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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Johannes Cordua Arzt in seinem Studierzimmer.jpg|160px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
A '''[[physician office laboratory]]''' ('''POL''') is a physician-, partnership-, or group-maintained [[laboratory]] that performs diagnostic tests or examines specimens in order to diagnose, prevent, and/or treat a disease or impairment in a patient as part of the physician practice. The POL shows up in primary care physician offices as well as the offices of specialists like urologists, hematologists, gynecologists, and endocrinologists. In many countries like the United States, the physician office laboratory is considered a [[clinical laboratory]] and is thus regulated by federal, state, and/or local laws affecting such laboratories.
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


The workflow of a POL is similar to other clinical labs; the difference in workflows mostly comes down to the time spent in transporting the specimen to an outside lab and waiting for the processing. The in-office lab saves time in those parts of the process. Potential benefits of a POL include quicker access to test results for the clinician, greater efficiency of the clinical workflow, cheaper testing, and greater patient comfort and happiness. Potential disadvantages include the physician office being the only point-of-access, patients not feeling comfortable about the physician's office being the central repository of information, and the cost of meeting compliance requirements for local, state, and federal regulations. ('''[[Physician office laboratory|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'': [[United States Department of Health and Human Services]], [[Bioimage informatics]], [[Biobank]]
{{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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