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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 González-FerrerIJIMAI2018 4-7.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
'''"[[Journal:Generating big data sets from knowledge-based decision support systems to pursue value-based healthcare|Generating big data sets from knowledge-based decision support systems to pursue value-based healthcare]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Talking about big data in healthcare we usually refer to how to use data collected from current [[electronic medical record]]s, either structured or unstructured, to answer clinically relevant questions. This operation is typically carried out by means of analytics tools (e.g., machine learning) or by extracting relevant data from patient summaries through natural language processing techniques. From other perspectives of research in [[medical informatics]], powerful initiatives have emerged to help physicians make decisions, in both diagnostics and therapeutics, built from existing medical evidence (i.e., knowledge-based [[Clinical decision support system|decision support systems]]). Many of the problems these tools have shown, when used in real clinical settings, are related to their implementation and deployment, more than failing in their support; however, technology is slowly overcoming interoperability and integration issues. Beyond the point-of-care decision support these tools can provide, the data generated when using them, even in controlled trials, could be used to further analyze facts that are traditionally ignored in the current clinical practice. In this paper, we reflect on the technologies available to make the leap and how they could help drive healthcare organizations shifting to a value-based healthcare philosophy. ('''[[Journal:Generating big data sets from knowledge-based decision support systems to pursue value-based healthcare|Full article...]]''')<br />
[[Chromatography|Chromatographic]] oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of [[convolutional neural network]]s (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The [[open-source software]] Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... ('''[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Full article...]]''')<br />
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Latest revision as of 13:37, 13 May 2024

Fig1 Bispo-Silva Geosciences23 13-11.png

"Geochemical biodegraded oil classification using a machine learning approach"

Chromatographic oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of convolutional neural networks (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The open-source software Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... (Full article...)
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