With the biomedical field generating large quantities of time series data, there has been a
growing interest in developing and refining machine learning methods that allow its mining …

Leveraging temporal observations to predict a patient's health state at a future period is a
very challenging task. Providing such a prediction early and accurately allows for designing …

Early classification of time series is prevalent in many time-sensitive applications such as,
but not limited to, early warning of disease outcome and early warning of crisis in stock …

Motivation Most modern intensive care units record the physiological and vital signs of
patients. These data can be used to extract signatures, commonly known as biomarkers, that …

Early classification of time series is valuable in many real‐world applications such as early
disease prediction, early disaster prediction, and patient monitoring where data are …

The early classification of time series data is a critical problem in many time-sensitive
applications such as health informatics. Where the prediction of class value, as early as …

Sepsis is a systematic response to an infectious disease, being a concerning factor because
of the increase in the mortality ratio for every delayed hour in the identification and start of …

Sepsis, an acute systemic inflammatory response syndrome caused by severe infection, is
one of the leading causes of in-hospital mortality. Our recent work provides evidence that …

High false alarm rates in intensive care units (ICUs) cause desensitization among care
providers, thus risking patients' lives. Providing early detection of true and false cardiac …

Sepsis is a serious, life-threatening condition that presents a growing problem in medicine,
but there is still no satisfying solution for treating it. Several blood cleansing approaches …