An Autoscaling Platform Supporting Graph Data Modelling Big Data Analytics

Kiourtis, Athanasios; Karamolegkos, Panagiotis; Karabetian, Andreas; Voulgaris, Konstantinos; Poulakis, Yannis; Mavrogiorgou, Argyro; Kyriazis, Dimosthenis

doi:10.3233/shti220743

Cited by 15 publications

(8 citation statements)

References 6 publications

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“…This application uses holistic management scheme to the HL-patients by facilitating public health services. This integrated solution also keeps the users away from this type of diseases in future 11 110 , 201 High storage capabilities Big data has a large amount of data to be processed, particularly a plethora of types of data to process and extract enriched information regarding a problem of interest. Furthermore, storing this vast amount of data is another big challenge for the research community.…”

Section: Resultsmentioning

confidence: 99%

Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing

Khan

Nazir

2022

Sci Rep

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Big data has revolutionized the world by providing tremendous opportunities for a variety of applications. It contains a gigantic amount of data, especially a plethora of data types that has been significantly useful in diverse research domains. In healthcare domain, the researchers use computational devices to extract enriched relevant information from this data and develop smart applications to solve real-life problems in a timely fashion. Electronic health (eHealth) and mobile health (mHealth) facilities alongwith the availability of new computational models have enabled the doctors and researchers to extract relevant information and visualize the healthcare big data in a new spectrum. Digital transformation of healthcare systems by using of information system, medical technology, handheld and smart wearable devices has posed many challenges to researchers and caretakers in the form of storage, minimizing treatment cost, and processing time (to extract enriched information, and minimize error rates to make optimum decisions). In this research work, the existing literature is analysed and assessed, to identify gaps that result in affecting the overall performance of the available healthcare applications. Also, it aims to suggest enhanced solutions to address these gaps. In this comprehensive systematic research work, the existing literature reported during 2011 to 2021, is thoroughly analysed for identifying the efforts made to facilitate the doctors and practitioners for diagnosing diseases using healthcare big data analytics. A set of rresearch questions are formulated to analyse the relevant articles for identifying the key features and optimum management solutions, and laterally use these analyses to achieve effective outcomes. The results of this systematic mapping conclude that despite of hard efforts made in the domains of healthcare big data analytics, the newer hybrid machine learning based systems and cloud computing-based models should be adapted to reduce treatment cost, simulation time and achieve improved quality of care. This systematic mapping will also result in enhancing the capabilities of doctors, practitioners, researchers, and policymakers to use this study as evidence for future research.

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Section: Resultsmentioning

confidence: 99%

Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing

Khan

Nazir

2022

Sci Rep

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“…Such a fact highlights the need for a holistic approach to exploit all the existing huge amounts of healthcare data for achieving better health management and patient outcomes, personalized medicine, the prevention of diseases, effective and targeted policy making, and health promotion in general. Especially the construction of effective and targeted policies making nowadays is more than obligatory and vital due various viruses' outbreaks (e.g., COVID-19) [26,27]. Building a model of healthcare that would meet all patients' needs is one of the main challenges to be faced.…”

Section: -Discussionmentioning

confidence: 99%

Batch and Streaming Data Ingestion towards Creating Holistic Health Records

et al. 2023

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The healthcare sector has been moving toward Electronic Health Record (EHR) systems that produce enormous amounts of healthcare data due to the increased emphasis on getting the appropriate information to the right person, wherever they are, at any time. This highlights the need for a holistic approach to ingest, exploit, and manage these huge amounts of data for achieving better health management and promotion in general. This manuscript proposes such an approach, providing a mechanism allowing all health ecosystem entities to obtain actionable knowledge from heterogeneous data in a multimodal way. The mechanism includes diverse techniques for automatically ingesting healthcare-related information from heterogeneous sources that produce batch/streaming data, managing, fusing, and aggregating this data into new data structures (i.e., Holistic Health Records (HHRs)). The latter enable the aggregation of data coming from different sources, such as Internet of Medical Things (IoMT) devices, online/offline platforms, while to effectively construct the HHRs, the mechanism develops various data management techniques covering the overall data path, from data acquisition and cleaning to data integration, modelling, and interpretation. The mechanism has been evaluated upon different healthcare scenarios, ranging from hospital-retrieved data to patient platforms, combined with data obtained from IoMT devices, having produced useful insights towards its successful and wide adaptation in this domain. In order to implement a paradigm shift from heterogeneous and independent data sources, limited data exploitation, and health records, the mechanism has combined multidisciplinary technologies toward HHRs. Doi: 10.28991/ESJ-2023-07-02-03 Full Text: PDF

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“…Although these works highlight the potential for study of personal and structural SDoH, there is considerable effort for systematically collecting, linking, and analyzing SDoH data from external sources together with EHR data at the community, state, and national levels [84,97,98]. Few studies have incorporated the use of common data models for improving standardization and interoperability of collected SDoH data [99][100][101].…”

Section: Challenges and Opportunitiesmentioning

confidence: 99%

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

Li,

Mowery,

et al. 2024

Preprint

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Background Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality. Methods We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions. Results We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes. Discussion Despite progress in identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfilling the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

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An Autoscaling Platform Supporting Graph Data Modelling Big Data Analytics

Cited by 15 publications

References 6 publications

Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing

Systematic analysis of healthcare big data analytics for efficient care and disease diagnosing

Batch and Streaming Data Ingestion towards Creating Holistic Health Records

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

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