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'''[[Health informatics]]''' (also called '''health care informatics''', '''healthcare informatics''', '''medical informatics''', '''nursing informatics''',  '''clinical informatics''', or '''biomedical informatics''') is a discipline at the intersection of [[information science]], computer science, and health care. It deals with the resources, devices, and methods required to optimize the "collection, storage, retrieval, [and] communication ... of health-related data, [[information]], and knowledge." Health informatics is applied to the areas of nursing, clinical care, dentistry, pharmacy, public health, occupational therapy, and biomedical research. Health informatics resources include not only computers but also clinical guidelines, formal medical terminologies, and information and communication systems.
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Worldwide use of technology in medicine began in the early 1950s with the rise of computers. Medical informatics research units began to appear during the 1970s in Poland and in the U.S., with medical informatics conferences springing up as early as 1974. Since then the development of high-quality health informatics research, education, and infrastructure has been the goal of the U.S. and the European Union. Hundreds of datasets, publications, guidelines, specifications, meetings, conferences, and organizations around the world continue to shape what health informatics is today. ('''[[Health informatics |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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''Recently featured'':
''Recently featured'': [[Content delivery network]], [[Federally qualified health center]], [[Home health agency]]
{{flowlist |
* [[Journal:Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study|Knowledge of internal quality control for laboratory tests among laboratory personnel working in a biochemistry department of a tertiary care center: A descriptive cross-sectional study]]
* [[Journal:Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study|Sigma metrics as a valuable tool for effective analytical performance and quality control planning in the clinical laboratory: A retrospective study]]
* [[Journal:Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems|Why do we need food systems informatics? Introduction to this special collection on smart and connected regional food systems]]
}}

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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