dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Risky behavioural characteristics are strongly associated with the likelihood of injury, a leading cause of death in adolescence. We investigated whether early behavioural and emotional difficulties, measured using the strengths and difficulties questionnaire (SDQ), predict the risk of injury in early adolescence. We used data from Welsh Millennium Cohort Study members, linked to their health records, to examine this question. Method There were 1683 (871 boys and 812 girls) and 1730 (895 boys and 835 girls) children at age 3 and 5, respectively, with parent reported SDQ scores, who were linked, with consent, to injury history during early adolescence (11 to < 14 years), within the Secure Anonymised Information Linkage Databank at Swansea University. First injury, recorded as primary diagnosis in hospital or emergency records, during early adolescence was considered in the study.
Emotional and behavioural difficulties were estimated from individual SDQ component scores (conduct disorder (CD), hyperactivity, emotional and peer relationship problems and prosocial behaviours) and 'total difficulty' scores (TDSs) (categorised as 'normal, 'borderline' and 'high'). Data were analysed using non-adjusted and adjusted (family factors, socio-economic level and gender) Cox proportional hazard models. results High hyperactivity scores were more common at age 3 than age 5 and more prevalent in boys than girls at both ages: the prevalence of high scores at ages 3 and 5, respectively, was 16. 3% (10. 2% boys, 6. 1% girls) and 11. 2% (7. 12% boys and 4% girls). Similarly, high conduct disorder scores were observed in 35. 1% of three year olds (19. 3% boys, 15. 8% girls) and 10. 6% of five year olds (7.
Method The SMASH intervention was rolled out in 11 general practices in Salford, UK. The dashboard interrogates electronic health records using a set of 13 medication safety indicators and presents the resulting information to its users in both aggregated form and as lists of individual patients with potential safety hazards. Clinical pharmacists were aligned to participating practices to assist practice staff in resolving safety hazards identified by the dashboard for a period of 12 weeks (intervention period) and were free to continue using the dashboard (follow-up period). We analysed the database of identified safety hazards and log files of user interactions with the dashboard during the first 6 months of its deployment. results Eleven general practices had used SMASH for a mean period of 17 weeks (range 4 to 25) at the time of analysis.
Introduction Stroke survivors are at a high risk of a recurrent stroke, which is likely to be more disabling and fatal than first time strokes. Secondary prevention requires health professionals to offer interventions to monitor and manage risk factors (e. g. blood pressure and antithrombotic treatment) and patients to change health-related behaviours, such as smoking and diet, and adhere to preventative medications. Currently, vascular risk factors tend to be neither well managed nor controlled. In this study, we engaged stakeholders to iteratively design a decision aid informed by an integrated clinical and research dataset, aiming to facilitate shared decision making (SDM) on effective treatments for secondary stroke prevention and motivate the patient to adhere to the selected treatments thereby reducing the risk of recurrence.
results and discussion We demonstrated the methodology on 589,169 older adults from the Clinical Practice Research Datalink. We successfully identified six distinct subgroups of falls from the elderly population who are identified with different risks. Some of the associations found are well defined in the literature, for example depression and musculoskeletal conditions are significantly associated with falls. However, a number of associations are not reported in the clinical literature. Such hypotheses need further exploration by epidemiologists. Introduction Mappings between codes in different medical terminologies are seen as a part of medical data analysis among ICD10, SNOMED CT and READ clinical terminology. Looking up terminologies from Unified Medical Language System Terminology Service (UTS) is one of the ways of achieving this.
However, the service provided by the US National Library is too general for users to be able to filter out unwanted information. Another route is to use the NCBO BioPortal, via an online ontology browsing facility. Terms collected in each ontology have been organised in a structured tree for a better visualisation. However, multiple ontologies cannot be used simultaneously, and there is a need to facilitate this task for medical data researchers who do this on a regular basis. Introduction Learning health systems (LHSs) rely on routine extraction, aggregation and transformation of medical data from a variety of sources into actionable clinical knowledge. Diagnostic decision support systems (DDSSs) are tools that are suitable for delivery using the LHS model; however, their acceptance has been hampered by perceived usability problems that hinder clinicians' workflow.
