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MULTIVARIATE TIME SERIES FORECASTING TANDA VITAL PASIEN UNIT PERAWATAN INTENSIF MENGGUNAKAN DEEP LEARNING

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Penilaian

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Time series forecasting (TSF) is the task of predicting future values of a particular time sequence. It used in various fields including forecast vital signs data. Vital signs data that are include five parameters; heart rate, blood pressure, oxygen saturation, respiratory rate, and body temperature. Abnormal vital signs help medical practitioners about potential health problems. This research develops a model for forecasting vital signs data in the future. The proposed forecast model is developed by using long-short term memory. The data used to build the model is a vital sign dataset taken from the MIMIC-III database. Missing values are filled in using autoencoder techniques. The proposed model is compared with the Bidirectional Long-Short Term Memory model. The input data is developed by creating a window with one value from a predetermined forecast range. The model was successfully developed with an RMSE value of 0.025615 for 60 minutes of data and 30 minutes of prediction range.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307000808	T88458	T884582023	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T884582023

Publisher

Inderalaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

viii, 64 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

006.307

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Sistem Pakar
Jurusan Sistem Komputer

Specific Detail Info

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Statement of Responsibility

SEPTA

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