Cognitive computing systems require human labeled data for evaluation and often for
training. The standard practice used in gathering this data minimizes disagreement between …

In order to effectively integrate medical data and alleviate the problem of uneven distribution
of medical resources. In this paper, we combine the techniques of expert systems, graph …

Managing error has become an increasingly central and contested arena within data
science work. While recent scholarship in artificial intelligence and machine learning has …

Suicide is the 10 th leading cause of death in the US (1999-2019). However, predicting
when someone will attempt suicide has been nearly impossible. In the modern world, many …

During the outbreak of the COVID-19 (2019 coronavirus disease), misinformation related to
the virus spread rapidly online and have led to serious difficulties in controlling the disease …

Abstract Knowledge about the interactions between dietary and biomedical factors is
scattered throughout uncountable research articles in an unstructured form (eg, text, images …

The process of gathering ground truth data through human annotation is a major bottleneck
in the use of information extraction methods for populating the Semantic Web …

The accelerating growth of big data in the biomedical domain, with an endless amount of
electronic health records and more than 30 million citations and abstracts in PubMed …

The lack of annotated datasets for training and benchmarking is one of the main challenges
of Clinical Natural Language Processing. In addition, current methods for collecting …

Semantic annotation tasks contain ambiguity and vagueness and require varying degrees of
world knowledge. Disagreement is an important indication of these phenomena. Most …