the international council on medical & care compunetics


December, 2014


Automated identification of postoperative complications within an electronic medical record using natural language processing

Murff HJ et al, JAMA, 306(8)

Currently most automated methods to identify patient safety occurrences rely on administrative data codes; however, free-text searches of electronic medical records could represent an additional surveillance approach.

To evaluate a natural language processing search-approach to identify postoperative surgical complications within a comprehensive electronic medical record.
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25 August 2011 | No Comments »
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Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions

Chapman WW et al, J Am Med Inform Assoc, 18(5)

This issue of JAMIA focuses on natural language processing (NLP) techniques for clinical-text information extraction. Several articles are offshoots of the yearly ‘Informatics for Integrating Biology and the Bedside’ (i2b2) (http://www.i2b2.org) NLP shared-task challenge, introduced by Uzuner et al (see page 552) and co-sponsored by the Veteran’s Administration for the last 2 years. This shared task follows long-running challenge evaluations in other fields, such as the Message Understanding Conference (MUC) for information extraction, TREC for text information retrieval, and CASP for protein structure prediction. Shared tasks in the clinical domain are recent and include annual i2b2 Challenges that began in 2006, a challenge for multi-label classification of radiology reports sponsored by Cincinnati Children’s Hospital in 2007, a 2011 Cincinnati Children’s Hospital challenge on suicide notes, and the 2011 TREC information retrieval shared task involving retrieval of clinical cases from narrative records.
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18 August 2011 | No Comments »
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Evaluation of natural language processing from emergency department computerized medical records for intra-hospital syndromic surveillance

Gerbier S et al, BMC Medical Informatics and Decision Making, 11(1)

The identification of patients who pose an epidemic hazard when they are admitted to a health facility plays a role in preventing the risk of hospital acquired infection. An automated clinical decision support system to detect suspected cases, based on the principle of syndromic surveillance, is being developed at the University of Lyon’s Hopital de la Croix-Rousse. This tool will analyse structured data and narrative reports from computerized emergency department (ED) medical records. The first step consists of developing an application (UrgIndex) which automatically extracts and encodes information found in narrative reports. The purpose of the present article is to describe and evaluate this natural language processing system.
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8 August 2011 | No Comments »
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Computer-assisted update of a consumer health vocabulary through mining of social network data

Doing-Harris KM, Zeng-Treitler Q. J Med Internet Res, 13(2)

Consumer health vocabularies (CHVs) have been developed to aid consumer health informatics applications. This purpose is best served if the vocabulary evolves with consumers’ language.

Our objective was to create a computer assisted update (CAU) system that works with live corpora to identify new candidate terms for inclusion in the open access and collaborative (OAC) CHV.
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29 May 2011 | No Comments »
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Natural Language Processing Improves Identification of Colorectal Cancer Testing in the Electronic Medical Record

Denny JC et al, Medical Decision Making, 2011

Difficulty identifying patients in need of colorectal cancer (CRC) screening contributes to low screening rates. OBJECTIVE: To use Electronic Health Record (EHR) data to identify patients with prior CRC testing.

DESIGN: . A clinical natural language processing (NLP) system was modified to identify 4 CRC tests (colonoscopy, flexible sigmoidoscopy, fecal occult blood testing, and double contrast barium enema) within electronic clinical documentation. Text phrases in clinical notes referencing CRC tests were interpreted by the system to determine whether testing was planned or completed and to estimate the date of completed tests. Setting. Large academic medical center. Patients. 200 patients ≥50 years old who had completed ≥2 non-acute primary care visits within a 1-year period.
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13 March 2011 | No Comments »
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Biomedical Informatics Techniques for Processing and Analyzing Web Blogs of Military Service Members

Konovalov S et al, J Med Internet Res, 12(4)

