Graph-guided network for irregularly sampled multivariate time series
In many domains, including healthcare, biology, and climate science, time series are
irregularly sampled with varying time intervals between successive readouts and different …
irregularly sampled with varying time intervals between successive readouts and different …
[HTML][HTML] Interpretable clinical time-series modeling with intelligent feature selection for early prediction of antimicrobial multidrug resistance
Electronic health records provide rich, heterogeneous data about the evolution of the
patients' health status. However, such data need to be processed carefully, with the aim of …
patients' health status. However, such data need to be processed carefully, with the aim of …
VS-GRU: A variable sensitive gated recurrent neural network for multivariate time series with massive missing values
Q Li, Y Xu - Applied Sciences, 2019 - mdpi.com
Multivariate time series are often accompanied with missing values, especially in clinical
time series, which usually contain more than 80% of missing data, and the missing rates …
time series, which usually contain more than 80% of missing data, and the missing rates …
Moving Beyond Medical Statistics: A Systematic Review on Missing Data Handling in Electronic Health Records
W Ren, Z Liu, Y Wu, Z Zhang, S Hong, H Liu… - Health Data …, 2024 - spj.science.org
Background: Missing data in electronic health records (EHRs) presents significant
challenges in medical studies. Many methods have been proposed, but uncertainty exists …
challenges in medical studies. Many methods have been proposed, but uncertainty exists …
Modeling regime shifts in multiple time series
We investigate the problem of discovering and modeling regime shifts in an ecosystem
comprising multiple time series known as co-evolving time series. Regime shifts refer to the …
comprising multiple time series known as co-evolving time series. Regime shifts refer to the …
[HTML][HTML] On missingness features in machine learning models for critical care: observational study
Background: Missing data in electronic health records is inevitable and considered to be
nonrandom. Several studies have found that features indicating missing patterns …
nonrandom. Several studies have found that features indicating missing patterns …
Approaching adverse event detection utilizing transformers on clinical time-series
H Fredriksen, PJ Burman, A Woldaregay… - arxiv preprint arxiv …, 2023 - arxiv.org
Patients being admitted to a hospital will most often be associated with a certain clinical
development during their stay. However, there is always a risk of patients being subject to …
development during their stay. However, there is always a risk of patients being subject to …
Categorization of phenotype trajectories utilizing transformers on clinical time-series
H Fredriksen, PJ Burman, AZ Woldaregay… - Proceedings of the …, 2024 - dl.acm.org
Patients being admitted to a hospital will most often be associated with a certain clinical
development during their stay. However, there is always a risk of patients being subject to …
development during their stay. However, there is always a risk of patients being subject to …
As easy as APC: overcoming missing data and class imbalance in time series with self-supervised learning
High levels of missing data and strong class imbalance are ubiquitous challenges that are
often presented simultaneously in real-world time series data. Existing methods approach …
often presented simultaneously in real-world time series data. Existing methods approach …
Deep Learning and Graph Analytics for Explainable Modeling of Clinical Time-Varying Data Associated with Infectious Diseases
S Martínez Agüero - 2024 - burjcdigital.urjc.es
Esta tesis doctoral realiza una investigación de las herramientas de ciencia de datos para
abordar dos problemas incipientes en entornos clínicos modernos: la Multiresistencia …
abordar dos problemas incipientes en entornos clínicos modernos: la Multiresistencia …