Time series predictions in healthcare applications
In recent years, deep learning has been applied in the medical field to perform at sorts of tasks such as the prediction of in-hospital mortality. Due to the inconsistency of follow-up appointments and recordings of vital signs, irregularity is inherently present in medical data. This irregularity...
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Format: | Final Year Project |
Language: | English |
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Nanyang Technological University
2024
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Online Access: | https://hdl.handle.net/10356/175261 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | In recent years, deep learning has been applied in the medical field to perform at
sorts of tasks such as the prediction of in-hospital mortality. Due to the inconsistency of follow-up appointments and recordings of vital signs, irregularity is inherently
present in medical data. This irregularity inherent in the data poses substantial hurdles
to traditional machine-learning techniques as these techniques can only be employed
on time-series data with regular intervals. Over the years, researchers have devoted
considerable efforts to tackling this challenge which has led to the development of
methods falling into two main categories: interpolation-based and non-interpolation-
based models. In this study, we propose a novel approach, STraTS-mTAND, which
integrates techniques from STraTS, a non-interpolation model, and mTAND, an inter-
polation model, to address the binary in-hospital mortality classification problem using
irregularly sampled time-series data. Our approach leverages the strengths of each
technique while mitigating their respective limitations. We evaluate the effectiveness
of STraTS-mTAND using the PhysioNet Challenge 2012 dataset and the MIMIC-III
dataset. Our experimental results demonstrate that our approach outperforms existing
methods in both PR-AUC and ROC-AUC. Additionally, our approach has also shown to
perform better with lesser training data and with sparser and more irregular time-series.
Furthermore, we analysed the PhysioNet Challenge 2012 dataset to provide valuable
insights, which may be used to improve medical resource allocation for patients in the
ICUs. |
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