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Mining Electronic Health Records : = Spatio-Temporal Analysis of Hospital Patient Trajectories.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Mining Electronic Health Records :/
其他題名:	Spatio-Temporal Analysis of Hospital Patient Trajectories.
作者:	Madhobi, Kaniz Fatema.
面頁冊數:	1 online resource (103 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-03, Section: A.
Contained By:	Dissertations Abstracts International85-03A.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9798380377874

Mining Electronic Health Records : = Spatio-Temporal Analysis of Hospital Patient Trajectories.
Madhobi, Kaniz Fatema.

Mining Electronic Health Records :Spatio-Temporal Analysis of Hospital Patient Trajectories. - 1 online resource (103 pages)

Source: Dissertations Abstracts International, Volume: 85-03, Section: A.

Thesis (Ph.D.)--Washington State University, 2023.

Includes bibliographical references

The widespread use of Electronic Health Records (EHRs) in the healthcare systems has resulted in a massive amount of patient specific information in digitally archived form. It has opened a door of opportunities for research communities to facilitate clinical studies and improve healthcare quality. Mining meaningful information from this data is challenging due to its inherent complex, noisy nature. In this dissertation, we present our research efforts to overcome some of the challenges associated with mining insight from health records.In the first part of the dissertation, we present a visual analytics framework to track patient trajectories. We extract the trajectories from medical records and visualize how a variable for example, antibiotic usage, evolves over time along the patients stay in the hospital. In the second part, we analyze patients contact pattern i.e. which patients came in close proximity during their hospital visit. This is particularly important in infectious disease modeling. Finally, in the third part we develop a predictive model to learn from patients trajectories and predict two outcomes simultaneously namely predict patients next unit label and predict how many days the patient will stay in the hospital. Utilizing this approach can aid in clinical decision support and lead to overall improved healthcare planning.

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

Mode of access: World Wide Web

ISBN: 9798380377874Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Electronic Health RecordsIndex Terms--Genre/Form:

554714
Electronic books.

Mining Electronic Health Records : = Spatio-Temporal Analysis of Hospital Patient Trajectories.
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520 $a The widespread use of Electronic Health Records (EHRs) in the healthcare systems has resulted in a massive amount of patient specific information in digitally archived form. It has opened a door of opportunities for research communities to facilitate clinical studies and improve healthcare quality. Mining meaningful information from this data is challenging due to its inherent complex, noisy nature. In this dissertation, we present our research efforts to overcome some of the challenges associated with mining insight from health records.In the first part of the dissertation, we present a visual analytics framework to track patient trajectories. We extract the trajectories from medical records and visualize how a variable for example, antibiotic usage, evolves over time along the patients stay in the hospital. In the second part, we analyze patients contact pattern i.e. which patients came in close proximity during their hospital visit. This is particularly important in infectious disease modeling. Finally, in the third part we develop a predictive model to learn from patients trajectories and predict two outcomes simultaneously namely predict patients next unit label and predict how many days the patient will stay in the hospital. Utilizing this approach can aid in clinical decision support and lead to overall improved healthcare planning.
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