Building a semantically annotated corpus of clinical texts

In this paper, we describe the construction of a semantically annotated corpus of clinical texts for use in the development and evaluation of systems for automatically extracting clinically significant information from the textual component of patient records. The paper details the sampling of textual material from a collection of 20,000 cancer patient records, the development of a semantic annotation scheme, the annotation methodology, the distribution of annotations in the final corpus, and the use of the corpus for development of an adaptive information extraction system. The resulting corpus is the most richly semantically annotated resource for clinical text processing built to date, whose value has been demonstrated through its use in developing an effective information extraction system. The detailed presentation of our corpus construction and annotation methodology will be of value to others seeking to build high-quality semantically annotated corpora in biomedical domains

Varying presentations of multisystem inflammatory syndrome temporarily associated with COVID-19

Background. A novel coronavirus identified in 2019 leads to a pandemic of severe acute respiratory distress syndrome with important morbidity and mortality. Initially, children seemed minimally affected, but there were reports of cases similar to (atypical) Kawasaki disease or toxic shock syndrome, and evidence emerges about a complication named paediatric inflammatory multisystem syndrome temporarily associated with SARS-CoV-2 (PIMS-TS) or multisystem inflammatory syndrome in children (MIS-C). Case Presentations. Two cases were compared and discussed demonstrating varying presentations, management, and evolution of MIS-C. These cases are presented to increase awareness and familiarity among paediatricians and emergency physicians with the different clinical manifestations of this syndrome. Discussion. MIS-C may occur with possible diverse clinical presentations. Early recognition and treatment are paramount for a beneficial outcome

Time Waits for No One! Analysis and Challenges of Temporal Misalignment

When an NLP model is trained on text data from one time period and tested or deployed on data from another, the resulting temporal misalignment can degrade end-task performance. In this work, we establish a suite of eight diverse tasks across different domains (social media, science papers, news, and reviews) and periods of time (spanning five years or more) to quantify the effects of temporal misalignment. Our study is focused on the ubiquitous setting where a pretrained model is optionally adapted through continued domain-specific pretraining, followed by task-specific finetuning. We establish a suite of tasks across multiple domains to study temporal misalignment in modern NLP systems. We find stronger effects of temporal misalignment on task performance than have been previously reported. We also find that, while temporal adaptation through continued pretraining can help, these gains are small compared to task-specific finetuning on data from the target time period. Our findings motivate continued research to improve temporal robustness of NLP models.Comment: 9 pages, 6 figures, 3 table

Universal History and the Emergence of Species Being

This paper seeks to recover the function of universal history, which was to place particulars into relation with universals. By the 20th century universal history was largely discredited because of an idealism that served to lend epistemic coherence to the overwhelming complexity arising from universal history's comprehensive scope. Idealism also attempted to account for history's being "open"--for the human ability to transcend circumstance. The paper attempts to recover these virtues without the idealism by defining universal history not by its scope but rather as a scientific method that provides an understanding of any kind of historical process, be it physical, biological or human. While this method is not new, it is in need of a development that offers a more robust historiography and warrant as a liberating historical consciousness. The first section constructs an ontology of process by defining matter as ontic probabilities rather than as closed entities. This is lent warrant in the next section through an appeal to contemporary physical science. The resulting conceptual frame and method is applied to the physical domain of existents, to the biological domain of social being and finally to the human domain of species being. It is then used to account for the emergence of human history's initial stage--the Archaic Socio-Economic Formation and for history' stadial trajectory--its alternation of evolution and revolution

Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting

Modern urbanization is demanding smarter technologies to improve a variety of applications in intelligent transportation systems to relieve the increasing amount of vehicular traffic congestion and incidents. Existing incident detection techniques are limited to the use of sensors in the transportation network and hang on human-inputs. Despite of its data abundance, social media is not well-exploited in such context. In this paper, we develop an automated traffic alert system based on Natural Language Processing (NLP) that filters this flood of information and extract important traffic-related bullets. To this end, we employ the fine-tuning Bidirectional Encoder Representations from Transformers (BERT) language embedding model to filter the related traffic information from social media. Then, we apply a question-answering model to extract necessary information characterizing the report event such as its exact location, occurrence time, and nature of the events. We demonstrate the adopted NLP approaches outperform other existing approach and, after effectively training them, we focus on real-world situation and show how the developed approach can, in real-time, extract traffic-related information and automatically convert them into alerts for navigation assistance applications such as navigation apps.Comment: This paper is accepted for publication in IEEE Technology Engineering Management Society International Conference (TEMSCON'20), Metro Detroit, Michigan (USA