東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

FindBook

Google Book

Amazon

博客來

Modeling Multivariate Time-Series Variables in Healthcare Using Deep Learning Methods.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modeling Multivariate Time-Series Variables in Healthcare Using Deep Learning Methods./
作者:	Sharafat, Ali Reza.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	47 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
Contained By:	Dissertations Abstracts International83-05B.
標題:	Patients. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28812982
ISBN:	9798494454683

Modeling Multivariate Time-Series Variables in Healthcare Using Deep Learning Methods.
Sharafat, Ali Reza.

Modeling Multivariate Time-Series Variables in Healthcare Using Deep Learning Methods. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 47 p.

Source: Dissertations Abstracts International, Volume: 83-05, Section: B.

Thesis (Ph.D.)--Stanford University, 2021.

This item must not be sold to any third party vendors.

Well-being of patients or operation of healthcare systems can be evaluated by monitoring certain health metrics or workflow parameters. This monitoring is done by periodically measuring those metrics and evaluating their variations and changes over time. The resulting measurements create a set of data points along the temporal dimension, which are called time series data. Decisions to improve patient outcome or healthcare operation often depend on accurately forecasting future trends in those time series variables. Thus, time series analysis and prediction is of very high importance in healthcare provisioning. Variables in healthcare are often interrelated, and future values of a given variable depend on past observation and trends of that variable as well as other related variables. Thus, in many cases, time series prediction tasks in healthcare are multivariate.In this dissertation, we develop a multivariate time series predictive model that can simultaneously learn from multiple time series variables over a long temporal window. We implement this model as a convolutional neural network, which we call PatientFlowNet. We use this model to predict patient flow in hospital emergency departments. Specifically, we predict the rates at which patients arrive, are treated, and are discharged from the hospital. We benchmark our model against the state-of-the-art methods in patient flow prediction using data from emergency departments in three different hospitals. We then reuse our model for predicting vital signs for patients under anesthesia, namely heart rate as well as systolic and diastolic blood pressure. We observe that our model has superior prediction accuracy in the case of patient flow in hospital emergency departments where short-term and long-term trends are present but hidden in the data and variables are significantly interrelated. However, where long-term trends are not present, as is the case with patient vital signs under anesthesia, the performance of our model is similar to the best baseline method. The convolutional design of PatientFlowNet allows us to extract dependencies between input and output variables by examining the values of convolutional filters in the first layer of the network. We provide visual and interpretable representations of learned dependencies between time series variables in each study.

ISBN: 9798494454683Subjects--Topical Terms:

1961957
Patients.

Modeling Multivariate Time-Series Variables in Healthcare Using Deep Learning Methods.
LDR:03485nmm a2200349 4500 001 2344920
005 20220531062215.5
008 241004s2021 ||||||||||||||||| ||eng d
020 $a 9798494454683
035 $a (MiAaPQ)AAI28812982
035 $a (MiAaPQ)STANFORDfv290sn7548
035 $a AAI28812982
040 $a MiAaPQ $c MiAaPQ
100 1 $a Sharafat, Ali Reza. $3 3683758
245 1 0 $a Modeling Multivariate Time-Series Variables in Healthcare Using Deep Learning Methods.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 47 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-05, Section: B.
500 $a Advisor: Bayati, Mohsen;Weissman, Tsachy;Widrow, Bernard.
502 $a Thesis (Ph.D.)--Stanford University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a Well-being of patients or operation of healthcare systems can be evaluated by monitoring certain health metrics or workflow parameters. This monitoring is done by periodically measuring those metrics and evaluating their variations and changes over time. The resulting measurements create a set of data points along the temporal dimension, which are called time series data. Decisions to improve patient outcome or healthcare operation often depend on accurately forecasting future trends in those time series variables. Thus, time series analysis and prediction is of very high importance in healthcare provisioning. Variables in healthcare are often interrelated, and future values of a given variable depend on past observation and trends of that variable as well as other related variables. Thus, in many cases, time series prediction tasks in healthcare are multivariate.In this dissertation, we develop a multivariate time series predictive model that can simultaneously learn from multiple time series variables over a long temporal window. We implement this model as a convolutional neural network, which we call PatientFlowNet. We use this model to predict patient flow in hospital emergency departments. Specifically, we predict the rates at which patients arrive, are treated, and are discharged from the hospital. We benchmark our model against the state-of-the-art methods in patient flow prediction using data from emergency departments in three different hospitals. We then reuse our model for predicting vital signs for patients under anesthesia, namely heart rate as well as systolic and diastolic blood pressure. We observe that our model has superior prediction accuracy in the case of patient flow in hospital emergency departments where short-term and long-term trends are present but hidden in the data and variables are significantly interrelated. However, where long-term trends are not present, as is the case with patient vital signs under anesthesia, the performance of our model is similar to the best baseline method. The convolutional design of PatientFlowNet allows us to extract dependencies between input and output variables by examining the values of convolutional filters in the first layer of the network. We provide visual and interpretable representations of learned dependencies between time series variables in each study.
590 $a School code: 0212.
650 4 $a Patients. $3 1961957
650 4 $a Deep learning. $3 3554982
650 4 $a Blood pressure. $3 661055
650 4 $a Vital signs. $3 3683759
650 4 $a Neural networks. $3 677449
650 4 $a Public health. $3 534748
650 4 $a Design. $3 518875
650 4 $a Engineering. $3 586835
650 4 $a Time series. $3 3561811
650 4 $a Departments. $3 3683760
650 4 $a Artificial intelligence. $3 516317
650 4 $a Morphology. $3 591167
690 $a 0573
690 $a 0389
690 $a 0537
690 $a 0800
690 $a 0287
710 2 $a Stanford University. $3 754827
773 0 $t Dissertations Abstracts International $g 83-05B.
790 $a 0212
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28812982