國立虎尾科技大學 |

Healthcare Text Analytics Using Recent MI Techniques and Data Classification Using Aws Cloud MI Services.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Healthcare Text Analytics Using Recent MI Techniques and Data Classification Using Aws Cloud MI Services./
作者:	Movinuddin.
面頁冊數:	1 online resource (63 pages)
附註:	Source: Masters Abstracts International, Volume: 85-06.
Contained By:	Masters Abstracts International85-06.
標題:	Information science. -
電子資源:	click for full text (PQDT)
ISBN:	9798381152463

Healthcare Text Analytics Using Recent MI Techniques and Data Classification Using Aws Cloud MI Services.
Movinuddin.

Healthcare Text Analytics Using Recent MI Techniques and Data Classification Using Aws Cloud MI Services. - 1 online resource (63 pages)

Source: Masters Abstracts International, Volume: 85-06.

Thesis (M.S.)--Middle Tennessee State University, 2023.

Includes bibliographical references

Classification of clinical texts has a significant impact on disease diagnosis, medical research and automated development of disease ontologies. Because they contain terms that describe medical concepts and terminology, the data set is quite noisy and the text in the transcriptions overlaps with the categories making clinical text difficult to classify. The clinical narrative, which provides a patient's history and evaluations as well as data for clinical decision-making, is the main form of communication in the medical field. The aim of the study is to make disease diagnoses based on medical records using ML algorithms. The proposed clinical text classification model using weak monitoring to reduce the human efforts to create labeled training data and conduct feature engineering. The primary objective is to contrast this approach with a logistic regression model to classify medical records clinical text and expect superior performance compared to the logistic regression model for an imbalanced medical transcriptions dataset. A promising intelligent data-driven health system to archive and classify healthcare records relies on the ability to extract and contextualize unstructured medical data in a form of a single easy-to-use API by leveraging Machine Learning (ML) services from Amazon cloud in a clinical workflow. AWS services such as S3, Textract, Comprehend Medical, and DynamoDB can be integrated to create a comprehensive solution for handling medical document processing, extracting medical information, performing medical text analysis, and storing the data in a structured manner.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381152463Subjects--Topical Terms:

561178
Information science.
Subjects--Index Terms:

Clinical text classificationIndex Terms--Genre/Form:

554714
Electronic books.

Healthcare Text Analytics Using Recent MI Techniques and Data Classification Using Aws Cloud MI Services.
LDR:03040ntm a22003977 4500 001 1147288
005 20240909100747.5
006 m o d
007 cr bn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798381152463
035 $a (MiAaPQ)AAI30636418
035 $a AAI30636418
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Movinuddin. $3 1472979
245 1 0 $a Healthcare Text Analytics Using Recent MI Techniques and Data Classification Using Aws Cloud MI Services.
264 0 $c 2023
300 $a 1 online resource (63 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 85-06.
500 $a Advisor: Poudel, Khem.
502 $a Thesis (M.S.)--Middle Tennessee State University, 2023.
504 $a Includes bibliographical references
520 $a Classification of clinical texts has a significant impact on disease diagnosis, medical research and automated development of disease ontologies. Because they contain terms that describe medical concepts and terminology, the data set is quite noisy and the text in the transcriptions overlaps with the categories making clinical text difficult to classify. The clinical narrative, which provides a patient's history and evaluations as well as data for clinical decision-making, is the main form of communication in the medical field. The aim of the study is to make disease diagnoses based on medical records using ML algorithms. The proposed clinical text classification model using weak monitoring to reduce the human efforts to create labeled training data and conduct feature engineering. The primary objective is to contrast this approach with a logistic regression model to classify medical records clinical text and expect superior performance compared to the logistic regression model for an imbalanced medical transcriptions dataset. A promising intelligent data-driven health system to archive and classify healthcare records relies on the ability to extract and contextualize unstructured medical data in a form of a single easy-to-use API by leveraging Machine Learning (ML) services from Amazon cloud in a clinical workflow. AWS services such as S3, Textract, Comprehend Medical, and DynamoDB can be integrated to create a comprehensive solution for handling medical document processing, extracting medical information, performing medical text analysis, and storing the data in a structured manner.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Information science. $3 561178
650 4 $a Computer science. $3 573171
653 $a Clinical text classification
653 $a Healthcare text analysis
653 $a Medical record archival
653 $a Medical transcriptions
653 $a Natural language processing
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0723
690 $a 0800
710 2 $a Middle Tennessee State University. $b Computer Science. $3 1472980
710 2 $a ProQuest Information and Learning Co. $3 1178819
773 0 $t Masters Abstracts International $g 85-06.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30636418 $z click for full text (PQDT)