Data mining and visualization for decision support and modeling of public health-care resources

Lavrač, Nada; Bohanec, Marko; Pur, Aleksander; Cestnik, Bojan; Debeljak, Marko; Kobler, Andrej

doi:10.1016/j.jbi.2006.10.003

Cited by 63 publications

(24 citation statements)

References 5 publications

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“…Visual Data Mining can integrate the human in the data exploration process and can be seen as a hypothesis generation process based on visualisations [41]. Data Mining analysis is also being applied to clinical data in conjunction with visualisation techniques in order to extract knowledge, for example by identifying outliers and deviations in health care data [42]. For clinical pathways, pathway mining is also prominent and often associated with process mining using clinical workflow logs to discover medical behaviour and patterns [4].…”

Section: State-of-the-artmentioning

confidence: 99%

Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice

Bettencourt‐Silva

Mannu

Iglesia

2016

Lecture Notes in Computer Science

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Abstract. Routinely collected data in hospital Electronic Medical Records (EMR) is rich and abundant but often not linked or analysed for purposes other than direct patient care. We have created a methodology to integrate patient-centric data from different EMR systems into clinical pathways that represent the history of all patient interactions with the hospital during the course of a disease and beyond. In this paper, the literature in the area of data visualisation in healthcare is reviewed and a method for visualising the journeys that patients take through care is discussed. Examples of the hidden knowledge that could be discovered using this approach are explored and the main application areas of visualisation tools are identified. This paper also highlights the challenges of collecting and analysing such data and making the visualisations extensively used in the medical domain. This paper starts by presenting the state-of-the-art in visualisation of clinical and other health related data. Then, it describes an example clinical problem and discusses the visualisation tools and techniques created for the utilisation of these data by clinicians and researchers. Finally, we look at the open problems in this area of research and discuss future challenges.

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Section: State-of-the-artmentioning

confidence: 99%

Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice

Bettencourt‐Silva

Mannu

Iglesia

2016

Lecture Notes in Computer Science

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“…with a large number of features) are extremely useful for data exploration, inference and decision making [7,8]. Standard visualization methods use classical multidimensional scaling [9], linear transformations that project multidimensional data into two or three dimensions, while preserving the relative distances between data points.…”

Section: Introductionmentioning

confidence: 99%

Visualizing nationwide variation in Medicare Part D prescribing patterns

Rosenberg

Fucile

White

et al. 2017

Preprint

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Objective To characterize the regional and national variation in prescribing patterns in the Medicare Part D program using machine learning and dimensional reduction visualization methods. Methods Using publicly available Medicare Part D claims data, we identified regional and national provider prescribing profile variation with unsupervised clustering and t-distributed stochastic neighbor embedding (t-SNE) dimensional reduction techniques. Additionally, we examined differences between regionally representative prescribing patterns for major metropolitan areas. Results Distributions of prescribing volume and medication diversity were highly skewed among over 800,000 Medicare Part D providers, and medical specialties had characteristic prescribing patterns. Although the number of Medicare providers in each state was highly correlated with the number of Medicare Part D enrollees, some states were enriched for providers with >10,000 prescription claims annually. Hierarchical clustering and t-SNE dimension-reduction visualization of drug-or drug-class prescribing patterns revealed that providers cluster strongly based on specialty and sub-specialty, with large regional variations in prescribing patterns. Major metropolitan areas had distinct prescribing patterns that tended to group by major geographical divisions. Conclusions There is substantial prescribing variation among providers in Medicare Part D both between and within specialties. Large regional variations in prescribing patterns, particularly among major metropolitan areas, were also seen. Unsupervised clustering and t-SNE dimension-reduction are an effective means to examine variation in provider prescribing patterns, including substantial regional and medical specialty variation.

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“…For example, the World Health Organization provides worldwide data related to different health themes. However, there is no specific report to present worldwide health-care expenditure by country 4 . To the best of our knowledge, in the Brazilian scenario, there is no software suite available to help citizens to keep track of SUS expenditures.…”

Section: Introductionmentioning

confidence: 99%

“…The software systems provided by the Ministry of Health are not integrated with one another and the way data is available requires a thorough knowledge of the data set structure and organization. In one of these studies, the authors analyzed atypical occurrences in terms of availability and accessibility to public health services in Slovenia [4]. For this purpose, they applied data mining techniques and visualization resources to represent the results.…”

Section: Introductionmentioning

confidence: 99%

An Infrastructure to Support the Visual Analysis of Data from the Brazilian Public Health System

Nascimento¹,

Carneiro²,

Costa³

et al. 2017

JSW

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Understanding how Brazilian health units have been reimbursed due to services provided on behalf of the Brazilian Public Unified Health System (SUS) is not a simple task. It requires the analysis of a myriad of data and hampers the identification of flaws as well as improvement opportunities regarding the budget application in the context of health public services. In this paper, we proposed an infrastructure to support the visual analysis of data from SUS related to services provided by licensed health units. To illustrate the use of proposed infrastructure, we conducted an example of use that analyzes the performance of licensed health units from the Brazilian Northeast region in a specific time interval.

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Data mining and visualization for decision support and modeling of public health-care resources

Cited by 63 publications

References 5 publications

Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice

Visualisation of Integrated Patient-Centric Data as Pathways: Enhancing Electronic Medical Records in Clinical Practice

Visualizing nationwide variation in Medicare Part D prescribing patterns

An Infrastructure to Support the Visual Analysis of Data from the Brazilian Public Health System

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