About: Preventive healthcare policies in the US: solutions for disease management using Big Data Analytics

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Preventive healthcare policies in the US: solutions for disease management using Big Data Analytics
Creator	Batarseh, Feras Chong, ) Ghassib, Iya Sondor, Deri Su, Po-Hsuan
Source	Medline; PMC
abstract	Data-driven healthcare policy discussions are gaining traction after the Covid-19 outbreak and ahead of the 2020 US presidential elections. The US has a hybrid healthcare structure; it is a system that does not provide universal coverage, albeit few years ago enacted a mandate (Affordable Care Act-ACA) that provides coverage for the majority of Americans. The US has the highest health expenditure per capita of all western and developed countries; however, most Americans don’t tap into the benefits of preventive healthcare. It is estimated that only 8% of Americans undergo routine preventive screenings. On a national level, very few states (15 out of the 50) have above-average preventive healthcare metrics. In literature, many studies focus on the cure of diseases (research areas such as drug discovery and disease prediction); whilst a minority have examined data-driven preventive measures—a matter that Americans and policy makers ought to place at the forefront of national issues. In this work, we present solutions for preventive practices and policies through Machine Learning (ML) methods. ML is morally neutral, it depends on the data that train the models; in this work, we make the case that Big Data is an imperative paradigm for healthcare. We examine disparities in clinical data for US patients by developing correlation and imputation methods for data completeness. Non-conventional patterns are identified. The data lifecycle followed is methodical and deliberate; 1000+ clinical, demographical, and laboratory variables are collected from the Centers for Disease Control and Prevention (CDC). Multiple statistical models are deployed (Pearson correlations, Cramer’s V, MICE, and ANOVA). Other unsupervised ML models are also examined (K-modes and K-prototypes for clustering). Through the results presented in the paper, pointers to preventive chronic disease tests are presented, and the models are tested and evaluated.
has issue date	2020-06-23(xsd:dateTime)
bibo:doi	10.1186/s40537-020-00315-8
bibo:pmid	32834926
has license	cc-by
sha1sum (hex)	b07507fb07f9913de77bd9f95a52d01bf4704cc8
schema:url	https://doi.org/10.1186/s40537-020-00315-8
resource representing a document's title	Preventive healthcare policies in the US: solutions for disease management using Big Data Analytics
has PubMed Central identifier	PMC7309216
has PubMed identifier	32834926
schema:publication	J Big Data
resource representing a document's body	covid:b07507fb07f9913de77bd9f95a52d01bf4704cc8#body_text
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