Web-based and machine learning approaches for identification of patient-reported outcomes in inflammatory bowel disease.

Background: Messages from an Internet forum are raw material that emerges in a natural setting (i.e., non-induced by a research situation).

Syntax-based transfer learning for the task of biomedical relation extraction.

Background: Transfer learning aims at enhancing machine learning performance on a problem by reusing labeled data originally designed for a related, but distinct problem. In particular, domain adaptation consists for a specific task, in reusing training data developedfor the same task but a distinct domain. This is particularly relevant to the applications of deep learning in Natural Language Processing, because they usually require large annotated corpora that may not exist for the targeted domain, but exist for side domains.

Flare-IBD: development and validation of a questionnaire based on patients' messages on an internet forum for early detection of flare in inflammatory bowel disease: study protocol.

Introduction: Crohn's disease and ulcerative colitis, the two major forms of inflammatory bowel disease (IBD), are chronic disabling conditions characterised by flares followed by periods of remission. However, patients with IBD are seen every 3-6 months in the outpatient clinic, and the occurrence of a flare between two outpatient visits is not captured. To our knowledge, there is no validated patient-reported outcome (PRO) tool to measure the phenomenon of flare in IBD.

PGxCorpus, a manually annotated corpus for pharmacogenomics.

Pharmacogenomics (PGx) studies how individual gene variations impact drug response phenotypes, which makes PGx-related knowledge a key component towards precision medicine. A significant part of the state-of-the-art knowledge in PGx is accumulated in scientific publications, where it is hardly reusable by humans or software. Natural language processing techniques have been developed to guide experts who curate this amount of knowledge.

A qualitative approach to signal mining in pharmacovigilance using formal concept analysis.

"Pharmacovigilance is the process and science of monitoring the safety of medicines, consisting in (i) collecting and managing data on the safety of medicines (ii) looking at the data to detect 'signals' (any new or changing safety issue)" [1]. Pharmacovigilance is mainly based on spontaneous reports: when suspecting an adverse drug reaction, health care practitioners send a report to a spontaneous reporting system (SRS). This produces huge databases containing numerous reports and their manual exploration is both cost and time prohibitive.

Mining for adverse drug events with formal concept analysis.

The pharmacovigilance databases consist of several case reports involving drugs and adverse events (AEs). Some methods are applied consistently to highlight all signals, i.e.