Web logs (“blogs”) have become a popular mechanism for people to express their daily thoughts, feelings, and emotions. Many of these expressions contain health care-related themes, both physical and mental, similar to information discussed during a clinical interview or medical consultation. Thus, some of the information contained in blogs might be important for health care research, especially in mental health where stress-related conditions may be difficult and expensive to diagnose and where early recognition is often key to successful treatment. In the field of biomedical informatics, techniques such as information retrieval (IR) and natural language processing (NLP) are often used to unlock information contained in free-text notes. These methods might assist the clinical research community to better understand feelings and emotions post deployment and the burden of symptoms of stress among US military service members.
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5 October 2010 | No Comments »
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Robust Replication of Genotype-Phenotype Associations across Multiple Diseases in an Electronic Medical Record

Ritchie, Marylyn D. et al, The American Journal of Human Genetics, 2010

Large-scale DNA databanks linked to electronic medical record (EMR) systems have been proposed as an approach for rapidly generating large, diverse cohorts for discovery and replication of genotype-phenotype associations. However, the extent to which such resources are capable of delivering on this promise is unknown. We studied whether an EMR-linked DNA biorepository can be used to detect known genotype-phenotype associations for five diseases. Twenty-one SNPs previously implicated as common variants predisposing to atrial fibrillation, Crohn disease, multiple sclerosis, rheumatoid arthritis, or type 2 diabetes were successfully genotyped in 9483 samples accrued over 4 mo into BioVU, the Vanderbilt University Medical Center DNA biobank. Previously reported odds ratios (ORPR) ranged from 1.14 to 2.36. For each phenotype, natural language processing techniques and billing-code queries were used to identify cases (n = 70–698) and controls (n = 808–3818) from deidentified health records.
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6 April 2010 | No Comments »
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Biomedical informatics and translational medicine

Sarkar I. Journal of Translational Medicine, 8(1)

Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the “translational barriers” associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies.
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26 February 2010 | No Comments »
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Clinical data mining and research in the allergy office

Dalan, Dan, Current Opinion in Allergy and Clinical Immunology, 2010

Purpose of review:
More data are anticipated from the expected increase in use of electronic health records (EHRs). Upcoming initiatives require reporting of quality measures, meaningful use of clinical decision support, alert systems, and pharmacovigilance – knowledge resulting through use of EHRs. Data mining is a new tool that will help us manage information and derive knowledge from these data, and is a part of evolving new disciplines of informatics and knowledge management.
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26 February 2010 | No Comments »
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MedEx: a medication information extraction system for clinical narratives

Xu, Hua et al, J Am Med Inform Assoc, 17(1)

Medication information is one of the most important types of clinical data in electronic medical records. It is critical for healthcare safety and quality, as well as for clinical research that uses electronic medical record data. However, medication data are often recorded in clinical notes as free-text. As such, they are not accessible to other computerized applications that rely on coded data. We describe a new natural language processing system (MedEx), which extracts medication information from clinical notes.
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16 December 2009 | No Comments »
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Characterizing environmental and phenotypic associations using information theory and electronic health records

Wang, Xiaoyan et al, BMC Bioinformatics, 10(s9)

The availability of up-to-date, executable, evidence-based medical knowledge is essential for many clinical applications, such as pharmacovigilance, but executable knowledge is costly to obtain and update. Automated acquisition of environmental and phenotypic associations in biomedical and clinical documents using text mining has showed some success. The usefulness of the association knowledge is limited, however, due to the fact that the specific relationships between clinical entities remain unknown. In particular, some associations are indirect relations due to interdependencies among the data.
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27 September 2009 | No Comments »
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Text Mining and Natural Language Processing Approaches for Automatic Categorization of Lay Requests to Web-Based Expert Forums

Himmel, Wolfgang et al, J Med Internet Res, 11(3)

Both healthy and sick people increasingly use electronic media to obtain medical information and advice. For example, Internet users may send requests to Web-based expert forums, or so-called “ask the doctor” services.
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22 July 2009 | No Comments »
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Incremental Semantic Enrichment of Narrative Content in Electronic Health Records

Stefan Schulz, IFMBE Proceedings 2009, 25/12

Semantic interoperability is a major desideratum in health care for computer-based documentation and communication through electronic health records. They require structured data ideally represented via standardized information models, e.g., HL7 RIM or openEHR, connected to standardized terminologies or ontologies, e.g., SNOMED CT or LOINC. But since natural language is seen by health professionals as their most natural and effective form of expression, semantically interoperable architectures must adequately deal with unstructured data and be seamlessly integrated into the workflows of health professionals. Therefore we propose a self-learning natural language processing system, which automatically segments input narratives into sections, detects contexts such as negations, and assigns terminology codes. To be usable in clinical contexts, the system must properly handle the idiosyncratic medical language and grammar and spelling errors of narratives produced in everyday clinical practice. We present user interaction items that are important to make the interaction with the system as easy as possible to reach high acceptance among health professionals.

