Big Data Analytics for Predictive Insights in Healthcare Article Swipe
John M. Gates
,
Yulianti Yulianti
,
Greian April Pangilinan
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.33050/italic.v3i1.622
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.33050/italic.v3i1.622
This study leverages the transformative power of big data analytics to enhance healthcare outcomes by integrating diverse data sources like electronic health records, medical imaging, and genomic data to refine predictive models that forecast disease progression and personalize treatment strategies. Employing rigorous data management and machine learning, our findings demonstrate effective risk factor identification and resource optimization, significantly reducing hospital readmissions and improving chronic disease management as evidenced by a case study at City Hospital. Despite challenges related to data security and integration, the research aligns with United Nations SDGs, particularly SDG 3 (Good Health and Well-being) and SDG 9 (Industry, Innovation, and Infrastructure), highlighting the role of analytics in promoting health equity and operational efficiency. The study advocates for the expanded use of big data to build a sustainable, resilient healthcare infrastructure responsive to diverse population needs, recommending that healthcare providers and policymakers utilize these insights to propel data-driven, patient-centric solutions, furthering progress towards global health goals and sustainable development. Future research should include emerging data streams like social determinants of health to enrich these models, ensuring ongoing advancements in healthcare analytics.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.33050/italic.v3i1.622
- https://journal.pandawan.id/italic/article/download/622/490
- OA Status
- diamond
- Cited By
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404594275
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4404594275Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.33050/italic.v3i1.622Digital Object Identifier
- Title
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Big Data Analytics for Predictive Insights in HealthcareWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-12Full publication date if available
- Authors
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John M. Gates, Yulianti Yulianti, Greian April PangilinanList of authors in order
- Landing page
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https://doi.org/10.33050/italic.v3i1.622Publisher landing page
- PDF URL
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https://journal.pandawan.id/italic/article/download/622/490Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://journal.pandawan.id/italic/article/download/622/490Direct OA link when available
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Big data, Health care, Analytics, Transformative learning, Data science, Predictive analytics, Knowledge management, Population health, Business, Computer science, Political science, Data mining, Psychology, Law, PedagogyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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12Total citation count in OpenAlex
- Citations by year (recent)
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2025: 11, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.advancements | 179 |
| abstract_inverted_index.data-driven, | 149 |
| abstract_inverted_index.determinants | 170 |
| abstract_inverted_index.development. | 160 |
| abstract_inverted_index.highlighting | 104 |
| abstract_inverted_index.integration, | 82 |
| abstract_inverted_index.particularly | 90 |
| abstract_inverted_index.policymakers | 143 |
| abstract_inverted_index.readmissions | 60 |
| abstract_inverted_index.recommending | 138 |
| abstract_inverted_index.sustainable, | 129 |
| abstract_inverted_index.optimization, | 56 |
| abstract_inverted_index.significantly | 57 |
| abstract_inverted_index.identification | 53 |
| abstract_inverted_index.infrastructure | 132 |
| abstract_inverted_index.transformative | 4 |
| abstract_inverted_index.patient-centric | 150 |
| abstract_inverted_index.Infrastructure), | 103 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.5600000023841858 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.98223363 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |