Dataset Information

Automatic extraction of medication information from medical discharge summaries.

ABSTRACT:

Objective

This article describes a system developed for the 2009 i2b2 Medication Extraction Challenge. The purpose of this challenge is to extract medication information from hospital discharge summaries.

Design

The system explored several linguistic natural language processing techniques (eg, term-based and token-based rule matching) to identify medication-related information in the narrative text. A number of lexical resources was constructed to profile lexical or morphological features for different categories of medication constituents.

Measurements

Performance was evaluated in terms of the micro-averaged F-measure at the horizontal system level.

Results

The automated system performed well, and achieved an F-micro of 80% for the term-level results and 81% for the token-level results, placing it sixth in exact matches and fourth in inexact matches in the i2b2 competition.

Conclusion

The overall results show that this relatively simple rule-based approach is capable of tackling multiple entity identification tasks such as medication extraction under situations in which few training documents are annotated for machine learning approaches, and the entity information can be characterized with a set of feature tokens.

SUBMITTER: Yang H

PROVIDER: S-EPMC2995675 | biostudies-literature | 2010 Sep-Oct

REPOSITORIES: biostudies-literature

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Publications

Automatic extraction of medication information from medical discharge summaries.

Yang Hui H

Journal of the American Medical Informatics Association : JAMIA 20100901 5

<h4>Objective</h4>This article describes a system developed for the 2009 i2b2 Medication Extraction Challenge. The purpose of this challenge is to extract medication information from hospital discharge summaries.<h4>Design</h4>The system explored several linguistic natural language processing techniques (eg, term-based and token-based rule matching) to identify medication-related information in the narrative text. A number of lexical resources was constructed to profile lexical or morphological ...[more]

PMID: 20819861

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Project description:BackgroundFor ecological studies, it is crucial to count on adequate descriptions of the environments and samples being studied. Such a description must be done in terms of their physicochemical characteristics, allowing a direct comparison between different environments that would be difficult to do otherwise. Also the characterization must include the precise geographical location, to make possible the study of geographical distributions and biogeographical patterns. Currently, there is no schema for annotating these environmental features, and these data have to be extracted from textual sources (published articles). So far, this had to be performed by manual inspection of the corresponding documents. To facilitate this task, we have developed EnvMine, a set of text-mining tools devoted to retrieve contextual information (physicochemical variables and geographical locations) from textual sources of any kind.ResultsEnvMine is capable of retrieving the physicochemical variables cited in the text, by means of the accurate identification of their associated units of measurement. In this task, the system achieves a recall (percentage of items retrieved) of 92% with less than 1% error. Also a Bayesian classifier was tested for distinguishing parts of the text describing environmental characteristics from others dealing with, for instance, experimental settings.Regarding the identification of geographical locations, the system takes advantage of existing databases such as GeoNames to achieve 86% recall with 92% precision. The identification of a location includes also the determination of its exact coordinates (latitude and longitude), thus allowing the calculation of distance between the individual locations.ConclusionEnvMine is a very efficient method for extracting contextual information from different text sources, like published articles or web pages. This tool can help in determining the precise location and physicochemical variables of sampling sites, thus facilitating the performance of ecological analyses. EnvMine can also help in the development of standards for the annotation of environmental features.

Project description:BackgroundIneffective transitions of care continue to be a source of risk for patients. Although there has been widespread implementation of electronic medical record (EMR) systems, little is currently known about hospitalists' and primary care providers' (PCPs) direct communication preferences at discharge using messaging capabilities in a shared EMR system.ObjectiveWe examined how hospitalists and PCPs with a shared EMR prefer to directly communicate at the time of hospital discharge by identifying preferred modes, information prioritization, challenges, facilitators, and proposed solutions.DesignA sequential, explanatory mixed methods study with surveys and semi-structured interviews.ParticipantsThirty-eight academic hospitalists and 63 PCPs working in outpatient clinics in a single safety net hospital system with a shared EMR.Main approachDescriptive statistics were used to analyze survey responses. Interviews were analyzed using immersion/crystallization and a mixture of inductive and deductive thematic analysis.Key resultsPCPs preferred direct communication at discharge through a message within the EMR while hospitalists preferred a message within the EMR and email. Qualitative results identified key themes related to patient care and direct communication: value of direct communication, safety, social determinants of health, and clinical judgment. Both groups prioritized direct communication for high-risk medications, pending and follow-up studies, and high-risk patients that hospitalists were concerned about. Overall, both hospitalists and PCPs reported that ensuring patient safety, flagging patients with social challenges, and expressing concerns about patients based on clinical judgment were key communication priorities.ConclusionsHospitalists and primary care providers report considerable overlap in preferences for direct communication at the time of hospital discharge through a shared EMR. Specifically, both groups reported similar concerns regarding patient safety and continuity during transitions. Direct messaging within the EMR could enable "closed loop" communication that helps ensure safe transitions of care for high-risk patients.

Dataset Information

Automatic extraction of medication information from medical discharge summaries.

Objective

Design

Measurements

Results

Conclusion

Publications

Automatic extraction of medication information from medical discharge summaries.

Similar Datasets

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