Prospective evaluation of the impact of repeated whole prison testing for hepatitis C.

Introduction: Chronic hepatitis C virus (HCV) infection is prevalent in prisons. Universal reception HCV testing is recommended, but acceptance can be suboptimal. To detect and treat missed HCV infections, a high-intensity test and treat (HITT) programme was introduced to rapidly test entire prisons.

Augmenting the Hospital Score with social risk factors to improve prediction for 30-day readmission following acute myocardial infarction.

Background: Hospital Score is a well-known and validated tool for predicting readmission risk among diverse patient populations. Integrating social risk factors using natural language processing with the Hospital Score may improve its ability to predict 30-day readmissions following an acute myocardial infarction.

Methods: A retrospective cohort included patients hospitalized at Vanderbilt University Medical Center between January 1, 2007, and December 31, 2016, with a primary index diagnosis of acute myocardial infarction, who were discharged alive.

Identifying clinical phenotypes of frontotemporal dementia in post-9/11 era veterans using natural language processing.

Introduction: Frontotemporal dementia (FTD) encompasses a clinically and pathologically diverse group of neurodegenerative disorders, yet little work has quantified the unique phenotypic clinical presentations of FTD among post-9/11 era veterans. To identify phenotypes of FTD using natural language processing (NLP) aided medical chart reviews of post-9/11 era U.S.

Automatic Extraction of Skin and Soft Tissue Infection Status from Clinical Notes.

The reliable identification of skin and soft tissue infections (SSTIs) from electronic health records is important for a number of applications, including quality improvement, clinical guideline construction, and epidemiological analysis. However, in the United States, types of SSTIs (e.g.

Development and optimisation of a reception testing protocol designed to eliminate HCV in the UK prison population.

Background & Aims: Micro-elimination of hepatitis C virus (HCV) in high-risk populations is a feasible approach towards achieving the World Health Organization's targets for viral hepatitis elimination by 2030. Prisons represent an area of high HCV prevalence and so initiatives that improve testing and treatment of residents are needed to eliminate HCV from prisons. This initiative aimed to improve the HCV screening and treatment rates of new residents arriving at prisons in England.

Chronic hepatitis C elimination prison initiative: HCV-intensive test and treat, a whole prisoner population HCV test-and-treat program in England.

Background And Aim: Prison residents are at high risk for hepatitis C virus (HCV) infection. HCV test-and-treat initiatives within prisons provide an opportunity to engage with prison residents and achieve HCV micro-elimination. The aim of the prison HCV-intensive test and treat initiative was to screen over 95% of all prison residents for HCV infection within a defined number of days determined by the size of the prison population and to initiate treatment within 7-14 days of a positive HCV RNA diagnosis.

Impact of 5 years of hepatitis C testing and treatment in the North East of England prisons.

Hepatitis C virus infection (HCV) is prevalent in prisons. Therefore, effective prison HCV services are critical for HCV elimination programmes. We aimed to evaluate the efficacy of a regional HCV prison testing and treatment programme.

Adaptation of an NLP system to a new healthcare environment to identify social determinants of health.

Social determinants of health (SDoH) are increasingly important factors for population health, healthcare outcomes, and care delivery. However, many of these factors are not reliably captured within structured electronic health record (EHR) data. In this work, we evaluated and adapted a previously published NLP tool to include additional social risk factors for deployment at Vanderbilt University Medical Center in an Acute Myocardial Infarction cohort.

Examining the Interfacility Variation of Social Determinants of Health in the Veterans Health Administration.

Introduction: Recently, numerous studies have linked social determinants of health (SDoH) with clinical outcomes. While this association is well known, the interfacility variability of these risk favors within the Veterans Health Administration (VHA) is not known. Such information could be useful to the VHA for resource and funding allocation.

Moonstone: a novel natural language processing system for inferring social risk from clinical narratives.

Background: Social risk factors are important dimensions of health and are linked to access to care, quality of life, health outcomes and life expectancy. However, in the Electronic Health Record, data related to many social risk factors are primarily recorded in free-text clinical notes, rather than as more readily computable structured data, and hence cannot currently be easily incorporated into automated assessments of health. In this paper, we present Moonstone, a new, highly configurable rule-based clinical natural language processing system designed to automatically extract information that requires inferencing from clinical notes.

Normalizing acronyms and abbreviations to aid patient understanding of clinical texts: ShARe/CLEF eHealth Challenge 2013, Task 2.

Background: The ShARe/CLEF eHealth challenge lab aims to stimulate development of natural language processing and information retrieval technologies to aid patients in understanding their clinical reports. In clinical text, acronyms and abbreviations, also referenced as short forms, can be difficult for patients to understand. For one of three shared tasks in 2013 (Task 2), we generated a reference standard of clinical short forms normalized to the Unified Medical Language System.

Towards a Generalizable Time Expression Model for Temporal Reasoning in Clinical Notes.

Accurate temporal identification and normalization is imperative for many biomedical and clinical tasks such as generating timelines and identifying phenotypes. A major natural language processing challenge is developing and evaluating a generalizable temporal modeling approach that performs well across corpora and institutions. Our long-term goal is to create such a model.

Evaluating the state of the art in disorder recognition and normalization of the clinical narrative.

Objective: The ShARe/CLEF eHealth 2013 Evaluation Lab Task 1 was organized to evaluate the state of the art on the clinical text in (i) disorder mention identification/recognition based on Unified Medical Language System (UMLS) definition (Task 1a) and (ii) disorder mention normalization to an ontology (Task 1b). Such a community evaluation has not been previously executed. Task 1a included a total of 22 system submissions, and Task 1b included 17.

Natural Language Processing to identify pneumonia from radiology reports.

Purpose: This study aimed to develop Natural Language Processing (NLP) approaches to supplement manual outcome validation, specifically to validate pneumonia cases from chest radiograph reports.

Methods: We trained one NLP system, ONYX, using radiograph reports from children and adults that were previously manually reviewed. We then assessed its validity on a test set of 5000 reports.

Methodology to develop and evaluate a semantic representation for NLP.

Natural language processing applications that extract information from text rely on semantic representations. The objective of this paper is to describe a methodology for creating a semantic representation for information that will be automatically extracted from textual clinical records. We illustrate two of the four steps of the methodology in this paper using the case study of encoding information from dictated dental exams: (1) develop an initial representation from a set of training documents and (2) iteratively evaluate and evolve the representation while developing annotation guidelines.

Identification of trauma patients at a level 1 trauma center utilizing natural language processing.

Trauma centers use trauma registries to collect information on injured patients they receive. The information is used for evaluation of care rendered, research, system and process improvement, and evaluation of injury prevention programs. Identification of patients qualifying for inclusion in registries can be problematic.

Classifying free-text triage chief complaints into syndromic categories with natural language processing.

Objective: Develop and evaluate a natural language processing application for classifying chief complaints into syndromic categories for syndromic surveillance.

Introduction: Much of the input data for artificial intelligence applications in the medical field are free-text patient medical records, including dictated medical reports and triage chief complaints. To be useful for automated systems, the free-text must be translated into encoded form.