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Big data in IBD: a look into the future
Open accessAbstract
Big data methodologies, made possible with the increasing generation and availability of digital data and enhanced analytical capabilities, have produced new insights to improve outcomes in many disciplines. Application of big data in the health-care sector is in its early stages, although the potential for leveraging underutilized data to gain a better understanding of disease and improve quality of care is enormous. Owing to the intrinsic characteristics of inflammatory bowel disease (IBD) and the management dilemmas that it imposes, the implementation of big data research strategies not only can complement current research efforts but also could represent the only way to disentangle the complexity of the disease. In this Review, we explore important potential applications of big data in IBD research, including predictive models of disease course and response to therapy, characterization of disease heterogeneity, drug safety and development, precision medicine and cost-effectiveness of care. We also discuss the strengths and limitations of potential data sources that big data analytics could draw from in the field of IBD, including electronic health records, clinical trial data, e-health applications and genomic, transcriptomic, proteomic, metabolomic and microbiomic data. In this Review, the authors explore potential applications of big data in IBD research, including predictive models of disease course and response to therapy, characterization of disease heterogeneity, drug safety and development, precision medicine and cost-effectiveness of care.
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“Similarly, Rath et al. employed molecular imaging to detect α4β7 integrin expression in a cohort of 5 CD patients before vedolizumab therapy.81”
From Artificial intelligence and endo-histo-omics: new dimensions of precision endoscopy and histology in inflammatory bowel disease (2024) · cites this paper as [81]
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“Their findings revealed that responders to treatment had peri cryptal α4β7+ cells, whereas non-responders did not have any α4β7+ cells.”
From Artificial intelligence and endo-histo-omics: new dimensions of precision endoscopy and histology in inflammatory bowel disease (2024) · cites this paper as [81]
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Metadata
- Authors
- Olivera, P., Danese, S., Jay, N., Natoli, G., & Peyrin-Biroulet, L.
- Affiliations
- Centro de Educación Médica e Investigaciones Clínicas Norberto Quirno (AR)
- IRCCS Humanitas Research Hospital (IT)
- Centre Hospitalier Régional et Universitaire de Nancy (FR)
- Venue
- Nature Reviews Gastroenterology & Hepatology
- Published
- 2019-01-18
- Volume
- 16
- Issue
- 5
- Pages
- 312-321
- ISSN
- 1759-5045 (ISSN-L)
- 1759-5053
- OpenAlex
- W2910890040
- Cited by
- 151
- References
- 149
- Citations by year
- 2026: 4 · 2025: 18 · 2024: 28 · 2023: 14 · 2022: 32 · 2021: 27 · 2020: 18 · 2019: 10
- FWCI
- 2.91
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Reviews Gastroenterology & Hepatology (publishedVersion, other-oa)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Archivio Istituzionale della Ricerca - Università Vita-Salute San Raffaele (submittedVersion)
- Topics
- Health Sciences › Medicine › Gastroenterology › Inflammatory Bowel Disease
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- big data, inflammatory bowel disease, predictive modeling, disease progression prediction, treatment response prediction, disease heterogeneity, drug safety, drug development, precision medicine, cost-effectiveness, electronic health records, clinical trial data, e-health applications, multi-omics data, microbiome data
- MeSH terms
- Clinical Decision-Making, Big Data, Cost-Benefit Analysis, Humans, Prognosis, United States, Inflammatory Bowel Diseases, Precision Medicine
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