Big Data Analyses in Health and Opportunities for Research in Radiology

Aphinyanaphongs, Yindalon

doi:10.1055/s-0036-1597255

Cited by 7 publications

(8 citation statements)

References 25 publications

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“…Thus, it is important both to assess and benchmark the quality of the generated data and the methods used to avoid overinterpretation of results, overfitting of the models and generalisation of the results when using big data. The task force also felt that it was important to validate results in independent datasets 24 28. Overall, the task force agreed that conclusions drawn from big data need independent validation (in other datasets) to overcome current limitations and to assure scientific soundness.…”

Section: Resultsmentioning

confidence: 96%

“…This next sentence is informative and aims to present the diversity of data sources leading to big data; we listed in a non-exhaustive way some of the sources of big data. The most common sources of big healthcare data found in the SLR were clinical; these include electronic health records, studies and registries, billing and healthcare system claims databases 21 28 29. A more recent source of clinical big data currently underused in RMDs is the Internet of Things (eg, wearables, apps, medical devices and sensors), but also social media, behavioural and environmental data 18 30 31.…”

Section: Resultsmentioning

confidence: 99%

“…Although there may be a perception that big data are more valid or less subject to bias than traditional studies, model overfitting, inappropriate generalisation of the results and/or bias can in fact lead to inappropriate conclusions 14 18 28. Thus, it is important both to assess and benchmark the quality of the generated data and the methods used to avoid overinterpretation of results, overfitting of the models and generalisation of the results when using big data.…”

Section: Resultsmentioning

confidence: 99%

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EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases

Gossec

Kedra

Servy³

et al. 2019

Ann Rheum Dis

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BackgroundTremendous opportunities for health research have been unlocked by the recent expansion of big data and artificial intelligence. However, this is an emergent area where recommendations for optimal use and implementation are needed. The objective of these European League Against Rheumatism (EULAR) points to consider is to guide the collection, analysis and use of big data in rheumatic and musculoskeletal disorders (RMDs).MethodsA multidisciplinary task force of 14 international experts was assembled with expertise from a range of disciplines including computer science and artificial intelligence. Based on a literature review of the current status of big data in RMDs and in other fields of medicine, points to consider were formulated. Levels of evidence and strengths of recommendations were allocated and mean levels of agreement of the task force members were calculated.ResultsThree overarching principles and 10 points to consider were formulated. The overarching principles address ethical and general principles for dealing with big data in RMDs. The points to consider cover aspects of data sources and data collection, privacy by design, data platforms, data sharing and data analyses, in particular through artificial intelligence and machine learning. Furthermore, the points to consider state that big data is a moving field in need of adequate reporting of methods and benchmarking, careful data interpretation and implementation in clinical practice.ConclusionThese EULAR points to consider discuss essential issues and provide a framework for the use of big data in RMDs.

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Section: Resultsmentioning

confidence: 96%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases

Gossec

Kedra

Servy³

et al. 2019

Ann Rheum Dis

View full text Add to dashboard Cite

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“…The total utility under the condition of referral is lower than the total utility under the traditional mode. (3)(4)(5)(6)(7)(8)(9)(10) Therefore, the HMS of first-class hospitals and secondclass hospitals both choose the clinical reception strategy.…”

Section: In Case Of the Hierarchical Medical System(hms)mentioning

confidence: 99%

“…When the accuracy of disease analysis is improved under the support of big data,p (c z )will be infinitely close to 1, and formula (3-11) can be reduced to: (3)(4)(5)(6)(7)(8)(9)(10)(11)(12) and c Z m 1 < m 2 , thus:…”

Section: ) the Influence Of Medical Knowledge Spilloversmentioning

confidence: 99%

Knowledge Spillovers of Medical Big Data Under Hierarchical Medical System and Patients’ Medical Treatment Decisions

et al. 2019

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In China, there are so many patients go to the highest-level hospitals to get medical treatment directly, it is essential to study how to guide patients to take the initiative to go to the basic medical institutions first in most cases. The information asymmetry theory and the game model are applied for rational analysis in this paper. We construct two models, a doctor-patient signaling game model to study the dynamic process of patients who seek medical treatment to hospitals that are trustworthy, and a two-level treatment game model to study how to achieve a hierarchical diagnosis and reasonable referral in the case of different types of patients (general and serious conditions), considering the knowledge spillover variables supported by big data to analyze the changes in the total utility of the medical market, hospitals, and patients. With the first model analysis, it points out that only when there are three preconditions, namely, high disguising cost, controlling the income of diagnosis and treatment, and reducing the losses caused by untrustworthy behaviors, can trust signals play a real role and achieve the coordination of the optimal market type. In addition to the second model analysis, it is found that the hierarchical medical system with optimizing the allocation of medical resources has a positive effect on the benefit of the medical system and the improvement of the total welfare of patients. It also shows that the full application of big data system can further enhance the total benefit of the medical system under this system. This paper gives management suggestions for strengthening information transmission, rational design of communication system, promotion and application of big data intelligent diagnosis, and treatment for medical management departments and medical institutions at all levels to make decisions. INDEX TERMS Tiered diagnosis and treatment, big data in medicine, knowledge spillovers, signaling game.

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Advances in FAI Imaging: a Focused Review

Mascarenhas

Caetano

Dantas

et al. 2020

Curr Rev Musculoskelet Med

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Purpose of Review Femoroacetabular impingement (FAI) is one of the main causes of hip pain in young adults and poses clinical challenges which have placed it at the forefront of imaging and orthopedics. Diagnostic hip imaging has dramatically changed in the past years, with the arrival of new imaging techniques and the development of magnetic resonance imaging (MRI). This article reviews the current state-of-the-art clinical routine of individuals with suspected FAI, limitations, and future directions that show promise in the field of musculoskeletal research and are likely to reshape hip imaging in the coming years. Recent Findings The largely unknown natural disease course, especially in hips with FAI syndrome and those with asymptomatic abnormal morphologies, continues to be a problem as far as diagnosis, treatment, and prognosis are concerned. There has been a paradigm shift in recent years from bone and soft tissue morphological analysis towards the tentative development of quantitative approaches, biochemical cartilage evaluation, dynamic assessment techniques and, finally, integration of artificial intelligence (AI)/deep learning systems. Summary Imaging, AI, and hip preserving care will continue to evolve with new problems and greater challenges. The increasing number of analytic parameters describing the hip joint, as well as new sophisticated MRI and imaging analysis, have carried practitioners beyond simplistic classifications. Reliable evidence-based guidelines, beyond differentiation into pure instability or impingement, are paramount to refine the diagnostic algorithm and define treatment indications and prognosis. Nevertheless, the boundaries of morphological, functional, and AI-aided hip assessment are gradually being pushed to new frontiers as the role of musculoskeletal imaging is rapidly evolving.

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Big Data Analyses in Health and Opportunities for Research in Radiology

Cited by 7 publications

References 25 publications

EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases

EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases

Knowledge Spillovers of Medical Big Data Under Hierarchical Medical System and Patients’ Medical Treatment Decisions

Advances in FAI Imaging: a Focused Review

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