dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction The effect of the wider social environment on physical and emotional health is a longstanding area of study. The impact of the individual's social residence type, such as living alone, or living in a care setting, adds a layer of complexity to health and social research and policy. For example, infection transmission in student accommodation could lead to improved sanitation measures. Mental and physical health outcomes of chronic disease patients living alone compared with living in a community care setting could highlight the need to additional social care support. Surveys, including the UK Census, are collecting this data. However, these surveys are expensive, time consuming and usually cover a subsection of the population. Large-scale, linked databanks, such as the Secure Anonymous Information Linkage (SAIL) Databank at Swansea University, holding administrative datasets, including health data, are broader in scope, both in terms of the nature of the data held and the population.
Despite known data quality issues, a method for anonymously modelling household types on the population as a whole, using routinely collected administrative data, would be beneficial to linked data research. SAIL contains anonymous linked data on over 3 million Welsh residents. Address data is not stored, but postcodes are anonymised by a Trusted Third Party to create the residential anonymised linkage field (RALF). RALFs can be mapped to local super output area (LSOA) codes for geographical analysis. Using anonymised demographic and addressed data, individuals can be grouped into likely households. This study aims to classify these households into types for use for further research. Method Residences containing more than 10 individuals were extracted from administrative datasets. Age ranges and moving dates for each residence were calculated from the minimum and maximum age at which the residence members moved into that residence.
Features include the family-based recruitment breadth and depth of phenotype information, with detailed data on cognition, personality and mental health. Genome-wide association and exome genotype data are available on most of the cohort. These features maximise the power of the resource to identify, replicate or control for genetic factors associated with a wide spectrum of illnesses and risk factors. By linkage to routine NHS hospital, maternity, laboratory tests, prescribing and dental records, this has become a longitudinal dataset, using the Scottish Community Health Index (CHI). Mortality data can be obtained from the General Register of Scotland. results Researchers are now able to use the linked datasets to find prevalent and incidental disease cases and healthy controls to test research hypotheses on a stratified population.
Using the Community Health Index, access has been obtained to routine NHS lab tests from the Tayside and Glasgow area health boards. This covers ~90% of the GS cohort. results More than 20,000 GS participants were genotyped using a genome-wide SNP array. We have extracted uric acid (urate) test results from the routine biochemistry dataset and performed genome wide association (GWAS) analyses with genotype and imputed data. This showed the expected significant result for the SLC2A9 uric acid transporter gene. This provides a proof of concept that the NHS laboratory data could be used for further GWAS (and other genetic) analyses of additional biomarkers, currently under way.
Discussion The Genetic Annotation Expert Working Group of Generation Scotland has now established and validated linkage to routine laboratory data, overcoming technical and governance issues in the process. There is no national dataset, with source data being held in different locations, although it is all part of the Scottish Care Information database (SCI Store). Linked data is released into accredited safe havens, with access limited to approved researchers, and great care must be taken when dealing with individual record level data. The test results go back up to 25 years, and in some cases, the test methodology has changed or been recalibrated several times during that period. conclusion Most laboratory tests are requested by GPs and the results could also be linked and accessed via the primary care dataset, which presents a new set of both opportunities and limitations.
This assumes that these combinations can be combined to create a binomial parameter to explain the number of patients in a group. We compared this model to a logistic regression model. results Using the Bayesian model we estimated conditional probabilities which were much closer to the real value. For example, for the probability of condition 1 (e. g. dementia) given condition 2 (e. g. diabetes) and 3 (e. g. obesity), conventional analyses that conflate diagnosis with true condition status predicted a value between 0. 26-0. 28, the Bayesian model predicted 0. 34-0. 44, and the true value was 0. 375. We could also estimate the probability of receiving a diagnosis for condition 1 in the case that the patient has the condition and doesn't have the condition. Conventional analysis cannot estimate these values from our dataset.
Methods We examined associations between diagnostic routes and experience for patients with colorectal cancer using linked data from the Cancer Patient Experience Survey 2010 and the Routes to Diagnosis dataset. Routes to Diagnosis denote eight different care pathways to the diagnosis of cancer, including screening detection, emergency presentation, 'urgent' specialist referral for suspected cancer to secondary care (otherwise known as 2-week-wait referral) and non-urgent referral. We selected 10 'report' and 8 'evaluation' questions from the survey instrument, representing all major aspects of the patient journey from diagnosis to post-treatment care. 'Report' questions reflect actual processes of care (e. g. whether patients were provided with access to specialist nursing), whereas 'evaluation' questions reflect the appraisal of how a process was experienced (e.
