On Robust Methodologies for Managing Public Health  Care Systems

Nimmagadda, Shastri L.; Dreher, Heinz

doi:10.3390/ijerph110101106

Cited by 6 publications

(3 citation statements)

References 13 publications

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“…Nimmagadda et al [137] developed a robust back-end application for web-based patient–doctor consultations and e-Health care, based on ontology-based multidimensional data warehousing and mining methodologies, while Renard et al [138] developed DIABECOLUX, an algorithm for the prediction of treated T2D patients via health insurance claims, when no diagnosis code is available. Similarly, in [139], data from electronic health records and financial billing systems were used to produce integrated patient-based datasets.…”

Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

Machine Learning and Data Mining Methods in Diabetes Research

Kavakiotis

Tsave

Salifoglou

et al. 2017

Computational and Structural Biotechnology Journal

1,032

530

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The remarkable advances in biotechnology and health sciences have led to a significant production of data, such as high throughput genetic data and clinical information, generated from large Electronic Health Records (EHRs). To this end, application of machine learning and data mining methods in biosciences is presently, more than ever before, vital and indispensable in efforts to transform intelligently all available information into valuable knowledge. Diabetes mellitus (DM) is defined as a group of metabolic disorders exerting significant pressure on human health worldwide. Extensive research in all aspects of diabetes (diagnosis, etiopathophysiology, therapy, etc.) has led to the generation of huge amounts of data. The aim of the present study is to conduct a systematic review of the applications of machine learning, data mining techniques and tools in the field of diabetes research with respect to a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and e) Health Care and Management with the first category appearing to be the most popular. A wide range of machine learning algorithms were employed. In general, 85% of those used were characterized by supervised learning approaches and 15% by unsupervised ones, and more specifically, association rules. Support vector machines (SVM) arise as the most successful and widely used algorithm. Concerning the type of data, clinical datasets were mainly used. The title applications in the selected articles project the usefulness of extracting valuable knowledge leading to new hypotheses targeting deeper understanding and further investigation in DM.

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Section: Dm Through Machine Learning and Data Miningmentioning

confidence: 99%

Machine Learning and Data Mining Methods in Diabetes Research

Kavakiotis

Tsave

Salifoglou

et al. 2017

Computational and Structural Biotechnology Journal

1,032

530

View full text Add to dashboard Cite

show abstract

“…Much attention has been paid during the last several years to develop and put into practice ontologies and web services to achieve better representations or to build knowledge-based infrastructure for decision-making [ 36 , 37 , 38 ]. Putting in practice clinical standards to deal with interoperability at different levels of the e-health communication infrastructure for different e-health applications has become a promising line or research [ 15 , 39 ].…”

Section: Discussionmentioning

confidence: 99%

An Interoperable System toward Cardiac Risk Stratification from ECG Monitoring

Soguero-Ruíz

Mora-Jiménez

Ramos-López

et al. 2018

IJERPH

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Many indices have been proposed for cardiovascular risk stratification from electrocardiogram signal processing, still with limited use in clinical practice. We created a system integrating the clinical definition of cardiac risk subdomains from ECGs and the use of diverse signal processing techniques. Three subdomains were defined from the joint analysis of the technical and clinical viewpoints. One subdomain was devoted to demographic and clinical data. The other two subdomains were intended to obtain widely defined risk indices from ECG monitoring: a simple-domain (heart rate turbulence (HRT)), and a complex-domain (heart rate variability (HRV)). Data provided by the three subdomains allowed for the generation of alerts with different intensity and nature, as well as for the grouping and scrutinization of patients according to the established processing and risk-thresholding criteria. The implemented system was tested by connecting data from real-world in-hospital electronic health records and ECG monitoring by considering standards for syntactic (HL7 messages) and semantic interoperability (archetypes based on CEN/ISO EN13606 and SNOMED-CT). The system was able to provide risk indices and to generate alerts in the health records to support decision-making. Overall, the system allows for the agile interaction of research and clinical practice in the Holter-ECG-based cardiac risk domain.

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“…To store and integrate multidimensional and heterogeneous data (e.g., diabetes, food, nutrients) applied to diabetes management, but generalizable to other diseases researchers [ 130 ] proposed an intelligent information management framework. Their proposed methodology is a robust back-end application for web-based patient-doctor consultation and e-Health care management systems with implications for cost savings.…”

Section: Application Of Analytics In Healthcarementioning

confidence: 99%

A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining

et al. 2018

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The growing healthcare industry is generating a large volume of useful data on patient demographics, treatment plans, payment, and insurance coverage—attracting the attention of clinicians and scientists alike. In recent years, a number of peer-reviewed articles have addressed different dimensions of data mining application in healthcare. However, the lack of a comprehensive and systematic narrative motivated us to construct a literature review on this topic. In this paper, we present a review of the literature on healthcare analytics using data mining and big data. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a database search between 2005 and 2016. Critical elements of the selected studies—healthcare sub-areas, data mining techniques, types of analytics, data, and data sources—were extracted to provide a systematic view of development in this field and possible future directions. We found that the existing literature mostly examines analytics in clinical and administrative decision-making. Use of human-generated data is predominant considering the wide adoption of Electronic Medical Record in clinical care. However, analytics based on website and social media data has been increasing in recent years. Lack of prescriptive analytics in practice and integration of domain expert knowledge in the decision-making process emphasizes the necessity of future research.

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On Robust Methodologies for Managing Public Health Care Systems

Cited by 6 publications

References 13 publications

Machine Learning and Data Mining Methods in Diabetes Research

Machine Learning and Data Mining Methods in Diabetes Research

An Interoperable System toward Cardiac Risk Stratification from ECG Monitoring

A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining

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