Business Process Modelling for a Greek Hospital's Medical Equipment Data Center

Katrakazas, Panagiotis; Costarides, Vassilia; Tarousi, Marilena; Christodoulakis, Manolis; Toumpaniaris, Petros; Pavlopoulos, S.; Iliopoulou, Dimitra; Koutsouris, Dimitrios

doi:10.1109/cbms.2018.00064

Cited by 2 publications

(1 citation statement)

References 31 publications

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“…Breast cancer statistics report that nearly 2 million of new cases were diagnosed in 2018; this represents about 12% of all new cancer cases and 25.3% of all cancers in women [2]. For an analysis of the efficiency in each stage of the cancer disease, the diagnosis, registration, and monitoring of diseases allow to validate the most appropriate treatments, including the optimization of costs [3]. The early detection for improving breast cancer outcome and survival remains an open issue.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Cluster Prominence Feature for Detecting Calcifications in Mammograms

Cruz-Bernal

Flores-Barranco

Almanza-Ojeda

et al. 2018

Journal of Healthcare Engineering

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In mammograms, a calcification is represented as small but brilliant white region of the digital image. Earlier detection of malignant calcifications in patients provides high expectation of surviving to this disease. Nevertheless, white regions are difficult to see by visual inspection because a mammogram is a gray-scale image of the breast. To help radiologists in detecting abnormal calcification, computer-inspection methods of mammograms have been proposed; however, it remains an open important issue. In this context, we propose a strategy for detecting calcifications in mammograms based on the analysis of the cluster prominence (cp) feature histogram. The highest frequencies of the cp histogram describe the calcifications on the mammography. Therefore, we obtain a function that models the behaviour of the cp histogram using the Vandermonde interpolation twice. The first interpolation yields a global representation, and the second models the highest frequencies of the histogram. A weak classifier is used for obtaining a final classification of the mammography, that is, with or without calcifications. Experimental results are compared with real DICOM images and their corresponding diagnosis provided by expert radiologists, showing that the cp feature is highly discriminative.

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Section: Introductionmentioning

confidence: 99%

Analysis of the Cluster Prominence Feature for Detecting Calcifications in Mammograms

Cruz-Bernal

Flores-Barranco

Almanza-Ojeda

et al. 2018

Journal of Healthcare Engineering

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Big Data Applications in Healthcare Administration

Kasten

2022

Research Anthology on Big Data Analytics, Architectures, and Applications

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The healthcare industry has a growing record of using big data-related technologies such as data analytics, internet of things, and machine learning, especially in the clinical areas. However, healthcare institutions must also perform all of the administrative processes just as any other organization. Thus, like many other industries, healthcare has begun to apply these same technologies to improve their understanding of these internal operations and use them to make better decisions and run a more effective operation. This study takes a structured literature review approach to describe the current state of this literature and identify the major themes and priorities of both the research community and the healthcare industry as a whole. The contribution made by this study is to provide a comprehensive analysis of the state of the literature to use as a foundation for the future research opportunities noted in the paper.

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Business Process Modelling for a Greek Hospital's Medical Equipment Data Center

Cited by 2 publications

References 31 publications

Analysis of the Cluster Prominence Feature for Detecting Calcifications in Mammograms

Analysis of the Cluster Prominence Feature for Detecting Calcifications in Mammograms

Big Data Applications in Healthcare Administration

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