ADEPt, a semantically-enriched pipeline for extracting adverse drug events from free-text electronic health records

Adverse drug events (ADEs) are unintended responses to medical treatment. They can greatly affect a patient's quality of life and present a substantial burden on healthcare. Although Electronic health records (EHRs) document a wealth of information relating to ADEs, they are frequently stored in the unstructured or semi-structured free-text narrative requiring Natural Language Processing (NLP) techniques to mine the relevant information. Here we present a rule-based ADE detection and classification pipeline built and tested on a large Psychiatric corpus comprising 264k patients using the de-identified EHRs of four UK-based psychiatric hospitals. The pipeline uses characteristics specific to Psychiatric EHRs to guide the annotation process, and distinguishes: a) the temporal value associated with the ADE mention (whether it is historical or present), b) the categorical value of the ADE (whether it is assertive, hypothetical, retrospective or a general discussion) and c) the implicit contextual value where the status of the ADE is deduced from surrounding indicators, rather than explicitly stated. We manually created the rulebase in collaboration with clinicians and pharmacists by studying ADE mentions in various types of clinical notes. We evaluated the open-source Adverse Drug Event annotation Pipeline (ADEPt) using 19 ADEs specific to antipsychotics and antidepressants medication. The ADEs chosen vary in severity, regularity and persistence. The average F-measure and accuracy achieved by our tool across all tested ADEs were 0.83 and 0.83 respectively. In addition to annotation power, the ADEPT pipeline presents an improvement to the state of the art context-discerning algorithm, ConText

The side effect profile of Clozapine in real world data of three large mental hospitals

Objective: Mining the data contained within Electronic Health Records (EHRs) can potentially generate a greater understanding of medication effects in the real world, complementing what we know from Randomised control trials (RCTs). We Propose a text mining approach to detect adverse events and medication episodes from the clinical text to enhance our understanding of adverse effects related to Clozapine, the most effective antipsychotic drug for the management of treatment-resistant schizophrenia, but underutilised due to concerns over its side effects. Material and Methods: We used data from de-identified EHRs of three mental health trusts in the UK (>50 million documents, over 500,000 patients, 2835 of which were prescribed Clozapine). We explored the prevalence of 33 adverse effects by age, gender, ethnicity, smoking status and admission type three months before and after the patients started Clozapine treatment. We compared the prevalence of adverse effects with those reported in the Side Effects Resource (SIDER) where possible. Results: Sedation, fatigue, agitation, dizziness, hypersalivation, weight gain, tachycardia, headache, constipation and confusion were amongst the highest recorded Clozapine adverse effect in the three months following the start of treatment. Higher percentages of all adverse effects were found in the first month of Clozapine therapy. Using a significance level of (p< 0.05) out chi-square tests show a significant association between most of the ADRs in smoking status and hospital admissions and some in gender and age groups. Further, the data was combined from three trusts, and chi-square tests were applied to estimate the average effect of ADRs in each monthly interval. Conclusion: A better understanding of how the drug works in the real world can complement clinical trials and precision medicine

Electronic records for dementia research: Do behavioural disturbances, antihypertensives and antidepressants influence decline trajectories?

Background: Understanding the drivers of heterogeneous progression in dementia has huge implications for recruitment to clinical trails and care planning. One way to investigate the factors contributing to this heterogeneity is to stratify subjects by similar patterns of change and compare the characteristics of the sub-populations identified. We applied this approach to routinely collected electronic health records from the South London and Maudesly NHS Foundation Trust (SLAM). Methods: This retrospective study includes 3441 patients with at least three MMSE scores recorded and available for research through the SLAM clinical record interactive search system (CRIS). A Latent Class Growth Analysis was used to identify key trajectories of decline. Information on age, gender, ethnicity, qualification levels, cohabiting status, retirement status, Health of Nation Outcome Scales (HoNOS) and medications were obtained to describe characteristics of the identified trajectory sub-populations in a multivariable multinomial regression analysis. Results: We identified six trajectories of cognitive decline. Four of these trajectories differed in initial MMSE score, and showed increased rate of decline with lower initial MMSE. Two trajectories had very similar initial MMSE scores but differed in the rate of decline. Exploring these trajectories further, the severity of cognitive problems at baseline and prescription of Donepezil and Amlodipine was significantly higher in the slower declining trajectory. In the faster declining trajectory, severity of behavioral problems and prescription of sertraline was significantly higher. Conclusions: Most of the trajectories differed by initial MMSE and thus likely represented patients at different disease stages, however differences in behavioral problems, antihypertensive, antidepressant and dementia medication prescription may be indicators of future rate of cognitive decline. Further information is required on depression and hypertensive comorbidities to explore these findings in greater detail

Lichenometric dating: a commentary, in the light of some recent statistical studies

This commentary article discusses the relative merits of new mathematical approaches to lichenometry. It highlights their strong reliance on complex statistics; their user un-friendliness; and their occasional mistreatment of existing lichenometric techniques. The article proposes that the success of lichenometric dating over the past 50 years has stemmed from its relative simplicity, transparency, and general field applicability. It concludes that any new techniques which ignore these principles are likely to be unjustified, unsuitable to the user community and inappropriate for the subject matter. Furthermore, the article raises a more general philosophical question: can statistical complexity and high precision in a ‘geobotanical’ dating technique, fraught with high degrees of environmental variability and in-built uncertainty, ever be scientifically valid

Value Chain of Technology in Higher Education Institutions: From IT Resources to Technological Performance

International audienceDifferent studies confirm that the presence of IT in firms, together with human and other organizational resources, has a positive influence on the performance of organizations. However, the details of the process through which that influence is produced have not been clarified. This study is based on an extensive IT data base corresponding to a sample of universities and presents an IT-technological performance value chain and confirms the hypotheses about its functioning. The result is a value chain that begins with the IT planning, passes through different components related to technology in organizations and ends with the performance of the technology. We believe that this research is useful to higher education institutions managers by allowing them to have a clear path on how to improve the return of IT investments