FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3156811699> ?p ?o ?g. }

• W3156811699 endingPage "e27275" @default.
• W3156811699 startingPage "e27275" @default.
• W3156811699 abstract "Background Although the potential of big data analytics for health care is well recognized, evidence is lacking on its effects on public health. Objective The aim of this study was to assess the impact of the use of big data analytics on people’s health based on the health indicators and core priorities in the World Health Organization (WHO) General Programme of Work 2019/2023 and the European Programme of Work (EPW), approved and adopted by its Member States, in addition to SARS-CoV-2–related studies. Furthermore, we sought to identify the most relevant challenges and opportunities of these tools with respect to people’s health. Methods Six databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews via Cochrane Library, Web of Science, Scopus, and Epistemonikos) were searched from the inception date to September 21, 2020. Systematic reviews assessing the effects of big data analytics on health indicators were included. Two authors independently performed screening, selection, data extraction, and quality assessment using the AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) checklist. Results The literature search initially yielded 185 records, 35 of which met the inclusion criteria, involving more than 5,000,000 patients. Most of the included studies used patient data collected from electronic health records, hospital information systems, private patient databases, and imaging datasets, and involved the use of big data analytics for noncommunicable diseases. “Probability of dying from any of cardiovascular, cancer, diabetes or chronic renal disease” and “suicide mortality rate” were the most commonly assessed health indicators and core priorities within the WHO General Programme of Work 2019/2023 and the EPW 2020/2025. Big data analytics have shown moderate to high accuracy for the diagnosis and prediction of complications of diabetes mellitus as well as for the diagnosis and classification of mental disorders; prediction of suicide attempts and behaviors; and the diagnosis, treatment, and prediction of important clinical outcomes of several chronic diseases. Confidence in the results was rated as “critically low” for 25 reviews, as “low” for 7 reviews, and as “moderate” for 3 reviews. The most frequently identified challenges were establishment of a well-designed and structured data source, and a secure, transparent, and standardized database for patient data. Conclusions Although the overall quality of included studies was limited, big data analytics has shown moderate to high accuracy for the diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes. Trial Registration International Prospective Register of Systematic Reviews (PROSPERO) CRD42020214048; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=214048" @default.
• W3156811699 created "2021-04-26" @default.
• W3156811699 creator A5011185954 @default.
• W3156811699 creator A5013336551 @default.
• W3156811699 creator A5037618706 @default.
• W3156811699 creator A5046370637 @default.
• W3156811699 creator A5062333685 @default.
• W3156811699 creator A5071147086 @default.
• W3156811699 creator A5085689685 @default.
• W3156811699 date "2021-04-13" @default.
• W3156811699 modified "2023-10-13" @default.
• W3156811699 title "Impact of Big Data Analytics on People’s Health: Overview of Systematic Reviews and Recommendations for Future Studies" @default.
• W3156811699 cites W1964803288 @default.
• W3156811699 cites W2000442546 @default.
• W3156811699 cites W2002461314 @default.
• W3156811699 cites W2098923148 @default.
• W3156811699 cites W2177512971 @default.
• W3156811699 cites W2177870565 @default.
• W3156811699 cites W2308085519 @default.
• W3156811699 cites W2487200295 @default.
• W3156811699 cites W2549346488 @default.
• W3156811699 cites W2553101787 @default.
• W3156811699 cites W2569214105 @default.
• W3156811699 cites W2734832579 @default.
• W3156811699 cites W2751884637 @default.
• W3156811699 cites W2753211498 @default.
• W3156811699 cites W2756578555 @default.
• W3156811699 cites W2799895073 @default.
• W3156811699 cites W2802925146 @default.
• W3156811699 cites W2806442587 @default.
• W3156811699 cites W2883860074 @default.
• W3156811699 cites W2885019712 @default.
• W3156811699 cites W2896013124 @default.
• W3156811699 cites W2899067514 @default.
• W3156811699 cites W2899170201 @default.
• W3156811699 cites W2901460192 @default.
• W3156811699 cites W2901737747 @default.
• W3156811699 cites W2905567853 @default.
• W3156811699 cites W2906321973 @default.
• W3156811699 cites W2912581524 @default.
• W3156811699 cites W2918498292 @default.
• W3156811699 cites W2920223366 @default.
• W3156811699 cites W2941571155 @default.
• W3156811699 cites W2943491685 @default.
• W3156811699 cites W2945091031 @default.
• W3156811699 cites W2962760173 @default.
• W3156811699 cites W2972112160 @default.
• W3156811699 cites W2973066167 @default.
• W3156811699 cites W2979307665 @default.
• W3156811699 cites W2985452234 @default.
• W3156811699 cites W2990375292 @default.
• W3156811699 cites W2990683333 @default.
• W3156811699 cites W2992764683 @default.
• W3156811699 cites W3006618054 @default.
• W3156811699 cites W3013184758 @default.
• W3156811699 cites W3016037596 @default.
• W3156811699 cites W3018719651 @default.
• W3156811699 cites W3023067252 @default.
• W3156811699 cites W3031443331 @default.
• W3156811699 cites W3034655770 @default.
• W3156811699 cites W3036858161 @default.
• W3156811699 cites W3039254823 @default.
• W3156811699 cites W3049024012 @default.
• W3156811699 cites W3126191299 @default.
• W3156811699 cites W3147809485 @default.
• W3156811699 cites W4294215472 @default.
• W3156811699 doi "https://doi.org/10.2196/27275" @default.
• W3156811699 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8080139" @default.
• W3156811699 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33847586" @default.
• W3156811699 hasPublicationYear "2021" @default.
• W3156811699 type Work @default.
• W3156811699 sameAs 3156811699 @default.
• W3156811699 citedByCount "26" @default.
• W3156811699 countsByYear W31568116992021 @default.
• W3156811699 countsByYear W31568116992022 @default.
• W3156811699 countsByYear W31568116992023 @default.
• W3156811699 crossrefType "journal-article" @default.
• W3156811699 hasAuthorship W3156811699A5011185954 @default.
• W3156811699 hasAuthorship W3156811699A5013336551 @default.
• W3156811699 hasAuthorship W3156811699A5037618706 @default.
• W3156811699 hasAuthorship W3156811699A5046370637 @default.
• W3156811699 hasAuthorship W3156811699A5062333685 @default.
• W3156811699 hasAuthorship W3156811699A5071147086 @default.
• W3156811699 hasAuthorship W3156811699A5085689685 @default.
• W3156811699 hasBestOaLocation W31568116991 @default.
• W3156811699 hasConcept C124101348 @default.
• W3156811699 hasConcept C142724271 @default.
• W3156811699 hasConcept C15744967 @default.
• W3156811699 hasConcept C160735492 @default.
• W3156811699 hasConcept C162324750 @default.
• W3156811699 hasConcept C17744445 @default.
• W3156811699 hasConcept C180747234 @default.
• W3156811699 hasConcept C189708586 @default.
• W3156811699 hasConcept C199539241 @default.
• W3156811699 hasConcept C2522767166 @default.
• W3156811699 hasConcept C2776478404 @default.
• W3156811699 hasConcept C2777466982 @default.
• W3156811699 hasConcept C2779356329 @default.

• next