The sequence of interactions comprises three phases. Initialisation and data extraction: The diagnostic consultation starts by extracting patient EHR data, while the DSI captures the main presentation reason. All data are then passed to the ES as a diagnostic question. The ES response is an initial ranked list of diagnoses to consider, each accompanied by a list of cues and examinations pertinent to each diagnosis. Data capture: Further diagnostic cues are captured in a structured manner by the DSI. In each iteration, every newly captured cue is sent to the DSM and the ES to obtain an updated ranked differential diagnosis list for display to the clinician, followed by an optional capture of a working diagnosis. Data storage: The final step is to write back the captured diagnostic cue data into the patient record using EHR compatible format.
results The resulting interaction protocol has been successfully implemented in a prototype DDSS, supporting both data extraction and recording to the EHR. The tool, developed as part of the TRANSFoRm project, has been integrated with Vision v3, a leading UK EHR system for general practice. The interaction with Vision is through a specialised API that requires XML formatting of data. Data extracted involves risk factors, lifestyle activities and demographics. The communication with the ES uses rest service calls and XML format for data exchange. The usability evaluation of this prototype has shown that the clinical decision-making has improved by 8%. Discussion and conclusion This work presents a step towards the standardisation of the integration between decision support tools and electronic health record systems.
Introduction The creation of sets of clinical codes for querying routine healthcare databases is an important part of using such data for research. It is usually an early step and a hazardous one, where missing or wrongly specified codes result in selection biases that propagate throughout subsequent analyses. Reviews have shown that code sets are rarely reported in publications, let alone their construction process. This process is the key, as the explicit methodology could be peer reviewed, and reused by others to improve their research. We review methods for managing (constructing, sharing, revising and reusing) clinical code sets reported in the literature and develop recommendations. Method A PubMed literature search was performed to exhaustively search for methodological papers on code set management published until August 2016.
We therefore sought to examine the relationship between SSRI use and mortality in patients with epilepsy using electronic health records (EHRs). Methods A published case definition for epilepsy was used to extract a cohort of patients from the CALIBER resource (which contains national linked structured EHR data from primary care, hospital care and a cause-specific mortality registry) between 1 January 1997 and 31 March 2010. We selected only those patients with active epilepsy defined by the failure to achieve 12-month seizure freedom over the duration of follow-up. The primary outcome was all-cause mortality treating SSRI use as a time varying covariate in a Cox proportional hazard regression model. Patients were considered exposed following their second SSRI prescription. We also evaluated the temporal association between SSRI use and mortality by dividing follow-up into 6-and 12-month epochs.
We have built a graph model of drug knowledge (protein targets and known side effects), developed a graph-based computational method to predict unknown ADRs and tested these predictions in electronic health records (EHRs). Method Drug targets were retrieved from DrugBank, known side effects from SIDER and protein-protein interactions from IntAct. These databases were parsed into a graph database in Neo4j. Based on this graph, a model is built and optimised to correctly classify the known causes of each ADR. The known causes of each ADR are compared to all other drugs in the network to identify predictive neighbours. The score for a drug is the sum of its adjacency with these predictors, multiplied by weight for each predictor type (target or ADR). Drugs with a final score above a threshold are predicted to cause the ADR, i.
e. each model is a binary classifier used to place edges in the network. These weights and the threshold are adjusted iteratively to optimise the performance of the model for each ADR. To validate the new predictions from the ADR models, an NLP pipeline was used to recover drug-ADR associations from the anonymised EHR data available through the Clinical Record Interactive Search in South London and Maudsley NHS Trust. results Predictive models were built for 311 ADRs. Based on leave-one-out cross validation, 78% of drug-ADR edges were correctly predicted by the best models. Ten different ADRs that can occur within weeks of beginning drug treatment were selected for validation, covering a range of severity and frequency, e. g. Stevens-Johnson Syndrome and hypersalivation. All ADR models were enriched for positive associations compared to chance, with 10/10 significantly enriched (hypergeometric test p < 0.
Method The study site is a large University teaching hospital in London. Data were gathered from a wide range of sources across the hospital and include data on patient spells, length of stay, diagnoses, treatments, equipment availability, adverse incidents, patient complaints, patient experience, staff experience, trust bed availability, staffing levels, locum cover, staff sickness and escalations. These data presented challenges due to their differing formats and timescales and their interpretation and reliability. Exploratory workshops were held with key informants to assist in interpretation. Extensive work was required to clean and transform the data in preparation for analysis. A longitudinal time series database was built and analysed using appropriate statistical techniques. Introduction Potentially preventable hospitalisations (PPHs) have drawn increased international attention in recent years.
conclusion To our knowledge, this study is the first to focus on developing validated prediction models to identify geographic areas expected to have persistently high rates of PPHs in long-term future time periods. Our models performed well when applied to multiple exemplar PPHs. We suggest that these methods can assist in the development of appropriate non-hospital interventions targeting PPH-related health inequality. Introduction Obesity is a risk factor for many chronic health conditions, but is reportedly protective for osteoporosis and most osteoporosis-related fractures. While administrative health data have been used extensively for predicting risk of chronic conditions within populations, most predictive models lack information about obesity because it is infrequently coded in administrative data.
