free text
LePendu P et al, Clinical Pharmacology & Therapeutics, 2013
With increasing adoption of electronic health records (EHRs), there is an opportunity to use the free-text portion of EHRs for pharmacovigilance. We present novel methods that annotate the unstructured clinical notes and transform them into a deidentified patient–feature matrix encoded using medical terminologies. We demonstrate the use of the resulting high-throughput data for detecting drug–adverse event associations and adverse events associated with drug–drug interactions.
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Published:
10 April 2013 |
Keyword(s): Adverse Drug Events, Data Mining, Drug-drug interactions, Electronic Health Records, Free text, Pharmacology, United States
Denny JC. PLoS Comput Biol, 8(12)
The combination of improved genomic analysis methods, decreasing genotyping costs, and increasing computing resources has led to an explosion of clinical genomic knowledge in the last decade. Similarly, healthcare systems are increasingly adopting robust electronic health record (EHR) systems that not only can improve health care, but also contain a vast repository of disease and treatment data that could be mined for genomic research. Indeed, institutions are creating EHR-linked DNA biobanks to enable genomic and pharmacogenomic research, using EHR data for phenotypic information.
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Published:
27 December 2012 |
Keyword(s): Biobank, Data Mining, Electronic Health Records, Free text, Genomics, Narrative, United States
Shah AD et al, BMC medical informatics and decision making, 12(1)
BACKGROUND:
Electronic health records are invaluable for medical research, but much information is stored as free text rather than in a coded form. For example, in the UK General Practice Research Database (GPRD), causes of death and test results are sometimes recorded only in free text. Free text can be difficult to use for research if it requires time-consuming manual review. Our aim was to develop an automated method for extracting coded information from free text in electronic patient records.
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Published:
7 August 2012 |
Keyword(s): Algorithms, Electronic Health Records, Free text, Research, UK
Fernando B et al, BMJ Quality and Safety, 2012
Background
Patient histories in electronic health records currently exist mainly in free text format thereby limiting the possibility that decision support technology may contribute to the accuracy and timeliness of clinical diagnoses. Structuring and/or coding make patient histories potentially computable.
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Published:
10 February 2012 |
Keyword(s): Coding, Decision Support, Electronic Health Records, Free text, Systematic Review
Schuemie MJ et al, Pharmacoepidemiology and Drug Safety, 2012
PURPOSE:
Increasingly, patient information is stored in electronic medical records, which could be reused for research. Often these records comprise unstructured narrative data, which are cumbersome to analyze. The authors investigated whether text mining can make these data suitable for epidemiological studies and compared a concept recognition approach and a range of machine learning techniques that require a manually annotated training set. The authors show how this training set can be created with minimal effort by using a broad database query.
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Published:
24 January 2012 |
Keyword(s): Electronic Health Records, Epidemiology, Free text, Narrative
Schmickl CN et al, Respiratory Medicine, 2011
Participant recruitment is an important process in successful conduct of randomized controlled trials. To facilitate enrollment into a National Institutes of Health–sponsored clinical trial involving patients with chronic obstructive pulmonary disease (COPD), we developed and prospectively validated an automated electronic screening tool based on boolean free-text search of admission notes in electronic medical records. During a 2-week validation period, all patients admitted to prespecified general medical services were screened for eligibility by both the electronic screening tool and a COPD nurse. Group discussion was the gold standard for confirmation of true-positive results.
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Published:
14 May 2011 |
Keyword(s): Accuracy, Clinical Trials, COPD, Efficiency, Electronic Health Records, Electronic Medical Records, Electronic screening, Free text, Patient recruitment, Search
Seidling HM et al, International Journal of Medical Informatics, 79(11)
Purpose
A key trade-off in computerized clinical documentation exists between collecting coded data versus free-text. Coded data are more readily computer-readable and easier to reuse in different contexts. However, clinical information often exceeds the scope of commonly available terminologies, and coding may be resisted by providers. Alert override reasons are one domain for which agreed-upon terminologies are rarely used. Few data are available on how the collection of information affects the responses of providers.
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Published:
24 September 2010 |
Keyword(s): Alerts and Reminders, Coding, Decision Support, Documentation, Drug Interactions, Free text
Konovalov S et al, J Med Internet Res, 12(4)
Introduction:
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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Published:
5 October 2010 |
Keyword(s): Biomedical Informatics, Blog, Free text, Information Storage and Retrieval, NLP, United States
Tu K et al, BMC Medical Informatics and Decision Making, 10(1)
BACKGROUND:
Electronic medical records (EMRs) represent a potentially rich source of health information for research but the free-text in EMRs often contains identifying information. While de-identification tools have been developed for free-text, none have been developed or tested for the full range of primary care EMR data.
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Published:
18 June 2010 |
Keyword(s): Canada, De-identify, Electronic Health Records, Electronic Medical Records, Free text, Primary Care, Research
