ArticlePloS one2015
Personalized Modeling for Prediction with Decision-Path Models.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Smart emergency care: a narrative review of predictive machine learning models.Annals of translational medicine · 2025Review
- Algorithmic individual fairness and healthcare: a scoping review.JAMIA open · 2025Article
- Algorithmic Individual Fairness and Healthcare: A Scoping Review.medRxiv : the preprint server for health sciences · 2024Article
- Development and external validation of multivariate prediction models for erectile dysfunction in men with localized prostate cancer.PloS one · 2023Article
- Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning.Scientific reports · 2021Article
- Patient-Specific Modeling with Personalized Decision Paths.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Deriving predictive models in medicine typically relies on a population approach where a single model is developed from a dataset of individuals. In this paper we describe and evaluate a personalized approach in which we construct a new type of decision tree model called decision-path model that takes advantage of the particular features of a given person of interest. We introduce three personalized methods that derive personalized decision-path models. We compared the performance of these methods to that of Classification And Regression Tree (CART) that is a population decision tree to predict seven different outcomes in five medical datasets. Two of the three personalized methods performed statistically significantly better on area under the ROC curve (AUC) and Brier skill score compared to CART. The personalized approach of learning decision path models is a new approach for predictive modeling that can perform better than a population approach.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.