Analysis of irregularly sampled time series health care data sets

The real-world healthcare system generates abundant time-series data. In most cases, these data have a high prevalence of missing values and are often irregularly sampled across both time and patient. Moreover, due to the complex level of a different dataset, the preprocessing is more significant an...

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Main Author: Wang, Anni
Other Authors: Ponnuthurai Nagaratnam Suganthan
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2022
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Online Access:https://hdl.handle.net/10356/154677
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1546772023-07-04T16:40:31Z Analysis of irregularly sampled time series health care data sets Wang, Anni Ponnuthurai Nagaratnam Suganthan School of Electrical and Electronic Engineering A*STAR Institute for Infocomm Research (I2R) Ramasamy Savitha EPNSugan@ntu.edu.sg Engineering::Electrical and electronic engineering The real-world healthcare system generates abundant time-series data. In most cases, these data have a high prevalence of missing values and are often irregularly sampled across both time and patient. Moreover, due to the complex level of a different dataset, the preprocessing is more significant and challenging. This dissertation focuses on imputation and prediction tasks to address the challenges of irregularly sampled time series data sets. First, we trained the Recurrent Imputation for Time Series (RITS) model and Bayesian Long Short Term Memory (BLSTM) model on a publicly available PhysioNet dataset for prediction task only. Next, we, train a Bayesian LSTM for imputation of missing values (Considering the irregular sampling as missing values too) and prediction of outcomes, on a proprietary heart failure risk prediction data set. The proposed model represents the distribution of irregularly sampled time series data, imputes both categorical and continuous missing data in the time series, and makes prediction of the outcome of interest. Furthermore, the Bayesian model allows for a reliable estimate of the outcome of interest. While the missing continuous variables are imputed through the MAE error minimization, the categorical variables are imputed using softmax and/or argmax operations. The outcome prediction task in the presence of imbalanced data set is addressed through the weighted loss function. Performance results indicate that the proposed approach is effective in imputing both categorical and continuous variables, with the superior prediction of outcome of interest. Master of Science (Computer Control and Automation) 2022-01-03T08:26:11Z 2022-01-03T08:26:11Z 2021 Thesis-Master by Coursework Wang, A. (2021). Analysis of irregularly sampled time series health care data sets. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/154677 https://hdl.handle.net/10356/154677 en D-255-20211-02961  application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Wang, Anni
Analysis of irregularly sampled time series health care data sets
description The real-world healthcare system generates abundant time-series data. In most cases, these data have a high prevalence of missing values and are often irregularly sampled across both time and patient. Moreover, due to the complex level of a different dataset, the preprocessing is more significant and challenging. This dissertation focuses on imputation and prediction tasks to address the challenges of irregularly sampled time series data sets. First, we trained the Recurrent Imputation for Time Series (RITS) model and Bayesian Long Short Term Memory (BLSTM) model on a publicly available PhysioNet dataset for prediction task only. Next, we, train a Bayesian LSTM for imputation of missing values (Considering the irregular sampling as missing values too) and prediction of outcomes, on a proprietary heart failure risk prediction data set. The proposed model represents the distribution of irregularly sampled time series data, imputes both categorical and continuous missing data in the time series, and makes prediction of the outcome of interest. Furthermore, the Bayesian model allows for a reliable estimate of the outcome of interest. While the missing continuous variables are imputed through the MAE error minimization, the categorical variables are imputed using softmax and/or argmax operations. The outcome prediction task in the presence of imbalanced data set is addressed through the weighted loss function. Performance results indicate that the proposed approach is effective in imputing both categorical and continuous variables, with the superior prediction of outcome of interest.
author2 Ponnuthurai Nagaratnam Suganthan
author_facet Ponnuthurai Nagaratnam Suganthan
Wang, Anni
format Thesis-Master by Coursework
author Wang, Anni
author_sort Wang, Anni
title Analysis of irregularly sampled time series health care data sets
title_short Analysis of irregularly sampled time series health care data sets
title_full Analysis of irregularly sampled time series health care data sets
title_fullStr Analysis of irregularly sampled time series health care data sets
title_full_unstemmed Analysis of irregularly sampled time series health care data sets
title_sort analysis of irregularly sampled time series health care data sets
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/154677
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