Data mining technology can search for potentially valuable knowledge from a large amount
of data, mainly divided into data preparation and data mining, and expression and analysis …

Gaining knowledge and actionable insights from complex, high-dimensional and
heterogeneous biomedical data remains a key challenge in transforming health care …

The rapid increase of interest in, and use of, artificial intelligence (AI) in computer
applications has raised a parallel concern about its ability (or lack thereof) to provide …

Secondary use of electronic health records (EHRs) promises to advance clinical research
and better inform clinical decision making. Challenges in summarizing and representing …

Survival analysis is a subfield of statistics where the goal is to analyze and model data
where the outcome is the time until an event of interest occurs. One of the main challenges …

Many real-world systems can be studied in terms of pattern recognition tasks, so that proper
use (and understanding) of machine learning methods in practical applications becomes …

Due to its continuously increasing occurrence, more and more families are influenced by
diabetes mellitus. Most diabetics know little about their health quality or the risk factors they …

The concept of big data, commonly characterized by volume, variety, velocity, and veracity,
goes far beyond the data type and includes the aspects of data analysis, such as hypothesis …

A new data-driven computational framework is developed to assist in the design and
modeling of new material systems and structures. The proposed framework integrates three …

This paper presents a taxonomy of explainability in human–agent systems. We consider
fundamental questions about the Why, Who, What, When and How of explainability. First, we …