Process Mining focuses on extracting knowledge from data generated and stored in
corporate information systems in order to analyze executed processes. In the healthcare …

Many high quality studies have emerged from public databases, such as Surveillance,
Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey …

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 …

The growing healthcare industry is generating a large volume of useful data on patient
demographics, treatment plans, payment, and insurance coverage—attracting the attention …

The magnitude of the daily explosion of high volumes of data has led to the emergence of
the Big Data paradigm. The ever-increasing amount of information available on the Internet …

Proper handling of missing data is important for many secondary uses of electronic health
record (EHR) data. Data imputation methods can be used to handle missing data, but their …

Data collected from hematopoietic SCT (HSCT) centers are becoming more abundant and
complex owing to the formation of organized registries and incorporation of biological data …

Rationale, aims, and objectives Time to the occurrence of an event is often studied in health
research. Survival analysis differs from other designs in that follow‐up times for individuals …

Data mining continues to play an important role in medicine; specifically, for the
development of diagnosis aid models used in expert and intelligent systems. Although we …

Data mining techniques are gaining in popularity among health researchers for an array of
purposes, such as improving diagnostic accuracy, identifying high‐risk patients and …