Background Temporal relations between clinical events play an important role in clinical
assessment and decision making. Extracting such relations from free text data is a …

Clinical TempEval 2016 evaluated temporal information extraction systems on the clinical
domain. Nine sub-tasks were included, covering problems in time expression identification …

Unstructured data in electronic health records, represented by clinical texts, are a vast
source of healthcare information because they describe a patient's journey, including clinical …

Documents contain information that can be used for various applications, such as question
answering (QA) system, information retrieval (IR) system, and recommendation system. To …

Understanding temporal relationships in text from electronic health records can be valuable
for many important downstream clinical applications. Since Clinical TempEval 2017, there …

Important information for public health is contained within Electronic Health Records (EHRs).
The vast majority of clinical data available in these records takes the form of narratives …

The widespread adoption of Electronic Health Records (EHRs) is generating an ever-
increasing amount of unstructured clinical texts. Processing time expressions from these …

Automatically extracting rich information contained in Electronic Health Records (EHRs) is
crucial to improve clinical research. However, most of this information is in the form of …

The rapid growth in the extraction of clinical events from unstructured clinical records has
raised considerable challenges. In this paper, we propose the use of different features with a …

The process in which the physician must know the patient's medical history is a time-
consuming process. The physician must know the diseases, operations, treatments and, in …