Search

Objectives: The aim of this research was to automate the search of publications concerning adverse drug reactions (ADR) by defining the queries used to search MEDLINE and by determining the required threshold for the number of extracted publications to confirm the drug/event association in the literature., Methods: We defined an approach based on the medical subject headings (MeSH) 'descriptor records' and 'supplementary concept records' thesaurus, using the subheadings 'chemically induced' and 'adverse effects' with the 'pharmacological action' knowledge. An expert-built validation set of true positive and true negative drug/adverse event associations (n=61) was used to validate our method., Results: Using a threshold of three of more extracted publications, the automated search method presented a sensitivity of 90% and a specificity of 100%. For nine different drug/event pairs selected, the recall of the automated search ranged from 24% to 64% and the precision from 93% to 48%., Conclusions: This work presents a method to find previously established relationships between drugs and adverse events in the literature. Using MEDLINE, following a MeSH approach to filter the signals, is a valid option. Our contribution is available as a web service that will be integrated in the final European EU-ADR project (Exploring and Understanding Adverse Drug Reactions by integrative mining of clinical records and biomedical knowledge) automated system.

Objective: Data from electronic healthcare records (EHR) can be used to monitor drug safety, but in order to compare and pool data from different EHR databases, the extraction of potential adverse events must be harmonized. In this paper, we describe the procedure used for harmonizing the extraction from eight European EHR databases of five events of interest deemed to be important in pharmacovigilance: acute myocardial infarction (AMI); acute renal failure (ARF); anaphylactic shock (AS); bullous eruption (BE); and rhabdomyolysis (RHABD)., Design: The participating databases comprise general practitioners' medical records and claims for hospitalization and other healthcare services. Clinical information is collected using four different disease terminologies and free text in two different languages. The Unified Medical Language System was used to identify concepts and corresponding codes in each terminology. A common database model was used to share and pool data and verify the semantic basis of the event extraction queries. Feedback from the database holders was obtained at various stages to refine the extraction queries., Measurements: Standardized and age specific incidence rates (IRs) were calculated to facilitate benchmarking and harmonization of event data extraction across the databases. This was an iterative process., Results: The study population comprised overall 19 647 445 individuals with a follow-up of 59 929 690 person-years (PYs). Age adjusted IRs for the five events of interest across the databases were as follows: (1) AMI: 60-148/100 000 PYs; (2) ARF: 3-49/100 000 PYs; (3) AS: 2-12/100 000 PYs; (4) BE: 2-17/100 000 PYs; and (5) RHABD: 0.1-8/100 000 PYs., Conclusions: The iterative harmonization process enabled a more homogeneous identification of events across differently structured databases using different coding based algorithms. This workflow can facilitate transparent and reproducible event extractions and understanding of differences between databases.

The overall objective of the eu-ADR project is the design, development, and validation of a computerised system that exploits data from electronic health records and biomedical databases for the early detection of adverse drug reactions. Eight different databases, containing health records of more than 30 million European citizens, are involved in the project. Unique queries cannot be performed across different databases because of their heterogeneity: Medical record and Claims databases, four different terminologies for coding diagnoses, and two languages for the information described in free text. The aim of our study was to provide database owners with a common basis for the construction of their queries. Using the UMLS, we provided a list of medical concepts, with their corresponding terms and codes in the four terminologies, which should be considered to retrieve the relevant information for the events of interest from the databases.

The French ministry of Health is setting up the Personal Medical Record (PMR). This innovative tool has long been expected by French Health Authorities, Associations of Patients, other Health's associations, those defending Individual Liberties and the French National Data Protection Authority. The PMR will lead to improvements in many areas such as Diagnosis (Research and monitoring) Healthcare (Management of emergencies, urgent situations, Temporal health monitoring and evaluation), Therapy (Cohorts of patients for Clinical trials and epidemiological studies). The PMR will foster safe healthcare management, clinical research and epidemiological studies. Nevertheless, it raises many important questions regarding duplicates and the quality, precision and coherence of the linkage with other health data coming from different sources. The currently planned identifying process raises many questions with regard to its ability to deal with potential duplicates and to perform data linkage with other health data sources. Through this article, using the electronic health records, we develop and propose an identification process to improve the French PMR. Our proposed unique patient identifier will guarantee the security, confidentiality and privacy of the personal data, and will prove to be particularly useful for health planning, health policies and research as well as clinical and epidemiological studies. Finally, it will certainly be interoperable with other European health information systems. We propose here an alternative identification procedure that would allow France to broaden the scope of its PMR project by making it possible to contribute to public health research and policy while increasing interoperability with European health information systems and preserving the confidentiality of the data.

Objective: After a review of the existing practical solution available to the citizen to retrieve eHealth document, the paper describes an original specialized search engine WRAPIN., Method: WRAPIN uses advanced cross lingual information retrieval technologies to check information quality by synthesizing medical concepts, conclusions and references contained in the health literature, to identify accurate, relevant sources. Thanks to MeSH terminology [1] (Medical Subject Headings from the U.S. National Library of Medicine) and advanced approaches such as conclusion extraction from structured document, reformulation of the query, WRAPIN offers to the user a privileged access to navigate through multilingual documents without language or medical prerequisites., Results: The results of an evaluation conducted on the WRAPIN prototype show that results of the WRAPIN search engine are perceived as informative 65% (59% for a general-purpose search engine), reliable and trustworthy 72% (41% for the other engine) by users. But it leaves room for improvement such as the increase of database coverage, the explanation of the original functionalities and an audience adaptability., Conclusion: Thanks to evaluation outcomes, WRAPIN is now in exploitation on the HON web site (http://www.healthonnet.org), free of charge. Intended to the citizen it is a good alternative to general-purpose search engines when the user looks up trustworthy health and medical information or wants to check automatically a doubtful content of a Web page.