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This assignment focuses on ontology application to healthcare. Below are several articles for you to review. You can use one of these articles or select a different one. You need to write an essay on...

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This assignment focuses on ontology application to healthcare. Below are several articles for you to review. You can use one of these articles or select a different one. You need to write an essay on how ontologies are applied to healthcare. Be sure to discuss ontologies in healthcare in general, then provide a specific example of an ontology already used in healthcare from your selected article. You will need to provide a summary of your article. Be sure to discuss what the ontology is, how it was created, what could be changed to make it better.

Normalization and Standardization of Electronic Health Records for High-throughput Phenotyping: the SHARPn ConsortiumDownload Normalization and Standardization of Electronic Health Records for High-throughput Phenotyping: the SHARPn Consortium

The Human Phenotype Ontology Project: Linking Molecular Biology and Disease Through Phenotype DataDownload The Human Phenotype Ontology Project: Linking Molecular Biology and Disease Through Phenotype Data

Gene Ontology Annotations and Resources

An Ontology-driven, Diagnostic Modeling SystemDownload An Ontology-driven, Diagnostic Modeling System

Knowledge Retrieval from PubMed Abstracts and Electronic Medical Records with the Multiple Sclerosis OntologyDownload Knowledge Retrieval from PubMed Abstracts and Electronic Medical Records with the Multiple Sclerosis Ontology

Ontology-guided Organ Detection to Retrieve We Images of Disease Manifestation: Towards the Construction of a Consumer-based Health Image LibraryDownload Ontology-guided Organ Detection to Retrieve We Images of Disease Manifestation: Towards the Construction of a Consumer-based Health Image Library

Grading

Only peer reviewed articles will be accepted for full credit. The minimum word count requirement is 600 words, but no more than 1000 words. The report must be prepared in an essay form; not as a list of bullet points. For a grading breakdown, please review the rubric below.

Rubric

Assignment Rubric (1) (1)
Assignment Rubric (1) (1)
CriteriaRatingsPts
This criterion is linked to a Learning OutcomeName/Font/Formatting
5pts
Excellent
Includes the student’s name, uses fonts and formatting consistently throughout the report
3pts
Good
Includes the student’s name, uses fonts and formatting consistently throughout the report
2pts
Acceptable
Uses different fonts and multiple font sizes or does not include the student’s name/heading
0pts
Unacceptable
N/A
5pts
This criterion is linked to a Learning OutcomeLength
10pts
Excellent
Exceeds by 100 words the minimum word count
3pts
Good
Meets the minimum word count
2pts
Acceptable
Does not meet the minimum word count
0pts
Unacceptable
Does not meet the minimum word count by more than 100 words
10pts
This criterion is linked to a Learning OutcomeIntroduction
10pts
Excellent
Presents a detailed background to what the paper is about.
3pts
Good
Minimally discusses what the paper is about.
2pts
Acceptable
Introduction does not relate to the paper.
0pts
Unacceptable
Does not include an introduction
10pts
This criterion is linked to a Learning OutcomePaper Summary
20pts
Excellent
Presents a detailed summary on the paper.
12pts
Good
Presents a summary on the paper.
8pts
Acceptable
Presents a short summary on the paper.
0pts
Unacceptable
Does not include a summary.
20pts
This criterion is linked to a Learning OutcomeBibliography
10pts
Excellent
Includes a bibliography and in-text citations that are formatted properly. Two additional sources are cited in APA format.
3pts
Good
Includes a bibliography formatted properly and two additional sources are cited.
2pts
Acceptable
Includes a bibliography and in-text citations but 1-3 formatting errors are present
0pts
Unacceptable
Includes a bibliography and in-text citations but 4 f formatting errors are present
10pts
This criterion is linked to a Learning OutcomeFlow/Clarity of Writing
10pts
Excellent
Has no grammatical/spelling formatting errors. The text goes from general ideas to specific conclusions. Transitions tie sections/paragraphs together. Writing is crisp, clear, and succinct. The author incorporates the active voice when appropriate. Meaning is explicit
2pts
Acceptable
Has major grammatical/spelling errors. References are not formatted properly. There is a basic flow from one section to the next, but not all sections or paragraphs follow in a natural or logical order. Hard to know what the author is trying to express.
0pts
Unacceptable
Has major grammatical/spelling errors. References are not included. The report appears to have no direction, with sections/paragraph appearing disjointed.
10pts
This criterion is linked to a Learning OutcomeApplication to Healthcare
20pts
Excellent
Presents a detailed summary as to how the ontology can be applied to healthcare
10pts
Good
Presents a summary as to how the ontology can be applied to healthcare
5pts
Accpetable
Briefly discusses the ontology's use in healthcare but it is lacking detail and discussion
0pts
Unacceptable
Does not discuss the onotology application to healthcare
20pts
This criterion is linked to a Learning OutcomeOntology Example
15pts
Excellent
In detail, discusses a specific ontology example, discusses its creation and how it can be improved.
8pts
Good
Discusses a specifc ontology example, but does not fully explain its creation or how it can be improved.
4pts
Acceptable
Mentions an ontology example but does not elaborate on its creation or how it can be improved
0pts
Unacceptable
Only briefly mentions an example of an ontology
15pts
Total Points:100
Answered 7 days After Oct 31, 2022

Solution

Dr Insiyah R. answered on Nov 07 2022
44 Votes
Article: Ontology-guided organ detection to retrieve web images of disease manifestation: towards the construction of a consumer-based health image li
ary
Introduction    1
Conclusion    3
Reference    4
Introduction
In medicine and practical applications, both textual and graphic information are essential. Natural language and graphics are needed to describe illnesses, medical procedures, and other topics that are important to doctors, carers, and patients. circumstances and methods Researchers have built and recycled a variety of elaborate models to databases of medical terminology texts like the Integrated Medical Language System (UMLS). Conversely, there has been a dearth of research attempting to standardise medical information visually (Bukhari et al,2015). Several medical picture databases focus on a particular topic, such as a database of lung CT scans or an archive of cardiovascular MRI scans or a database of human anatomy photographs. This is primarily due to the time-consuming and arduous nature of the picture-gathering methods, which severely restricts the size of these databases (Chen et al,2013). As an added bonus, they comment on pictures with phrases written in a common language, which might cause confusion while searching for pictures. Finally, most existing picture databases are behind paywalls.
When it comes to encoding useful information in biomedicine, ontologies have shown to be particularly effective. The ease with which they may be used in the reasoning processes by medical decision assistance systems and the formal but straightforward, powerful, and incremental nature of the biological information they record are two explanations for this success (Kim et al,2014).
Our ultimate objective is to create a patient-centric, large-scale health picture base that is available online without charge and that includes photos of human illness symptoms, organs, medications, and other medical entities. Our picture database will follow the UMLS template and be annotated with words from established medical ontologies like the FMA, which is a departure from prior efforts (Gurcan et al,2017).
For this reason, researchers focus on collecting photographic pictures, which make up a significant portion of all biological images, since the information base is intended for laypeople. As far as researchers know, their first attempt is to develop a large-scale medical picture base with attached ontology words (Zillner et al,2014).
The most challenging part of creating this picture database is amassing a significant number of trustworthy photos for millions of medical phrases. The Internet is a rapidly expanding, widely accessible, and free...
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