[HTML][HTML] Deep imputation of missing values in time series health data: A review with benchmarking
M Kazijevs, MD Samad - Journal of biomedical informatics, 2023 - Elsevier
The imputation of missing values in multivariate time series (MTS) data is a critical step in
ensuring data quality and producing reliable data-driven predictive models. Apart from many …
ensuring data quality and producing reliable data-driven predictive models. Apart from many …
TEST: Text prototype aligned embedding to activate LLM's ability for time series
This work summarizes two ways to accomplish Time-Series (TS) tasks in today's Large
Language Model (LLM) context: LLM-for-TS (model-centric) designs and trains a …
Language Model (LLM) context: LLM-for-TS (model-centric) designs and trains a …
Unsupervised representation learning for time series: A review
Unsupervised representation learning approaches aim to learn discriminative feature
representations from unlabeled data, without the requirement of annotating every sample …
representations from unlabeled data, without the requirement of annotating every sample …
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 …
Hypergraph contrastive learning for electronic health records
Abstract Electronic Health Records (EHR) is the repository of patients' involved medical
codes in the hospital, including diagnosis codes, medication codes, procedure codes, lab …
codes in the hospital, including diagnosis codes, medication codes, procedure codes, lab …
A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources
Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and
point of care applications; however, many challenges such as data privacy concerns impede …
point of care applications; however, many challenges such as data privacy concerns impede …
A survey of generative adversarial networks for synthesizing structured electronic health records
Electronic Health Records (EHRs) are a valuable asset to facilitate clinical research and
point of care applications; however, many challenges such as data privacy concerns impede …
point of care applications; however, many challenges such as data privacy concerns impede …
Time pattern reconstruction for classification of irregularly sampled time series
Abstract Irregularly Sampled Time Series (ISTS) include partially observed feature vectors
caused by the lack of temporal alignment across dimensions and the presence of variable …
caused by the lack of temporal alignment across dimensions and the presence of variable …
A ranking-based cross-entropy loss for early classification of time series
Early classification tasks aim to classify time series before observing full data. It is critical in
time-sensitive applications such as early sepsis diagnosis in the intensive care unit (ICU) …
time-sensitive applications such as early sepsis diagnosis in the intensive care unit (ICU) …
Stop&Hop: Early Classification of Irregular Time Series
Early classification algorithms help users react faster to their machine learning model's
predictions. Early warning systems in hospitals, for example, let clinicians improve their …
predictions. Early warning systems in hospitals, for example, let clinicians improve their …