Difference between revisions of "Template:Article of the week"

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'''"[[Journal:Learning health systems need to bridge the "two cultures" of clinical informatics and data science|Learning health systems need to bridge the "two cultures" of clinical informatics and data science]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


United Kingdom (U.K.) health research policy and plans for population health management are predicated upon transformative knowledge discovery from operational "big data." Learning health systems require not only data but also feedback loops of knowledge into changed practice. This depends on [[Information management|knowledge management]] and application, which in turn depends upon effective system design and implementation. [[Health informatics|Biomedical informatics]] is the interdisciplinary field at the intersection of health science, social science, and information science and technology that spans this entire scope.
[[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 />
 
In the U.K., the separate worlds of health data science ([[bioinformatics]], big data) and effective healthcare system design and implementation ([[Health informatics#Clinical informatics|clinical informatics]], "digital health") have operated as "two cultures." Much National Health Service and social care data is of very poor quality. Substantial research funding is wasted on data cleansing or by producing very weak evidence. There is not yet a sufficiently powerful professional community or evidence base of best practice to influence the practitioner community or the digital health industry. ('''[[Journal:Learning health systems need to bridge the "two cultures" of clinical informatics and data science|Full article...]]''')<br />
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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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