Big Data in Health: a Literature Review from the Year 2005

Díez, Isabel Torre; Cosgaya, Héctor Merino; Garcia-Zapirain, Begonya; López-Coronado, Miguel

doi:10.1007/s10916-016-0565-7

Cited by 34 publications

(23 citation statements)

References 45 publications

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“…However, there is no predefined validated model that works efficiently and effectively on all databases. One or several algorithms must be selected to create a model and later validate it to ensure optimal functions, according to the type of data and the output variable being predicted [9,10].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Prevalence of the Sarcopenia Level Using Machine Learning Techniques: Case Study in Tijuana Baja California, Mexico

Cristian

Zúñiga

2020

IJERPH

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The article presents a study based on timeline data analysis of the level of sarcopenia in older patients in Baja California, Mexico. Information was examined at the beginning of the study (first event), three months later (second event), and six months later (third event). Sarcopenia is defined as the loss of muscle mass quality and strength. The study was conducted with 166 patients. A total of 65% were women and 35% were men. The mean age of the enrolled patients was 77.24 years. The research included 99 variables that consider medical history, pharmacology, psychological tests, comorbidity (Charlson), functional capacity (Barthel and Lawton), undernourishment (mini nutritional assessment (MNA) validated test), as well as biochemical and socio-demographic data. Our aim was to evaluate the prevalence of the level of sarcopenia in a population of chronically ill patients assessed at the Tijuana General Hospital. We used machine learning techniques to assess and identify the determining variables to focus on the patients’ evolution. The following classifiers were used: Support Vector Machines, Linear Support Vector Machines, Radial Basis Function, Gaussian process, Decision Tree, Random Forest, multilayer perceptron, AdaBoost, Gaussian Naive Bayes, and Quadratic Discriminant Analysis. In order of importance, we found that the following variables determine the level of sarcopenia: Age, Systolic arterial hypertension, mini nutritional assessment (MNA), Number of chronic diseases, and Sodium. They are therefore considered relevant in the decision-making process of choosing treatment or prevention. Analysis of the relationship between the presence of the variables and the classifiers used to measure sarcopenia revealed that the Decision Tree classifier, with the Age, Systolic arterial hypertension, MNA, Number of chronic diseases, and Sodium variables, showed a precision of 0.864, accuracy of 0.831, and an F1 score of 0.900 in the first and second events. Precision of 0.867, accuracy of 0.825, and an F1 score of 0.867 were obtained in event three with the same variables. We can therefore conclude that the Decision Tree classifier yields the best results for the assessment of the determining variables and suggests that the study population’s sarcopenia did not change from moderate to severe.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Prevalence of the Sarcopenia Level Using Machine Learning Techniques: Case Study in Tijuana Baja California, Mexico

Cristian

Zúñiga

2020

IJERPH

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“…Digitalized data already provide many benefits to healthcare organizations through disease prediction and surveillance, population health management and patient care improvement. Moreover, big data can stimulate innovation, cost and risk reduction and productivity gains [2].…”

Section: Introductionmentioning

confidence: 99%

“…Current research on big data in healthcare is highly interdisciplinary [1], and literature has been rapidly growing since 2011. Big data is a cost reducer for public and private healthcare [2]. The healthcare sector is one of the most promising areas for big data [3].…”

Section: Introductionmentioning

confidence: 99%

Big Data for the Sustainability of Healthcare Project Financing

Visconti

Morea

2019

Sustainability

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This study aims to detect if and how big data can improve the quality and timeliness of information in infrastructural healthcare Project Finance (PF) investments, making them more sustainable, and increasing their overall efficiency. Interactions with telemedicine or disease management and prediction are promising but are still underexploited. However, given rising health expenditure and shrinking budgets, data-driven cost-cutting is inevitably required. An interdisciplinary approach combines complementary aspects concerning big data, healthcare information technology, and PF investments. The methodology is based on a business plan of a standard healthcare Public-Private Partnership (PPP) investment, compared with a big data-driven business model that incorporates predictive analytics in different scenarios. When Public and Private Partners interact through networking big data and interoperable databases, they boost value co-creation, improving Value for Money and reducing risk. Big data can also help by shortening supply chain steps, expanding economic marginality and easing the sustainable planning of smart healthcare investments. Flexibility, driven by timely big data feedbacks, contributes to reducing the intrinsic rigidity of long-termed PF healthcare investments. Healthcare is a highly networked and systemic industry, that can benefit from interacting with big data that provide timely feedbacks for continuous business model re-engineering, reducing the distance between forecasts and actual occurrences. Risk shrinks and sustainability is fostered, together with the bankability of the infrastructural investment.

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“…Big data has firmly established itself in the health research,1,2 illustrated by publications in high-ranking general-interest biomedical journals, including The New England Journal of Medicine ,3 JAMA ,4 Journal of Internal Medicine ,5 Science ,6–9 and Nature 10–13. A basic definition of big data includes “the 3 Vs”: variety (linkage of many data sets from heterogeneous independent sources in a single data set); volume (large number of observations and variables per observation from different sources); and/or velocity (real-time or frequent data updates, often fully or partially automated) 14.…”

Section: Introductionmentioning

confidence: 99%

“…These data have evolved from mere byproducts of health care delivery or billing into a powerful tool for improving patient care through preventive, etiologic, and prognostic epidemiologic research 4. A recent article summarizes 46 most influential studies conducted with big data in health care,1 while a review from 2015 provides multiple examples of the “variety” V in big data for health 2…”

Section: Introductionmentioning

confidence: 99%

Clinical epidemiology in the era of big data: new opportunities, familiar challenges

Ehrenstein¹,

Nielsen²,

Pedersen³

et al. 2017

CLEP

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Routinely recorded health data have evolved from mere by-products of health care delivery or billing into a powerful research tool for studying and improving patient care through clinical epidemiologic research. Big data in the context of epidemiologic research means large interlinkable data sets within a single country or networks of multinational databases. Several Nordic, European, and other multinational collaborations are now well established. Advantages of big data for clinical epidemiology include improved precision of estimates, which is especially important for reassuring (“null”) findings; ability to conduct meaningful analyses in subgroup of patients; and rapid detection of safety signals. Big data will also provide new possibilities for research by enabling access to linked information from biobanks, electronic medical records, patient-reported outcome measures, automatic and semiautomatic electronic monitoring devices, and social media. The sheer amount of data, however, does not eliminate and may even amplify systematic error. Therefore, methodologies addressing systematic error, clinical knowledge, and underlying hypotheses are more important than ever to ensure that the signal is discernable behind the noise.

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Big Data in Health: a Literature Review from the Year 2005

Cited by 34 publications

References 45 publications

Evaluation of Prevalence of the Sarcopenia Level Using Machine Learning Techniques: Case Study in Tijuana Baja California, Mexico

Evaluation of Prevalence of the Sarcopenia Level Using Machine Learning Techniques: Case Study in Tijuana Baja California, Mexico

Big Data for the Sustainability of Healthcare Project Financing

Clinical epidemiology in the era of big data: new opportunities, familiar challenges

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