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 …

Breast, lung, prostate, and stomach cancers are the most frequent cancer types globally.
Early-stage detection and diagnosis of these cancers pose a challenge in the literature …

Abstract Electronic Health Record (EHR) data are captured over time as patients receive
care. Accordingly, variations among patients, such as when a patient presents for care …

In medical domain, huge amounts of data are generated at all times. Data exploration is very
important to help physicians or medical researchers to find the required datasets. However …

Modern applications of survival analysis increasingly involve time-dependent covariates. In
healthcare settings, such covariates provide dynamic patient histories that can be used to …

This research investigates the impact of missing data on the performance of machine
learning algorithms, with a particular focus on the MIMIC-IV dataset. This project aims to …

The study of existing links among different types of medical concepts can support research
on optimal pathways for the treatment of human diseases. Here, we present a clustering …

The imputation of the Multivariate time series (MTS) is particularly challenging since the MTS
typically contains irregular patterns of missing values due to various factors such as …

EHR contains detailed information about a large number of patients. In the past ten years,
EHR-related research has involved various fields, and research in various fields is …

Electronic Health Record (EHR) data provide a tremendous opportunity for enhancing the
quality of care by delivering personalized treatments [1]. EHR data are rich and tracked at …