Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective

Abstract

In this review the critical parts and milestones for data harmonization, from the biomedical engineering perspective, are outlined. The need for data sharing between heterogeneous sources pave the way for cohort harmonization; thus, fostering data integration and interdisciplinary research. Unmet needs in chronic as well as in other diseases, can be addressed based on the integration of patient health records and the sharing of information of the clinical picture and outcome. The stratification of patients, the determination of various clinical and outcome features and the identification of novel biomarkers for the different phenotypes of the disease characterize the impact of cohort harmonization in patient-centered clinical research and in precision medicine. Subsequently, the establishment of matching techniques and ontologies for the creation of data schemas are also presented. The exploitation of web technologies and data-collection tools support the opportunities to achieve new levels of integration and interoperability. Ethical and legal issues which arise when sharing and harmonizing individual-level data are discussed in order to evaluate the harmonization potential. Use cases that shape and test the harmonization approach are explicitly analyzed along with their significant results on their research objectives. Finally, future trends and directions are discussed and critically reviewed towards a roadmap in cohort harmonization for clinical medicine.

Predicting lymphoma development in patients with Sjögren’s syndrome

“…The development of a novel composite index for lymphoma prediction in pSS, based on harmonized data in the largest expected cohort of cases and controls, is one of the key clinical unmet needs in HarmonicSS. Both traditional statistical methods and novel engineerbased methodologies [42,96] will be used to achieve the best result. Two previous scores for lymphoma prediction, i.e.…”

“…Secondly, we will focus to some recent observations which could be useful, in our mind, to improve the study of lymphoma predictors. Finally, the most recent proposals and collaborative ongoing research approaches to this topic will be briefly updated, mainly in the HarmonicSS project (HarmonicSS, European Union Grant 731944; https://harmonicss.eu) [42].…”

Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review

“…Additionally, Chondrogiannis et al, generated a tool for clinical data in a heterogeneous form and for integration of data, an ontology-based tool suggested to arrange data in a structured format. Moreover, patient cohort and biomedical data play an important role for previous health treatment and analysis, and data provided by patients in a heterogeneous structure need to be harmonized, as argued by Kourou et al [11], so that, in an online tool, all patient data are available to medical staff during analysis. In this survey, different cohort harmonization techniques were highlighted, which will help in healthcare applications, such as ML, DL, and Ontology techniques.…”

“…Domain Contributions [43] Oil and Gas High performance measure [44] Agriculture High performance, high availability, and high scalability, using the latest techniques [45] General-Purpose Data generation, storing, fetching, analysis, visualization, and decision-making [46] Banking Helps in auditing the multisource data [47] Healthcare Facilitate for navigation of HL7 FHIR core resources [48] General-Purpose Delivering automatic services to interoperable system [49] Healthcare Helps in developing an automatic system for disordered patient [50] Education To motivate researchers and academicians about the latest techniques [10] Healthcare Useful for decisions of scientific, clinical, and administrative work [51] Education Facilitate in online learning, storage, processing, and academic activities [52] General-Purpose Recommendation system, opinion mining, and parallelism can be targeted [53] Oil and Gas Helpful for decision-makers during exploration, drilling, and production [54] General-Purpose It will facilitate for fetching data and performance measure [65] Healthcare Helpful for disease prevention, tracking, and policy making [11] Healthcare Helps in boosting statistical power of sustainable and robust data [55] Infrastructure Geographic based smart city for aggregation, visualization, and analysis [56] Healthcare Helps in predicting the clinical codes of patient stays…”

“…Emerging technologies, such as the Internet of Things (IoT), Industry 4.0 (I4.0), and extended reality (XR), produce distinct kinds of information via heterogeneous sources and real-world applications that create heterogeneity issues [6], in IoT integration, security, analytics challenges, and computational time [7][8][9]. Among them, data harmonization (DH), which describes the uniform representation of heterogeneous data, was proposed in References [10,11].…”