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

Protocol: Are primary care consultations for insomnia associated with dementia in later life?

Insomnia has been defined as a difficulty initiating or maintaining sleep, leading to sleep that is either insufficient or unrefreshing. It is well-established that insomnia is more common in people with dementia, but it is not clear if insomnia predates dementia in these individuals. This latter question is an important one: if it can be shown that people with insomnia are more likely to develop dementia in later life, this may improve our ability to predict an individual’s dementia risk, and possibly to help manage that risk. Several recent studies have found a link between insomnia and later dementia, but typically give little information about the time between the onset of insomnia and the onset of dementia, raising the possibility that the insomnia is an early symptom of dementia, rather than a risk factor or potential cause of the disease. Furthermore, in some studies the link between insomnia and dementia becomes weaker when factors such as depression and sleeping tablet use are taken into account. The proposed study uses primary care records to learn whether people with dementia are more likely to have consulted with their general practitioner (GP) about insomnia 5-10 years earlier, compared to those who do not have dementia

A Machine Learning Framework for Predicting Dementia and Mild Cognitive Impairment

Dementia is one of the most feared illnesses that has a growing year-to-year negative global impact, having a health and social care cost higher than cancer, stroke and chronic heart disease, taken together. Without the availability of a cure, nor a standardised clinical test, the utilisation of machine learning methods to identify individuals that are at risk of developing dementia could bring a new step towards proactive intervention. This study’s goal is to carry out a precursor analysis leading to building classification models with enhanced capabilities for differentiating diagnoses of CN (Cognitively Normal), MCI (Mild Cognitive Impairment) and Dementia. The predictive modelling approach we propose is based on the ReliefF method combined with statistical permutation tests for feature selection, and on model training, tuning, and testing based on algorithms such as Random Forests, Support Vector Machines, Gaussian Processes, Stochastic Gradient Boosting, and eXtreme Gradient Boosting. Stability of model performances were studied in computationally intensive Monte Carlo simulations. The results consistently show that our models accurately detect dementia, and also mild cognitive impairment patients by only using the inclusion of baseline measurements as predictors, thus illustrating the importance of baseline measurements. The best results issued from Monte Carlo were achieved by eXtreme Gradient Boosting optimised models, with an accuracy of 0.88 (SD 0.02), a sensitivity of 0.93 (SD 0.02) and a specificity of 0.94 (SD 0.01) for dementia, and a sensitivity of 0.86 (SD 0.02) and a specificity of 0.9 (SD 0.02) for mild cognitive impairment. These results support in particular future developments for a risk-based method that can identify an individual’s risk of developing dementia

Are symptoms of insomnia in primary care associated with subsequent onset of dementia? A matched retrospective case-control study

Objective: There is evidence from neuroimaging studies of an association between insomnia and early dementia biomarkers, but observational studies have so far failed to show a clear association between insomnia and the later development of dementia. We investigated the association between dementia diagnosis and recording of insomnia symptoms 5-10 years earlier in primary care. Method: A case-control study using data from the Clinical Practice Research Datalink. 15,209 cases with dementia (either Alzheimer's, vascular, mixed or non-specific subtypes) at least 65 years old at time of diagnosis, were matched with the same number of controls on year of birth and gender. We ascertained the presence of insomnia symptoms during a five-year period starting 10 years before the index date. Odds ratios for developing dementia were estimated using logistic regression after controlling for hypnotic exposure and physical and mental health comorbidities. Results: The adjusted odds ratio for dementia in those with previous insomnia was 1.34 (95% CI = 1.20-1.50). Conclusion: There is an association between dementia and previous insomnia. It may be possible to incorporate insomnia into predictive tools for dementia

The legacies of coercion and the challenges of contingency: Mozambican unions in difficult times

Although insecure work may be found everywhere, the general lack of secure work in emerging economies is a particularly striking feature of the contemporary condition, undermining the continued viability of the labour movement in such countries. Yet, this topic is rarely tackled directly in African studies or business history journals. The two key questions addressed in this paper are, first, to what extent does the labour movement’s past define their present and future, and second, what are the challenges and opportunities affecting their ability to mobilise workers, influence government and effectively tackle employment security? This article details how in Mozambique, unions’ ability to mobilise has been affected by: the post-colonial, post-conflict and post-socialist historical context; the resulting legacies of regional and racial discrimination; international imperatives for liberalisation and privatisation; challenging relationships with the country’s African neighbours; and high levels of informal sector work. In order to remain viable, key imperatives include: effectively influencing national government, engaging internationally and working with organisations representing informal sector workers

Data retrieval system

A browsing process is driven by user interaction. The user is presented with a selection of items 511, 512, ... etc from a range. The user can give inputs representing interest in one or other of the items displayed. The inputs represent rewards, which are distributed to attribute data items or 'keywords' associated with the display items. These keywords are transparent to the user, but represent characteristics of the display items with which they are associated. The browsing system selects items for display according to a probabilistic function weighted to favour those having the keywords which have accrued the highest number of rewards, these being the display items having the most keywords in common with display items previously rewarded by the user during the browsing session. Partial or negative weightings may also be applied to the keyword associations, which are also taken into account in the selection process

A Cross-Sectional Randomised Study of Fracture Risk in People with HIV Infection in the Probono 1 Study

OBJECTIVE:To determine comparative fracture risk in HIV patients compared with uninfected controls. DESIGN:A randomised cross-sectional study assessing bone mineral density (BMD), fracture history and risk factors in the 2 groups. SETTING:Hospital Outpatients. SUBBBJECTS:222 HIV infected patients and an equal number of age-matched controls. ASSESSMENTS:Fracture risk factors were assessed and biochemical, endocrine and bone markers measured. BMD was assessed at hip and spine. 10-year fracture probability (FRAX) and remaining lifetime fracture probability (RFLP) were calculated. MAIN OUTCOME MEASURES:BMD, and history of fractures. RESULTS:Reported fractures occurred more frequently in HIV than controls, (45 vs. 16; 20.3 vs. 7%; OR=3.27; p=0.0001), and unsurprisingly in this age range, non-fragility fractures in men substantially contributed to this increase. Osteoporosis was more prevalent in patients with HIV (17.6% vs. 3.6%, p<0.0001). BMD was most reduced, and predicted fracture rates most increased, at the spine. Low BMD was associated with antiretroviral therapy (ART), low body mass index and PTH. 10-year FRAX risk was <5% for all groups. RLFP was greater in patients with HIV (OR=1.22; p=0.003) and increased with ART (2.4 vs. 1.50; OR= 1.50; p=0.03). CONCLUSIONS:The increased fracture rate in HIV patients in our relatively youthful population is partly driven by fractures, including non-fragility fractures, in men. Nonetheless, these findings may herald a rise in osteoporotic fractures in HIV patients. An appropriate screening and management response is required to assess these risks and identify associated lifestyle factors that are also associated with other conditions such as cardiovascular disease and diabetes