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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Rath et al. employed molecular imaging to detect α4β7 integrin expression in a cohort of 5 CD patients before vedolizumab therapy.
“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]
Responders to vedolizumab treatment had pericryptal α4β7+ cells, whereas non-responders did not have any α4β7+ cells.
“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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“mation, especially in patients with confirmed IBD [5].”
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“With the rapid technological advances, digital health tools and telemedicine (defined as diagnosis, treatment, and monitoring of disease by means of the internet, mobile phone applications, and wearable devices) can potentially provide an opportunity for highvalue care by improving efficiency in health care delivery (…”
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“Recent meta-analyses have supported the efficacy and safety of JAK inhibitors in clinical and endoscopic remission [50,51].”
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“71 79 This comprehensive narrative review will summarise our current understanding of the genetic, immunological and microbial drivers of IBD.”
BackgroundSerum exosomal pregnancy zone protein as a promising biomarker in inflammatory bowel disease
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“Previous reports have shown that numerous factors, such as environment, genes, diet, and microbiota, interact in an intricate way, leading to the biological complexity of IBD [4].”
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“The integration of omics data into clinical practice using AI can provide personalized medicine in real time to improve quality of care and patient outcomes.(10,43) A machine-learning model predicted endoscopic response to ustekinumab in patients with CD by integrating genomics and transcriptomics data.”
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“Inflammatory bowel disease (IBD) is a group of chronic nonspecific intestinal inflammatory diseases with unknown etiology, including ulcerative colitis (UC) and crohn's disease (CD) [1].”
The burden of cost in inflammatory bowel disease: a medical economic perspective
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“Applications for data aggregation and analysis are being used to developed predictive models of disease course, response to drug therapy, drug safety profiles, understand disease heterogeneity, and evaluate decision models for cost-effectiveness [37].”
MethodImmunological Networks Defining the Heterogeneity of Inflammatory Bowel Diseases
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“In recent years, we have seen many success stories of artificial intelligence-based tools applied to big data that can be used to improve public health management and assist decision-making in health care [55,62,63].”
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“There is consensus in the literature that these are negative outcomes for IBD that should be avoided [5, 6].”
BackgroundWhat’s new in IBD therapy: An “omics network” approach
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“Multiple factors, such as genes, diet, environment and the microbiota contribute to complex etiologies (2).”
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“12,18,19 Next, different analysis models and methods could be adopted for the heterogeneous data in gastrointestinal disease research.”
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“In contrast, non-linear machine learning (ML) models demonstrated their potential in leveraging complex non-linear correlations between the input variables and outcomes [23, 24, 25].”
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“Despite these benefits/successes, it is also opined that most businesses are yet to capitalize on what the utilitarian benefits-discourse of BDA [87].”
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“44 Recently, they are also using machine learning technologies to improve the diagnostic performance, lower the cost, and save time.”
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“This has been lacking in many previous studies (Olivera et al., 2019).”
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BackgroundImmunological Profiling of Paediatric Inflammatory Bowel Disease Using Unsupervised Machine Learning
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“It may be argued that given the complex polygenic nature of IBD and the resultant biological heterogeneity, the 2 conditions represent a phenotypic spectrum of an underlying immune dysregulation (29).”
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BackgroundBig Data in Gastroenterology Research
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“Imaging data X-ray, CT, MRI, endoscopy, capsule imaging data AI-based interpretation of upper, lower, and video capsule endoscopy can capture findings that may otherwise be overlooked Subjective interpretation by the operator is still required (relatively minor concern since these can be standardized) [14,15]”
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“Data access and computing often bears other costs, such as requiring connections at data producers (Olivera et al .”
Data Asset Quality Evaluation Framework Based on a Hybrid Multi‐Criteria Decision‐Making Method
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“While traditional dimensions such as accuracy, completeness, consistency, trustworthiness, and timeliness remain relevant, the inclusion of novel dimensions such as cost-effectiveness and profitability has become essential [17, 18].”
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“27 While this can include structured as well as unstructured data, advances in natural language processing (NLP) may aid rapid data extraction from healthcare records.”
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InfluentialPregnancy zone protein, a potential research target in multiple diseases
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“31 Recently, deep learning and neural networks have revolutionized medical imaging, significantly improving the accuracy of disease detection, including cancers and cardiovascular conditions.”
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“However, as other working groups suggest [24,25], our study shows that in combination with big data, ML approaches can help improve the treatment of IBD patients in the future.”
ResultPhase I, II and III Trials in Inflammatory Bowel Diseases: A Practical Guide for the Non-specialist
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“Despite their potential, large interpersonal variation among patients remains a challenge, emphasizing the need for personalization in model application and refinement [91,92].”
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“GM have been implicated in the pathophysiology of in fl ammation, especially in patients with con fi rmed IBD [3].”
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“6 Hence, a systematic connection or “ data fusion ” is vital to integrate the various datasets to provide meaningful insights and valuable perspectives in health data interpretation across multiple nations or regions.”
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“Previous research has found similar challenges; hence, these themes may represent common barriers in eHealth development [24-27].”
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- 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
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › 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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