20 July 2009 | No Comments »
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Exploring the Ability of Natural Language Processing to Extract Data From Nursing Narratives

Hyun, Sookyung et al, Computers Informatics Nursing, 27(4)

Natural Language Processing (NLP) offers an approach for capturing data from narratives and creating structured reports for further computer processing. We explored the ability of a NLP system, Medical Language Extraction and Encoding (MedLEE), on nursing narratives. MedLEE extracted 490 concepts from narrative text in a sample of 553 oncology nursing process notes.
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4 July 2009 | No Comments »
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Developing a standard for de-identifying electronic patient records written in Swedish: Precision, recall and F-measure in a manual and computerized annotation trial

Velupillai, Sumithra et al, International Journal of Medical Informatics, 78(12)

Electronic patient records (EPRs) contain a large amount of information written in free text. This information is considered very valuable for research but is also very sensitive since the free text parts may contain information that could reveal the identity of a patient. Therefore, methods for de-identifying EPRs are needed. The work presented here aims to perform a manual and automatic Protected Health Information (PHI)-annotation trial for EPRs written in Swedish.
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1 June 2009 | No Comments »
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Syndromic surveillance using ambulatory electronic health records

Hripcsak, George et al, J Am Med Inform Assoc, 16(3)

To assess the performance of electronic health record data for syndromic surveillance and to assess the feasibility of broadly distributed surveillance.
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19 May 2009 | No Comments »
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Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study

Wang, Xiaoyan et al, J Am Med Inform Assoc, 16, 3

It is vital to detect the full safety profile of a drug throughout its market life. Current pharmacovigilance systems still have substantial limitations, however. The objective of our work is to demonstrate the feasibility of using natural language processing (NLP), the comprehensive Electronic Health Record (EHR), and association statistics for pharmacovigilance purposes.
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19 May 2009 | No Comments »
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Mining for Measures: NLP Technology Eases the Task of Reporting Quality Measures

Carol, Ruth, Journal of AHIMA, 80(4)

Healthcare organizations have been asked to report quality measures since the 1990s. The activity went public when the Joint Commission began releasing such data in 2004. The Centers for Medicare and Medicaid Services (CMS) followed suit by publishing hospital data on its Web site the following year (www.hospitalcompare.hhs.gov).
However, nearly two decades later the process of reporting measures remains a tedious one, still largely performed manually. This is especially true of measures collected from unstructured, free-text documents such as physician notes.
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20 April 2009 | No Comments »
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Assessment of commercial NLP engines for medication information extraction from dictated clinical notes

Jagannathan, V. et al, International Journal of Medical Informatics, 78(4)

We assessed the current state of commercial natural language processing (NLP) engines for their ability to extract medication information from textual clinical documents.
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7 March 2009 | No Comments »
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Repurposing the clinical record: can an existing natural language processing system de-identify clinical notes?

Morrison, Frances P. et al, J Am Med Inform Assoc, 16(1)

Electronic clinical documentation can be useful for activities such as public health surveillance, quality improvement, and research, but existing methods of de-identification may not provide sufficient protection of patient data. The general-purpose natural language processor MedLEE retains medical concepts while excluding the remaining text so, in addition to processing text into structured data, it may be able provide a secondary benefit of de-identification. Without modifying the system, the authors tested the ability of MedLEE to remove protected health information (PHI) by comparing 100 outpatient clinical notes with the corresponding XML-tagged output. Of 809 instances of PHI, 26 (3.2%) were detected in output as a result of processing and identification errors.
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15 December 2008 | No Comments »
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