing penalized logistic regression model, based on diagnosis and procedure codes, by adding prescription medication data or using machine learning. Using Medicare Part D beneficiaries age 66-90 from a population-based case-control study of incident Parkinson disease, we fit a penalized logistic regression both with and without Part D data. We also built a predictive algorithm using a random forest classifier for comparison. In a combined approach, we introduced the probability of Parkinson disease from the random forest, as a predictor in the penalized regression model. We calculated the receiver operator characteristic area under the curve (AUC) for each model. All models performed well, with AUCs ranging from 0.824 (simplest model) to 0.835 (combined approach). We conclude that medication data and random forests improve Parkinson disease prediction, but are not essential.
format	article
author	Mark N Warden Susan Searles Nielsen Alejandra Camacho-Soto Roman Garnett Brad A Racette
author_facet	Mark N Warden Susan Searles Nielsen Alejandra Camacho-Soto Roman Garnett Brad A Racette
author_sort	Mark N Warden
title	A comparison of prediction approaches for identifying prodromal Parkinson disease.
title_short	A comparison of prediction approaches for identifying prodromal Parkinson disease.
title_full	A comparison of prediction approaches for identifying prodromal Parkinson disease.
title_fullStr	A comparison of prediction approaches for identifying prodromal Parkinson disease.
title_full_unstemmed	A comparison of prediction approaches for identifying prodromal Parkinson disease.
title_sort	comparison of prediction approaches for identifying prodromal parkinson disease.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/4f17074530b341eaa642d598bcba33fe
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A comparison of prediction approaches for identifying prodromal Parkinson disease.

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