Big data hurdles in precision medicine and precision public health.
Background: Nowadays, trendy research in biomedical sciences juxtaposes the term 'precision' to medicine and public health with companion words like big data, data science, and deep learning. Technological advancements permit the collection and merging of large heterogeneous datasets from different...
| Publicado en: | BMC Medical Informatics & Decision Making Vol. 18; no. 1 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
BioMed Central
12/29/2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=133780155&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133780155 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14726947 1CI0 jtl: BMC Medical Informatics & Decision Making issn: 14726947 maglogo: N pubinfo: dt: 12/29/2018 vid: 18 iid: 1 pid: 24147 pub: BioMed Central artinfo: ui: 133780155 133780155 NLM30594159 133780155 10.1186/s12911-018-0719-2 NLM30594159 133780155 ppct: 1 formats: tig: atl: Big data hurdles in precision medicine and precision public health. aug: au: Prosperi, Mattia Min, Jae S. Bian, Jiang Modave, François affil: Department of Epidemiology, College of Medicine & College of Public Health and Health Professions, University of Florida, 32610, Gainesville, FL, USA sug: subj: Health Care Delivery Public Health Human Resource Databases Algorithms Social Media Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales Social Readjustment Rating Scale ab: Background: Nowadays, trendy research in biomedical sciences juxtaposes the term 'precision' to medicine and public health with companion words like big data, data science, and deep learning. Technological advancements permit the collection and merging of large heterogeneous datasets from different sources, from genome sequences to social media posts or from electronic health records to wearables. Additionally, complex algorithms supported by high-performance computing allow one to transform these large datasets into knowledge. Despite such progress, many barriers still exist against achieving precision medicine and precision public health interventions for the benefit of the individual and the population.Main Body: The present work focuses on analyzing both the technical and societal hurdles related to the development of prediction models of health risks, diagnoses and outcomes from integrated biomedical databases. Methodological challenges that need to be addressed include improving semantics of study designs: medical record data are inherently biased, and even the most advanced deep learning's denoising autoencoders cannot overcome the bias if not handled a priori by design. Societal challenges to face include evaluation of ethically actionable risk factors at the individual and population level; for instance, usage of gender, race, or ethnicity as risk modifiers, not as biological variables, could be replaced by modifiable environmental proxies such as lifestyle and dietary habits, household income, or access to educational resources.Conclusions: Data science for precision medicine and public health warrants an informatics-oriented formalization of the study design and interoperability throughout all levels of the knowledge inference process, from the research semantics, to model development, and ultimately to implementation. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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