Prevention and treatment of complications are the backbone of medical care, particularly in
critical care settings. Early detection and prompt intervention can potentially prevent …

Mortality prevention in T2D elderly population having Chronic Kidney Disease (CKD) may
be possible thorough risk assessment and predictive modeling. In this study we investigate …

Abstract Outcomes' prediction in Electronic Health Records (EHR) and specifically in Critical
Care is increasingly attracting more exploration and research. In this study, we used clinical …

Many time series classification algorithms have been proposed, including deep neural
networks based, which so far focused mainly on improving model architectures rather than …

In many daily applications, such as meteorology or patient data, the starting and ending
times of the events are stored in a database, resulting in time interval data. Discovering …

The effect of socio-economic factors, ethnicity, and other factors, on the morbidity and
mortality of COVID-19 at the sub-population-level, rather than at the individual level, and …

Objective: Develop a new method for continuous prediction that utilizes a single temporal
pattern ending with an event of interest and its multiple instances detected in the temporal …

Temporal knowledge discovery in clinical problems, is crucial to investigate problems in the
data science era. Meaningful progress has been made computationally in the discovery of …

Purpose To understand what clinical presenting features of sepsis patients are historically
associated with rapid treatment involving antibiotics and fluids, as appropriate. Design This …

In recent years, the use of frequent temporal patterns as features for classification has
increasingly been used and investigated. In this process, commonly frequent patterns are …