Background Interest in the application of machine learning (ML) to the design, conduct, and
analysis of clinical trials has grown, but the evidence base for such applications has not …

Abstract Information systems such as Electronic Health Record (EHR) systems are
susceptible to data quality (DQ) issues. Given the growing importance of EHR data, there is …

Unsupervised anomaly detection for spatio-temporal data has extensive use in a wide
variety of applications such as earth science, traffic monitoring, fraud and disease outbreak …

The increasing demand for advanced fault detection in manufacturing processes has
encouraged the application of industrial intelligence based on deep learning. However …

Customer reviews are an essential source of information to consumers. Meanwhile, opinion
spams spread widely and the detection of spam reviews becomes critically important for …

Background: Observational biomedical studies facilitate a new strategy for large-scale
electronic health record (EHR) utilization to support precision medicine. However, data label …

Introduction Secondary use of electronic health record (EHR) data for research requires that
the data are fit for use. Data quality (DQ) frameworks have traditionally focused on structural …

Objective To summarize the research literature evaluating automated methods for early
detection of safety problems with health information technology (HIT). Materials and …

Humans use pictures to model the world. The structure of a picture maps to mind space to
form a concept. When an internal structure matches the corresponding external structure, an …

Background The importance of real-world evidence is widely recognized in observational
oncology studies. However, the lack of interoperable data quality standards in the …