A Systematic Temporal Extraction Pipeline for Medical Concepts in Clinical Notes.

With increased application of natural language processing (NLP) in medicine, many NLP models are being developed for uncovering relevant clinical features from electronic health records. Temporal information plays a key role in understanding the context, significance, and interpretation of medical concepts extracted from clinical notes. This is particularly true in situations where the behavior, value, or status of a medical concept changes over time.

An Assessment of Mentions of Adverse Drug Events on Social Media With Natural Language Processing: Model Development and Analysis.

Background: Adverse reactions to drugs attract significant concern in both clinical practice and public health monitoring. Multiple measures have been put into place to increase postmarketing surveillance of the adverse effects of drugs and to improve drug safety. These measures include implementing spontaneous reporting systems and developing automated natural language processing systems based on data from electronic health records and social media to collect evidence of adverse drug events that can be further investigated as possible adverse reactions.

Identifying the Presence, Activity, and Status of Extraintestinal Manifestations of Inflammatory Bowel Disease Using Natural Language Processing of Clinical Notes.

Background: Extraintestinal manifestations (EIMs) occur commonly in inflammatory bowel disease (IBD), but population-level understanding of EIM behavior is difficult. We present a natural language processing (NLP) system designed to identify both the presence and status of EIMs using clinical notes from patients with IBD.

Methods: In a single-center retrospective study, clinical outpatient electronic documents were collected in patients with IBD.

Uncovering the relationship between food-related discussion on Twitter and neighborhood characteristics.

Objective: Initiatives to reduce neighborhood-based health disparities require access to meaningful, timely, and local information regarding health behavior and its determinants. We examined the validity of Twitter as a source of information for neighborhood-level analysis of dietary choices and attitudes.

Materials And Methods: We analyzed the "healthiness" quotient and sentiment in food-related tweets at the census tract level, and associated them with neighborhood characteristics and health outcomes.