Methods We identified 36,372 individuals (50+ years) in a clinical registry database for bone mineral density (BMD) from the province of Manitoba, Canada, with body mass index (BMI) measured between 2001 and 2015. Measured obesity (MOB) was defined as BMI ≥ 30 kg/m 2. Linked administrative data were used to ascertain obesity and fracture diagnoses. Obesity was defined from administrative data as (a) at least one hospital or physician International Classification of Diseases (ICD) code within three years prior to the BMD test date (DOB1) or (b) at least one hospital or physician ICD code, laparoscopic surgery procedure code or appetite-supressing prescription medication within three years prior to the BMD test date (DOB2). Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident osteoporosis-related fractures (hip, forearm, clinical spine and humerus fracture) associated with MOB, DOB1, and DOB2 before and after adjusting for confounding covariates (e.
Introduction In healthcare structures, operational applications routinely record huge volumes of data and allow users to exploit them in the primary objective for which they were developed (i. e. medico-legal aspect and administrative purpose). As these operational applications have now been implemented for several years, they may provide significant volumes of data of interest for research for example. Initiatives have been undertaken to reuse these data, it faces various difficulties, e. g. operational databases contain errors due to input error or poor documentation from users and monitoring artefacts from monitors. Moreover, the way data are stored is not optimized to make the reuse of data easier. In order to improve the data quality, it is necessary to identify precisely the difficulties faced and provide adapted recommendations to fix them.
The first step is to define an illustrated taxonomy of data quality problems. This presentation deals with the design of this taxonomy and its usefulness to assess the overall quality of data recorded within an anaesthesia database. Method An exhaustive list of data quality problems was identified from existing published works in scientific literature or based on our experience in designing data warehouses. These items were then ordered in a taxonomy so that it was as operational as possible for easily identifying data quality issues. Finally, this taxonomy was used to assess the quality of data recorded between 2010 and 2016 in the Anaesthesia Information Management System implemented in Lille University Hospital. results We identified 100 items of data quality problems from eight papers.
After selection of relevant items and deduplication, we added new types of issues not yet reported that we met during our previous experiences (n = 6), leading a total of 50 different data quality problems. Those items were classified in the taxonomy according to the levels of granularity of the databases they are related to single field of a single record, single field of multiple records, multiple records, etc. Based on this taxonomy, the quality of data recorded for 388 026 interventions was assessed. The main problems were 'imprecise values' (e. g. values outside normal ranges) and 'missing data' (data not documented or not linked between systems). We noticed that Introduction Existing data linkage projects in Wales have primarily focused on health datasets. To build the complete picture of care service provision, there is a need to broaden the linked data available to included health, social service provision by local authorities and provision of support by third sector organisations.
The aims of this pilot project are to (i) test the feasibility of linking datasets from a local authority, the NHS and third sector organisations, (ii) build a more complete picture of service provision using adults who have been referred to social services in order to avoid admission to hospital or to facilitate their discharge from hospital and (iii) assess the range and quality of data available in each of the organisations providing services to those individuals. Method Bangor University led a research team partnered with Gwynedd Local Authority to explore the governance issues and practicalities of providing an anonymised dataset to the SAIL databank (Swansea University). Two third sector agencies were also approached. With the various required service level agreements in place, data were put through the SAIL process for analysis.
results Details of 20,373 referrals generated by 12,228 social services clients and 21,220 service delivery records provided to 6,278 clients of Gwynedd Local Authority from the period 2008 to 2015 were transferred into the SAIL databank. The personal data relating to people known to Social Services were anonymised through the NHS Information Service from a table of 24,431 Gwynedd records. Removing duplication, 17,431 (96%) individuals were linked and 744 (4%) remain unmatched. Examination of the referral and service records related to the unmatched clients revealed no pattern or bias in the data related to these individuals. Of the 12,802 clients having a referral, service or both, we were able to link 84% to GP recorded events 94% to hospital day case or in-patient spell 71% to accident and emergency attendance and 95% to outpatient activity.
