Normalizing Spontaneous Reports Into MedDRA: Some Experiments With <inline-formula> <tex-math notation="LaTeX">$\mathsf{MagiCoder}$</tex-math> </inline-formula>

Text normalization into medical dictionaries is useful to support clinical tasks. A typical setting is pharmacovigilance (PV). The manual detection of suspected adverse drug reactions (ADRs) in narrative reports is time consuming and natural language processing (NLP) provides a concrete help to PV experts. In this paper, we carry out experiments for testing performances of MagiCoder, an NLP application designed to extract MedDRA terms from narrative clinical text. Given a narrative description, MagiCoder proposes an automatic encoding. The pharmacologist reviews, (possibly) corrects, and then, validates the solution. This drastically reduces the time needed for the validation of reports with respect to a completely manual encoding. In previous work, we mainly tested MagiCoder performances on Italian written spontaneous reports. In this paper, we include some new features, change the experiment design, and carry on more tests about MagiCoder. Moreover, we do a change of language, moving to English documents. In particular, we tested MagiCoder on the CADEC dataset, a corpus of manually annotated posts about ADRs collected from the social media.

Authors

• Carlo Combi University of Verona
• Margherita Zorzi
• Gabriele Pozzani
• Elena Arzenton
• Ugo Moretti

Keywords

• Dictionaries
• Drugs
• Encoding
• Software
• Informatics
• Medical diagnostic imaging
• Task analysis
• Adverse Events
• Social Media
• Language Change
• Medical Dictionary
• Number Of Reports
• Precision And Recall
• Domain Experts
• Hash Function
• Human Experts
• Medical Terms
• Conditional Random Field
• English Text
• Italian Language
• Basic Version
• Anaphylactic Shock
• Unified Medical Language System
• Machine Learning-based Approaches
• Automatic Solution
• Natural language processing
• healthcare informatics
• pharmacovigilance
• adverse drug reactions
• term identification
• Data Mining
• Drug-Related Side Effects and Adverse Reactions
• Humans
• Medical Informatics