Conceptualisation and Annotation of Drug Nonadherence Information for Knowledge Extraction from Patient-Generated Texts

Approaches to knowledge extraction (KE) in the health domain often start by annotating text to indicate the knowledge to be extracted, and then use the annotated text to train systems to perform the KE. This may work for annotating named entities or other contiguous noun phrases (drugs, some drug effects), but be- comes increasingly difficult when items tend to be expressed across multiple, possibly non- contiguous, syntactic constituents (e.g. most descriptions of drug effects in user-generated text). Other issues include that it is not al- ways clear how annotations map to actionable insights, or how they scale up to, or can form part of, more complex KE tasks. This paper reports our efforts in developing an approach to extracting knowledge about drug nonadherence from health forums which led us to conclude that development cannot proceed in separate steps but that all aspects—from conceptualisation to annotation scheme development, annotation, KE system training and knowledge graph instantiation—are interdependent and need to be co-developed. Our aim in this paper is two-fold: we describe a generally applicable framework for developing a KE approach, and present a specific KE approach, developed with the framework, for the task of gathering information about antidepressant drug nonadherence. We report the conceptualisation, the annotation scheme, the annotated corpus, and an analysis of annotated texts

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

Automated data analysis of unstructured grey literature in health research: A mapping review

\ua9 2023 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd. The amount of grey literature and ‘softer’ intelligence from social media or websites is vast. Given the long lead-times of producing high-quality peer-reviewed health information, this is causing a demand for new ways to provide prompt input for secondary research. To our knowledge, this is the first review of automated data extraction methods or tools for health-related grey literature and soft data, with a focus on (semi)automating horizon scans, health technology assessments (HTA), evidence maps, or other literature reviews. We searched six databases to cover both health- and computer-science literature. After deduplication, 10% of the search results were screened by two reviewers, the remainder was single-screened up to an estimated 95% sensitivity; screening was stopped early after screening an additional 1000 results with no new includes. All full texts were retrieved, screened, and extracted by a single reviewer and 10% were checked in duplicate. We included 84 papers covering automation for health-related social media, internet fora, news, patents, government agencies and charities, or trial registers. From each paper, we extracted data about important functionalities for users of the tool or method; information about the level of support and reliability; and about practical challenges and research gaps. Poor availability of code, data, and usable tools leads to low transparency regarding performance and duplication of work. Financial implications, scalability, integration into downstream workflows, and meaningful evaluations should be carefully planned before starting to develop a tool, given the vast amounts of data and opportunities those tools offer to expedite research

Analysis of Twitter data for postmarketing surveillance in pharmacovigilance

Postmarketing surveillance (PMS) has the vital aim to monitor effects of drugs af- ter release for use by the general pop- ulation, but suffers from under-reporting and limited coverage. Automatic meth- ods for detecting drug effect reports, es- pecially for social media, could vastly in- crease the scope of PMS. Very few auto- matic PMS methods are currently avail- able, in particular for the messy text types encountered on Twitter. In this paper we describe first results for developing PMS methods specifically for tweets. We de- scribe the corpus of 125,669 tweets we have created and annotated to train and test the tools. We find that generic tools per- form well for tweet-level language iden- tification and tweet-level sentiment anal- ysis (both 0.94 F1-Score). For detection of effect mentions we are able to achieve 0.87 F1-Score, while effect-level adverse- vs.-beneficial analysis proves harder with an F1-Score of 0.64. Among other things, our results indicate that MetaMap seman- tic types provide a very promising ba- sis for identifying drug effect mentions in tweets

“Gear is the Next Weed”:A Qualitative Exploration of the Beliefs, Attitudes and Behaviours of Performance and Image Enhancing Drug Using Subcultures in the South-West of England

Performance-enhancing drugs until relatively recently have been seen to be the preserve of sport-focussed athletes, but in recent years there has been an apparent increase in use amongst the general population, with individuals now using PIEDs not only to increase athletic prowess, but for image-conscious reasons entirely divorced from such ‘competitive’ notions. This research explores the different types of PIED user training in gym environments today, identifying differences in ‘ethnopharmacologies’ between these groups, allowing them to be categorised by their beliefs, attitudes, and patterns of use, based on qualitative data gathered ‘in the field’ from a total of 27 respondents, including 14 in-depth interviews. This exploration further evidences the extent to which a ‘normalisation’ of PIED use is occurring.Results suggest PIED users can be split into three categories, ‘sport-oriented’, ‘image-oriented’ and ‘hedonic’, with sport-oriented users conducting the most research, and having the most rigid cultural ‘disciplines’, and ‘hedonic’ users the least. This is evidenced through exploration of participants’ ‘decision to begin using’, their processes of ‘learning to use’ and their ‘longer term use’ of PIEDs, all of which suggest that patterns of use exist on a spectrum, from informed and cautious use employed by the most serious sport-focussed PIED users, to high-risk, high time-preference use associated with ‘hedonic’ users. This divergence in ethnopharmacologies and behaviours between groups evidences the need for such a categorisation of users in future research and policy, particularly for harm-minimisation purposes, as well as offering in-depth qualitative contributions to findings reported in recent longitudinal studies. Further, these elements of use evidence an increasing normalisation of PIEDs, which appears to have been largely achieved, excepting a perception of ‘stigmatisation’ still faced by users, principally stemming from media portrayals of ‘roid rage’. This limitation to cultural acceptance is therefore addressed, with evidence suggesting ‘roid rage’ is a ‘myth’, and further that this stigmatisation is likely to decline as knowledge is transferred from using populations to their non-using peers, indicating ‘normalisation’ is occurring.<br/