Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning
Background The coronavirus disease 2019 (COVID-19) pandemic has caused health
concerns worldwide since December 2019. From the beginning of infection, patients will …
concerns worldwide since December 2019. From the beginning of infection, patients will …
Universal time-series representation learning: A survey
Time-series data exists in every corner of real-world systems and services, ranging from
satellites in the sky to wearable devices on human bodies. Learning representations by …
satellites in the sky to wearable devices on human bodies. Learning representations by …
MultiWave: Multiresolution Deep Architectures through Wavelet Decomposition for Multivariate Time Series Prediction
The analysis of multivariate time series data is challenging due to the various frequencies of
signal changes that can occur over both short and long terms. Furthermore, standard deep …
signal changes that can occur over both short and long terms. Furthermore, standard deep …
A Machine Learning Pipeline for Mortality Prediction in the ICU
Y Sun, YH Zhou - International Journal of Digital Health, 2022 - journals.lww.com
Mortality risk prediction for patients admitted into the intensive care unit (ICU) is a crucial and
challenging task, so that clinicians are able to respond with timely and appropriate clinical …
challenging task, so that clinicians are able to respond with timely and appropriate clinical …
Optimized deep learning-based multimodal method for irregular medical timestamped data
S Rabhi - 2022 - theses.hal.science
The wide adoption of Electronic Health Records in hospitals' information systems has led to
the definition of large databases grouping various types of data such as textual notes …
the definition of large databases grouping various types of data such as textual notes …
[PDF][PDF] Modelling Heterogeneous Time Series from Irregular Data Streams
FMA Shaqra - 2024 - rmit.figshare.com
In this era of rapid advances in technology, machine learning and artificial intelligence (AI)
have emerged as transformative forces, revolutionising the way we approach complex real …
have emerged as transformative forces, revolutionising the way we approach complex real …
Knowledge-Empowered Dynamic Graph Network for Irregularly Sampled Medical Time Series
Irregularly Sampled Medical Time Series (ISMTS) are commonly found in the healthcare
domain, where different variables exhibit unique temporal patterns while interrelated …
domain, where different variables exhibit unique temporal patterns while interrelated …
Generation of patient trajectories: Improving Time-series Generative Adversarial Networks for generating Electronic Health Records
J Hjerpe - 2024 - diva-portal.org
ABSTRACT The integration of Electronic Health Records has transformed patient data
management in healthcare, offering comprehensive insights into patient journeys through …
management in healthcare, offering comprehensive insights into patient journeys through …