Using text‐mining techniques in electronic patient records to identify ADRs from medicine use

Warrer, Pernille; Hansen, Ebba Holme; Jensen, Lars Juhl; Aagaard, Lise

doi:10.1111/j.1365-2125.2011.04153.x

Cited by 83 publications

(55 citation statements)

References 19 publications

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“…Studies using text searching methods have also been shown to be an efficient and practical means to identify other ADR's 12 . For example, Honigman et al found that free text searching was superior to diagnosis codes, allergy codes, and a computerized event monitoring system for detecting various ADR's with an overall sensitivity of 91% and a positive predictive value (PPV) of 7.2% 13 .…”

Section: Introductionmentioning

confidence: 99%

A Text Searching Tool to Identify Patients with Idiosyncratic Drug-Induced Liver Injury

2015

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Background Idiosyncratic drug induced liver injury (DILI) is an uncommon but important cause of liver disease that is challenging to diagnose and identify in the electronic medical record (EMR). Aim To develop an accurate, reliable, and efficient method of identifying patients with bonafide DILI in an EMR system. Methods 527,000 outpatient and ER encounters in an EPIC-based EMR were searched for potential DILI cases attributed to 8 drugs. A searching algorithm that extracted 200 characters of text around 14 liver injury terms in the EMR were extracted and collated. Physician investigators reviewed the data outputs and used standardized causality assessment methods to adjudicate the potential DILI cases. Results A total of 101 DILI cases were identified from the 2,564 potential DILI cases that included 62 probable DILI cases, 25 possible DILI cases, 9 historical DILI cases, and 5 allergy only cases. Elimination of the term “liver disease” from the search strategy improved the search recall from 4% to 19% while inclusion of the 4 highest yield liver injury terms further improved the positive predictive value to 64% but reduced the overall case detection rate by 47%. RUCAM scores of the 57 probable DILI cases were generally high and concordant with expert opinion causality assessment scores. Conclusions A novel text searching tool was developed that identified a large number of DILI cases from a widely used EMR system. A computerized extraction of dictated text followed by the manual review of text snippets can rapidly identify bonafide cases of idiosyncratic DILI.

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Section: Introductionmentioning

confidence: 99%

A Text Searching Tool to Identify Patients with Idiosyncratic Drug-Induced Liver Injury

2015

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“…Several recent and ongoing projects have attempted to extract ADEs from the DailyMed structured product labels (e.g., [11]), unstructured clinical notes, like those in electronic health records (e.g., [12–14]), the social media (e.g., [15, 16]), from the biomedical literature, especially MEDLINE ® (e.g., [17–19]), or a combination of such sources (e.g., [20]).…”

Section: Introductionmentioning

confidence: 99%

Leveraging MEDLINE indexing for pharmacovigilance – Inherent limitations and mitigation strategies

Winnenburg¹,

Sorbello

Ripple³

et al. 2015

Journal of Biomedical Informatics

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Background Traditional approaches to pharmacovigilance center on the signal detection from spontaneous reports, e.g., the U.S. Food and Drug Administration (FDA) adverse event reporting system (FAERS). In order to enrich the scientific evidence and enhance the detection of emerging adverse drug events that can lead to unintended harmful outcomes, pharmacovigilance activities need to evolve to encompass novel complementary data streams, for example the biomedical literature available through MEDLINE. Objectives 1) to review how the characteristics of MEDLINE indexing influence the identification of adverse drug events (ADEs); 2) to leverage this knowledge to inform the design of a system for extracting ADEs from MEDLINE indexing; and 3) to assess the specific contribution of some characteristics of MEDLINE indexing to the performance of this system. Methods We analyze the characteristics of MEDLINE indexing. We integrate three specific characteristics into the design of a system for extracting ADEs from MEDLINE indexing. We experimentally assess the specific contribution of these characteristics over a baseline system based on co-occurrence between drug descriptors qualified by adverse effects and disease descriptors qualified by chemically induced. Results Our system extracted 405,300 ADEs from 366,120 MEDLINE articles. The baseline system accounts for 297,093 ADEs (73%). 85,318 ADEs (21%) can be extracted only after integrating specific pre-coordinated MeSH descriptors and additional qualifiers. 22,889 ADEs (6%) can be extracted only after considering indirect links between the drug of interest and the descriptor that bears the ADE context. Conclusions In this paper, we demonstrate significant improvement over a baseline approach to identifying ADEs from MEDLINE indexing, which mitigates some of the inherent limitations of MEDLINE indexing for pharmacovigilance. ADEs extracted from MEDLINE indexing are complementary to, not a replacement for, other sources.

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“…Warrer et al investigated studies that "use text-mining techniques in narrative documents stored in electronic patient records (EPRs) to investigate ADRs"[14]. They searched PubMed, Embase, Web of Science and International Pharmaceutical Abstracts without restrictions from origin until July 2011.…”

Section: Introductionmentioning

confidence: 99%

Knowledge discovery of drug data on the example of adverse reaction prediction

et al. 2014

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BackgroundAntibiotics are the widely prescribed drugs for children and most likely to be related with adverse reactions. Record on adverse reactions and allergies from antibiotics considerably affect the prescription choices. We consider this a biomedical decision-making problem and explore hidden knowledge in survey results on data extracted from a big data pool of health records of children, from the Health Center of Osijek, Eastern Croatia.ResultsWe applied and evaluated a k-means algorithm to the dataset to generate some clusters which have similar features. Our results highlight that some type of antibiotics form different clusters, which insight is most helpful for the clinician to support better decision-making.ConclusionsMedical professionals can investigate the clusters which our study revealed, thus gaining useful knowledge and insight into this data for their clinical studies.

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Using text‐mining techniques in electronic patient records to identify ADRs from medicine use

Cited by 83 publications

References 19 publications

A Text Searching Tool to Identify Patients with Idiosyncratic Drug-Induced Liver Injury

A Text Searching Tool to Identify Patients with Idiosyncratic Drug-Induced Liver Injury

Leveraging MEDLINE indexing for pharmacovigilance – Inherent limitations and mitigation strategies

Knowledge discovery of drug data on the example of adverse reaction prediction

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