Impact of guidelines for the management of minor head injury on the utilization and diagnostic yield of CT over two decades, using natural language processing in a large dataset.

Objectives: We investigated the impact of clinical guidelines for the management of minor head injury on utilization and diagnostic yield of head CT over two decades.

Methods: Retrospective before-after study using multiple electronic health record data sources. Natural language processing algorithms were developed to rapidly extract indication, Glasgow Coma Scale, and CT outcome from clinical records, creating two datasets: one based on all head injury CTs from 1997 to 2009 (n = 9109), for which diagnostic yield of intracranial traumatic findings was calculated.

Adrenal Incidentaloma and Adherence to International Guidelines for Workup Based on a Retrospective Review of the Type of Language Used in the Radiology Report.

Introduction: The purpose of this study is to explore what terms are used to describe adrenal incidentalomas and to determine what reporting factors are associated with clinicians adhering to international guidelines.

Methods: This retrospective study was approved by the institutional review board, with a waiver of informed consent. Adrenal incidentaloma cases were identified from CT reports between 2010 and 2012 and filtered based on terminology used to describe the adrenal mass at initial presentation.

Patient-specific workup of adrenal incidentalomas.

Purpose: : To develop a clinical prediction model to predict a clinically relevant adrenal disorder for patients with adrenal incidentaloma.

Materials And Methods: : This retrospective study is approved by the institutional review board, with waiver of informed consent. Natural language processing is used for filtering of adrenal incidentaloma cases in all thoracic and abdominal CT reports from 2010 till 2012.

Chemical entity recognition in patents by combining dictionary-based and statistical approaches.

We describe the development of a chemical entity recognition system and its application in the CHEMDNER-patent track of BioCreative 2015. This community challenge includes a Chemical Entity Mention in Patents (CEMP) recognition task and a Chemical Passage Detection (CPD) classification task. We addressed both tasks by an ensemble system that combines a dictionary-based approach with a statistical one.

Natural Language Processing in Radiology: A Systematic Review.

Radiological reporting has generated large quantities of digital content within the electronic health record, which is potentially a valuable source of information for improving clinical care and supporting research. Although radiology reports are stored for communication and documentation of diagnostic imaging, harnessing their potential requires efficient and automated information extraction: they exist mainly as free-text clinical narrative, from which it is a major challenge to obtain structured data. Natural language processing (NLP) provides techniques that aid the conversion of text into a structured representation, and thus enables computers to derive meaning from human (ie, natural language) input.

ContextD: an algorithm to identify contextual properties of medical terms in a Dutch clinical corpus.

Background: In order to extract meaningful information from electronic medical records, such as signs and symptoms, diagnoses, and treatments, it is important to take into account the contextual properties of the identified information: negation, temporality, and experiencer. Most work on automatic identification of these contextual properties has been done on English clinical text. This study presents ContextD, an adaptation of the English ConText algorithm to the Dutch language, and a Dutch clinical corpus.