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EMR-Based Computational Phenotyping in Multiple Sclerosis Incorporating Natural Language Processing and Machine Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	EMR-Based Computational Phenotyping in Multiple Sclerosis Incorporating Natural Language Processing and Machine Learning./
作者:	Chang, Jack Z.
面頁冊數:	1 online resource (136 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-08, Section: B.
Contained By:	Dissertations Abstracts International84-08B.
標題:	Information science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30241194click for full text (PQDT)
ISBN:	9798374404524

EMR-Based Computational Phenotyping in Multiple Sclerosis Incorporating Natural Language Processing and Machine Learning.
Chang, Jack Z.

EMR-Based Computational Phenotyping in Multiple Sclerosis Incorporating Natural Language Processing and Machine Learning. - 1 online resource (136 pages)

Source: Dissertations Abstracts International, Volume: 84-08, Section: B.

Thesis (Ph.D.)--University of Rochester, 2022.

Includes bibliographical references

Multiple sclerosis (MS) phenotypes (clinically isolated syndrome, relapsing-remitting, secondary progressive, primary progressive) are useful disease descriptions for standardizing communication about patients and selecting appropriate therapies and clinical trial candidates. Disease activity and progression of disability can be meaningful modifiers to MS phenotypes which can further impact prognosis, therapeutic decisions, and clinical trial designs and outcomes. However, studies have shown that neither MS phenotypes nor their modifiers are consistently documented in electronic medical record (EMR) chart notes. The evidence for disease activity and progression often resides in the progress notes, requiring manual chart review from clinical experts while increasing the difficulty of conducting clinical research. In this thesis, a novel, fully executable, and scalable computational phenotyping process was developed, incorporating natural language processing technologies and shallow machine-learning models to detect evidence of MS disease activity and progression in EMR progress notes while predicting MS phenotype modifiers to be easily interpreted by clinicians. The results demonstrated that this integrated method extracts clinically-relevant information from progress notes persistently associated with disease activity and progression, predicting MS phenotype modifiers with satisfactory performance while encouraging portability and interpretability. Thus, this method holds promise for facilitating the screening of MS clinical trial participants and potentially identifying early evidence of disease progression.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798374404524Subjects--Topical Terms:

554358
Information science.
Subjects--Index Terms:

MS phenotype modifierIndex Terms--Genre/Form:

542853
Electronic books.

EMR-Based Computational Phenotyping in Multiple Sclerosis Incorporating Natural Language Processing and Machine Learning.
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