國立虎尾科技大學 |

A self-organizing map approach for hospital data analysis.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	A self-organizing map approach for hospital data analysis./
作者:	Pourkia, Javid.
面頁冊數:	1 online resource (95 pages)
附註:	Source: Masters Abstracts International, Volume: 54-03.
Contained By:	Masters Abstracts International54-03(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9781321600360

A self-organizing map approach for hospital data analysis.
Pourkia, Javid.

A self-organizing map approach for hospital data analysis. - 1 online resource (95 pages)

Source: Masters Abstracts International, Volume: 54-03.

Thesis (M.S.)--Southern Illinois University at Carbondale, 2014.

Includes bibliographical references

In this work, we utilize Self Organized Maps (SOM) to cluster and classify hospital related data with large dimensions, provided by Medicare website. These data have published every year and it includes numerous measures for each hospital in the nationwide. It might be possible to unearth some correlations in health-care industry by being able to interpreting this dataset, for example by examining the relations between data of immunizations department to readmission records and hospital expenses. It is not feasible to make any sense from these measures altogether using traditional methods (2D or 3D charts, diagrams or graphs, different tables), because as a result of being human, we cannot comprehend more than 3 dimensions with naked eyes. Since it would be very useful if we could correlate the dimensions to each other to discover new patterns and knowledge, SOMs are a type of Artificial Neural Networks that can be trained using unsupervised learning to illustrate complex and high dimensional data by generating a low dimension representation of the training sample. This way, a powerful and easy-to-interpret visualization will be provided for healthcare officials to rapidly identify the correlation between different attributes of the dataset using clusters illustration.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781321600360Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

A self-organizing map approach for hospital data analysis.
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500 $a Adviser: Shahram Rahimi.
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504 $a Includes bibliographical references
520 $a In this work, we utilize Self Organized Maps (SOM) to cluster and classify hospital related data with large dimensions, provided by Medicare website. These data have published every year and it includes numerous measures for each hospital in the nationwide. It might be possible to unearth some correlations in health-care industry by being able to interpreting this dataset, for example by examining the relations between data of immunizations department to readmission records and hospital expenses. It is not feasible to make any sense from these measures altogether using traditional methods (2D or 3D charts, diagrams or graphs, different tables), because as a result of being human, we cannot comprehend more than 3 dimensions with naked eyes. Since it would be very useful if we could correlate the dimensions to each other to discover new patterns and knowledge, SOMs are a type of Artificial Neural Networks that can be trained using unsupervised learning to illustrate complex and high dimensional data by generating a low dimension representation of the training sample. This way, a powerful and easy-to-interpret visualization will be provided for healthcare officials to rapidly identify the correlation between different attributes of the dataset using clusters illustration.
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