Normalizing Spontaneous Reports into MedDRA: some Experiments with MagiCoder

Text normalization into medical dictionaries is useful to support clinical task. 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 on 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 social media

From narrative descriptions to MedDRA: automagically encoding adverse drug reactions

The collection of narrative spontaneous reports is an irreplaceable source for the prompt detection of suspected adverse drug reactions (ADRs). In such task qualified domain experts manually revise a huge amount of narrative descriptions and then encode texts according to MedDRA standard terminology. The manual annotation of narrative documents with medical terminology is a subtle and expensive task, since the number of reports is growing up day-by-day. Natural Language Processing (NLP) applications can support the work of people responsible for pharmacovigilance. Our objective is to develop NLP algorithms and tools for the detection of ADR clinical terminology. Efficient applications can concretely improve the quality of the experts\u2019 revisions. NLP software can quickly analyze narrative texts and offer an encoding (i.e., a list of MedDRA terms) that the expert has to revise and validate. MagiCoder, an NLP algorithm, is proposed for the automatic encoding of free-text descriptions into MedDRA terms. MagiCoder procedure is efficient in terms of computational complexity. We tested MagiCoder through several experiments. In the first one, we tested it on a large dataset of about 4500 manually revised reports, by performing an automated comparison between human and MagiCoder encoding. Moreover, we tested MagiCoder on a set of about 1800 reports, manually revised ex novo by some experts of the domain, who also compared automatic solutions with the gold reference standard. We also provide two initial experiments with reports written in English, giving a first evidence of the robustness of MagiCoder w.r.t. the change of the language. For the current base version of MagiCoder, we measured an average recall and precision of and , respectively. From a practical point of view, MagiCoder reduces the time required for encoding ADR reports. Pharmacologists have only to review and validate the MedDRA terms proposed by the application, instead of choosing the right terms among the 70\u202fK low level terms of MedDRA. Such improvement in the efficiency of pharmacologists\u2019 work has a relevant impact also on the quality of the subsequent data analysis. We developed MagiCoder for the Italian pharmacovigilance language. However, our proposal is based on a general approach, not depending on the considered language nor the term dictionary

A Co-occurrence Based MedDRA Terminology Generation: Some Preliminary Results

The generation of medical terminologies is an important activity. A flexible and structured terminology both helps professionals in everyday manual classification of clinical texts and is crucial to build knowledge bases for encoding tools implementing software to support medical tasks. For these reasons, it would be nice to "enforce" medical dictionaries such as MedDRA with sets of locutions semantically related to official terms. Unfortunately, the manual generation of medical terminologies is time consuming. Even if the human validation is an irreplaceable step, a significative set of "high-quality" candidate terminologies can be automatically generated from clinical documents by statistical methods for linguistic. In this paper we adapt and use a co-occurrence based technique to generate new MedDRA locutions, starting from some large sets of narrative documents about adverse drug reactions. We describe here the methodology we designed and results of some first experiments

Pedagogia delle immagini e odontoiatria nei bambini con Disturbi Pervasivi dello Sviluppo

Scopo dello studio: è stato quello di verificare l’utilità del metodo educativo “per immagini” TEACH (Treatment and Education of Autistic and related Communication handicapped Children) in soggetti con disturbi pervasivi dello sviluppo (DPS), per facilitare il loro primo avvicinamento allo studio odontoiatrico. Materiali e metodi: sono stati scelti 2 gruppi: test e controllo. Entrambi i gruppi comprendono 11 pazienti con DPS tra i 5 ed i 14 anni con scarsa collaborazione alla prima visita. Il gruppo test è stato sottoposto al metodo TEACH. E’ stato quindi preparato per il gruppo test uno speciale album fotografico contenente le fotografie degli operatori e dell’ambulatorio odontoiatrico e una semplice descrizione delle stesse immagini. Una copia di tale album è stata inoltre consegnata ai genitori e/o ai riabilitatori almeno 15 giorni prima dell’appuntamento stabilito. Il gruppo controllo è stato sottoposto invece ad un normale approccio odontoiatrico. Risultati: Il metodo educativo TEACH ci ha permesso di poter far affrontare con serenità ad 8 dei nostri pazienti appartenenti al gruppo test la visita odontoiatrica e l’esecuzione di metodiche atte alla prevenzione. In particolare il livello di collaborazione è stato ottimo per 3 degli 8 pazienti e buono per gli altri 5. Per 3 pazienti la collaborazione si è dimostrata, nonostante il tipo di approccio, scarsa e comunque insufficiente per le cure odontoiatriche ambulatoriali. Nel gruppo di controllo la collaborazione si è dimostrata scarsa ed insufficiente per le cure ambulatoriali in 7 casi su 11.Conclusioni: L’approccio alle cure odontoiatriche con il metodo TEACH ha permesso l’accesso all’ambulatorio odontoiatrico per la visita e le cure preventive al 72% di un gruppo di pazienti con DPS, che erano risultati in precedenza non collaboranti. Invece, nel gruppo di controllo i pazienti selezionati per le cure ambulatoriali sono risultati essere solo il 36%. A nostro parere, il metodo di approccio proposto risulta efficace nel ridurre l’”ansia da esposizione e da rottura degli schemi” in pazienti con DP