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

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'''"[[Journal:The evolution, use, and effects of integrated personal health records: A narrative review|The evolution, use, and effects of integrated personal health records: A narrative review]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Objective: To present a summarized literature review of the evolution, use, and effects of Personal Health Records (PHRs).  
[[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 />
 
''Recently featured'':
Methods: Medline and PubMed were searched for ‘personal health records’. Seven hundred thirty-three references were initially screened resulting in 230 studies selected as relevant based on initial title and abstract review. After further review, a total of 52 articles provided relevant information and were included in this paper. These articles were reviewed by one author and grouped into the following categories: PHR evolution and adoption, patient user attitudes toward PHRs, patient reported barriers to use, and the role of PHRs in self-management.
{{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]]
Results: Eleven papers described evolution and adoption, 17 papers described PHR user attitudes, 10 papers described barriers to use, and 11 papers described PHR use in self-management. Three papers were not grouped into a category but were used to inform the Discussion. PHRs have evolved from patient-maintained paper health records to provider-linked [[electronic health record]]s. Patients report enthusiasm for the potential of modern PHRs, yet few patients actually use an electronic PHR. Low patient adoption of PHRs is associated with poor interface design and low health and computer literacy on the part of patient users. ('''[[Journal:The evolution, use, and effects of integrated personal health records: A narrative review|Full article...]]''')<br />
* [[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]]
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''Recently featured'': [[Journal:Undertaking sociotechnical evaluations of health information technologies|Undertaking sociotechnical evaluations of health information technologies]], [[Journal:Basics of case report form designing in clinical research|Basics of case report form designing in clinical research]], [[Journal:Why health services research needs geoinformatics: Rationale and case example|Why health services research needs geoinformatics: Rationale and case example]]

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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