ArticleStudies in health technology and informatics2018
Machine Learning to Identify Behavioral Determinants of Oral Health in Inner City Older Hispanic Adults.
Article in Studies in health technology and informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Article
- Fairness of Machine Learning Algorithms for Predicting Foregone Preventive Dental Care for Adults.JAMA network open · 2023Article
- Machine Learning in Dentistry: A Scoping Review.Journal of clinical medicine · 2023Article
- Using big data to promote precision oral health in the context of a learning healthcare system.Journal of public health dentistry · 2020Review
- Completeness of Electronic Dental Records in a Student Clinic: Retrospective Analysis.JMIR medical informatics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
We applied machine learning techniques to a community-based behavioral dataset to build prediction models to gain insights about minority dental health and population aging as the foundation for future interventions for urban Hispanics. Our application of machine learning techniques identified emotional and systemic factors such as chronic stress and health literacy as the strongest predictors of self-reported dental health among hundreds of possible variables. Application of machine learning algorithms was useful to build prediction models to gain insights about dental health and minority population aging.
Indexed as
Identifiers
29968651PMC6211787What OpenQuestion holds
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.