Ascertainment of asthma prognosis using natural language processing from electronic medical records.

NLP algorithm successfully determined asthma prognosis (i.e., no remission, long-term remission, and intermittent remission) by taking into account asthma symptoms documented in EMR, and addressed the limitations of billing code- based asthma outcome assessment.

MACE: model based analysis of ChIP-exo.

Understanding the role of a given transcription factor (TF) in regulating gene expression requires precise mapping of its binding sites in the genome. Chromatin immunoprecipitation-exo, an emerging technique using λ exonuclease to digest TF unbound DNA after ChIP, is designed to reveal transcription factor binding site (TFBS) boundaries with near-single nucleotide resolution. Although ChIP-exo promises deeper insights into transcription regulation, no dedicated bioinformatics tool exists to leverage its advantages.

Patient-level temporal aggregation for text-based asthma status ascertainment.

Objective: To specify the problem of patient-level temporal aggregation from clinical text and introduce several probabilistic methods for addressing that problem. The patient-level perspective differs from the prevailing natural language processing (NLP) practice of evaluating at the term, event, sentence, document, or visit level.

Methods: We utilized an existing pediatric asthma cohort with manual annotations.

Open Source Clinical NLP - More than Any Single System.

The number of Natural Language Processing (NLP) tools and systems for processing clinical free-text has grown as interest and processing capability have surged. Unfortunately any two systems typically cannot simply interoperate, even when both are built upon a framework designed to facilitate the creation of pluggable components. We present two ongoing activities promoting open source clinical NLP.

An information extraction framework for cohort identification using electronic health records.

Information extraction (IE), a natural language processing (NLP) task that automatically extracts structured or semi-structured information from free text, has become popular in the clinical domain for supporting automated systems at point-of-care and enabling secondary use of electronic health records (EHRs) for clinical and translational research. However, a high performance IE system can be very challenging to construct due to the complexity and dynamic nature of human language. In this paper, we report an IE framework for cohort identification using EHRs that is a knowledge-driven framework developed under the Unstructured Information Management Architecture (UIMA).

Automated chart review for asthma cohort identification using natural language processing: an exploratory study.

Background: A significant proportion of children with asthma have delayed diagnosis of asthma by health care providers. Manual chart review according to established criteria is more accurate than directly using diagnosis codes, which tend to under-identify asthmatics, but chart reviews are more costly and less timely.

Objective: To evaluate the accuracy of a computational approach to asthma ascertainment, characterizing its utility and feasibility toward large-scale deployment in electronic medical records.

Analysis of cross-institutional medication description patterns in clinical narratives.

A large amount of medication information resides in the unstructured text found in electronic medical records, which requires advanced techniques to be properly mined. In clinical notes, medication information follows certain semantic patterns (eg, medication, dosage, frequency, and mode). Some medication descriptions contain additional word(s) between medication attributes.

Tracking health disparities through natural-language processing.

Health disparities and solutions are heterogeneous within and among racial and ethnic groups, yet existing administrative databases lack the granularity to reflect important sociocultural distinctions. We measured the efficacy of a natural-language-processing algorithm to identify a specific immigrant group. The algorithm demonstrated accuracy and precision in identifying Somali patients from the electronic medical records at a single institution.

Towards a semantic lexicon for clinical natural language processing.

A semantic lexicon which associates words and phrases in text to concepts is critical for extracting and encoding clinical information in free text and therefore achieving semantic interoperability between structured and unstructured data in Electronic Health Records (EHRs). Directly using existing standard terminologies may have limited coverage with respect to concepts and their corresponding mentions in text. In this paper, we analyze how tokens and phrases in a large corpus distribute and how well the UMLS captures the semantics.

A common type system for clinical natural language processing.

Background: One challenge in reusing clinical data stored in electronic medical records is that these data are heterogenous. Clinical Natural Language Processing (NLP) plays an important role in transforming information in clinical text to a standard representation that is comparable and interoperable. Information may be processed and shared when a type system specifies the allowable data structures.

Using SNOMED-CT to encode summary level data - a corpus analysis.

Extracting and encoding clinical information captured in free text with standard medical terminologies is vital to enable secondary use of electronic medical records (EMRs) for clinical decision support, improved patient safety, and clinical/translational research. A critical portion of free text is comprised of 'summary level' information in the form of problem lists, diagnoses and reasons of visit. We conducted a systematic analysis of SNOMED-CT in representing the summary level information utilizing a large collection of summary level data in the form of itemized entries.

Coreference analysis in clinical notes: a multi-pass sieve with alternate anaphora resolution modules.

Objective: This paper describes the coreference resolution system submitted by Mayo Clinic for the 2011 i2b2/VA/Cincinnati shared task Track 1C. The goal of the task was to construct a system that links the markables corresponding to the same entity.

Materials And Methods: The task organizers provided progress notes and discharge summaries that were annotated with the markables of treatment, problem, test, person, and pronoun.

Unified Medical Language System term occurrences in clinical notes: a large-scale corpus analysis.

Objective: To characterise empirical instances of Unified Medical Language System (UMLS) Metathesaurus term strings in a large clinical corpus, and to illustrate what types of term characteristics are generalisable across data sources.

Design: Based on the occurrences of UMLS terms in a 51 million document corpus of Mayo Clinic clinical notes, this study computes statistics about the terms' string attributes, source terminologies, semantic types and syntactic categories. Term occurrences in 2010 i2b2/VA text were also mapped; eight example filters were designed from the Mayo-based statistics and applied to i2b2/VA data.