Methods The Avon Longitudinal Study of Parents and Children (ALSPAC) has been linked to the National Pupil Database (NPD), a repository of educational data for pupils in England. The NPD has been linked to the Department for Education's CLA data return (since 2006) and CIN census data (since 2009). ALSPAC participants (n ~12,000) were coded as being CLA during school years 10 and 11 (aged approximately 14-16 years) if they had a CLA record while in Year 10 or Year 11 and as being CIN if they had a CIN record in 2009 that stated they had been registered as in need before the age of 16 years. A 3-category exposure variable was derived: 'not CIN/CLA', 'CIN (not CLA)', and 'CLA'. GCSE exams are taken at the end of Year 11. Two measures of attainment were examined: capped GCSE percentage score (summed score of eight best subjects, converted into a percentage of maximum possible score) and attainment of five GCSEs at grades A*-C including English and Maths.
Discussion and conclusions Although CLA and CIN statuses were strongly socially patterned, the low educational attainment of these individuals relative to their peers was not explained by the maternal and socio-economic position factors considered. Future work will also examine the influence of school-level factors on the academic achievement of adolescents who are CIN or CLA. Introduction Routinely collected datasets including electronic health records are increasing widely used in healthcare research. Among other advantages, they allow research to be done on a much wider scale, using hundreds of thousands of patient records to answer questions about what is happening in the 'real world'. Although routine datasets provide exciting opportunities for research, their size and variability can make them difficult to analyse and might discourage some researchers.
Methods We fit mixed effects regression models to outcome data, including predictors of interest and confounding factors as covariates. To allow for variation in outcome among general practices, we include random intercepts. We implement the analysis using weighted regression or meta-analysis of estimated regression coefficients from each practice. These methods are likely to be familiar to applied healthcare researchers, though not for the purpose of analysing large datasets. Both methods reduce the size of the dataset, thus decreasing the amount of time required for statistical analysis. We compare the methods to an existing subsampling approach. results All methods yield similar effect estimates. Weighted regression and meta-analysis give similar precision to analysis of the entire dataset, but subsampling is substantially less precise.
We compare the methods through application to two contrasting primary care datasets from the Clinical Practice Research Datalink (CPRD). For example, in a dataset comprising 2,116,948 observations recorded by 674 general practices between 1996 and 2014, we investigate the impact of financial incentives in UK primary care on the recording of elevated cholesterol in patients with severe mental illness. In conventional mixed effects logistic regression of the entire dataset, the estimated immediate effect of an incentive introduced in 2011 has odds ratio 1. 80 (95% CI: 1. 70 to 1. 92), showing increased recording of cholesterol. Weighted regression reduces the size of the dataset by 98. 5% and gives identical results. Meta-analysis and analysis of representative 10% of the dataset give similar estimates of 1.
67 (95% CI: 1. 54 to 1. 80) and 1. 63 (95% CI: 1. 32 to 2. 01), respectively. Discussion Fitting mixed effects regression models that allow for multiple sources of variation have become relatively straightforward in standard statistical software. However, it is time consuming and not very practical to fit such models to data comprising hundreds of thousands of observations nested within hundreds of general practices. An existing subsampling approach allows us to extract the data required to answer the research question and has performed well in example applications, but it may be preferable to make use of all the data. Where all data are discrete, weighted regression is equivalent to fitting the model to the entire dataset. In the presence of a continuous covariate, meta-analysis is useful.
conclusion We have identified scalable statistical methods for analysing large routine clinical datasets. These methods would be accessible to applied researchers and are easily implemented using standard statistical software. Introduction Given an ageing population, the importance of patient frailty in clinical decision making is increasingly recognised for both treatment and care. The electronic Frailty Index (eFI) is a tool developed at the University of Leeds and implemented in GP systems that is based on a cumulative deficit model of frailty, consisting of 36 'deficits' constructed using around 1,500 clinical codes from a patient's primary care record. There is much debate on whether frailty is unidimensional or multidimensional. Unidimensional models, like the eFI, generate a single frailty score.
Multidimensional models produce scores on frailty sub-domains, such as physical, cognitive and psychosocial. The aims of this study are to identify the multidimensional models of frailty that best fit actual patient data on health deficits and then use these models to generate frailty profiles for individual patients and explore subdomains. The work uses two large UK primary care databases of electronic health records, the Clinical Practice Research Datalink (CPRD) and ResearchOne, and is funded by the School for Primary Care Research.
Different models may be suited to different purposes, e. g. a many-dimensional model to aid clinical decision making, and a broader model with fewer dimensions for epidemiological work. External validity will be assessed by applying the model(s) to equivalent samples of patients from the ResearchOne database. Predictive validity in relation to mortality will be assessed and compared in performance to the original eFI. Multidimensional frailty score profiles for individual patients will be generated and patients clustered according to their profiles to identify homogenous subgroups.