We have shown that the diagnoses of T2D and incident cancer commonly co-occur. Thus, there are opportunities to develop risk models of cancer occurrence within the first 6 months after diagnosis of T2D as an early cancer diagnosis strategy. Previous studies on this topic have been limited by failure to link with cancer registries, which (i) commonly underestimates cancer incidence by 9% to 25% and (ii) may misclassification date of cancer diagnosis, when cancer is diagnosed through primary care datasets only.
The dashboard interrogates electronic health records using a set of 13 medication safety indicators, and presents the resulting information to healthcare professionals in both aggregated form and as lists of individual patients with potential safety hazards. We explored the ways in which the dashboard was used in general practice by clinical pharmacists, GPs and other GP staff. Methods Eighteen semi-structured interviews were conducted with pharmacists and a range of practice staff from nine General Practices within Salford. Interviews were audio recorded and transcribed verbatim. Contemporaneous field notes from three non-participant observations undertaken with pharmacists whilst working with the dashboard in practices were included in the dataset. We adopted a template analysis approach that was iterative and concurrent with data collection.
Introduction Linkage of administrative data sources often relies on probabilistic methods using a set of common identifiers (e. g. sex, date of birth, postcode). Variation in data quality on an individual or organisational level (e. g. by hospital) can result in clustering of identifier errors, violating the assumption of independence between identifiers required for traditional probabilistic match weight estimation. This potentially introduces selection bias to the resulting linked dataset. We aimed to measure variation in identifier error rates in a large English administrative data source (Hospital Episode Statistics HES) and to incorporate this information into match weight calculation.
Consent rates for linkage to external datasets ranged from 87% for earnings and employment data to over 97% for school records. Predictors of consent varied. Detailed results for both response and consent rates will be presented. Discussion & conclusion Although consent to data linkage was high among those who responded, almost two-thirds of those contacted did not respond. Our results indicate that linkages relying on explicit consent will not overcome issues of bias due to non-response since individuals who respond to consent campaigns typically share the same socio-demographic characteristics of those participating in the study itself. Introduction Testing potential drug treatments in animal models is a crucial part of preclinical drug discovery however, subsequent clinical failures of drugs show that animal studies do not always reliably inform clinical research.
Methods In this study, we use text mining to extract information from the descriptions of over 100,000 drug screening experiments (bioassays) in rats and mice. We retrieve our dataset from ChEMBL -a literature-based bioactivity database focused on preclinical drug discovery. Our novel analysis of these data uses natural language processing techniques to parse the assay descriptions and mine them for information about animal experiments: genetic strains, experimental treatments, and phenotypic readouts used in the assays. To this end, we use a text mining approach that leverages existing vocabularies and manually crafted extraction rules. To automatically organize the extracted information, we construct a semantic space of assay descriptions using a neural network language model, Word2Vec, and train several assay classifiers based on the generated semantic vector representations.
Introduction Phenotyping algorithms using electronic health record (EHR) data can progress service delivery and research. However, many factors may affect whether an algorithm developed in one setting can be used reliably in another, such as: variations in how datasets were produced (e. g. coding systems, measurement procedures) differences in case mix of the underlying populations the complexity of algorithm implementation. To assess the robustness of phenotyping algorithms against such factors, replicating them across data sources and healthcare regions is essential. We replicated an established electronic phenotyping algorithm for identifying and characterising episodes of acute kidney disease (AKI) in EHR data from different healthcare regions. In clinical practice, individuals who experience AKI episodes require timely and tailored follow-up.
Methods We replicated an algorithm (Sawhney et al. Am J Kidney Dis 2016) developed in Grampian (482K adult population), using datasets from two other UK regions: Hampshire (643K) and Swansea (415. 5K). We determined how the three datasets, which all consisted of linked EHR data across primary and secondary care, were produced. In our analysis, we only included people with a creatinine test. With the algorithm, we determined the incidence of AKI episodes in a certain index year, and compared results between regions. Index years were 2007 (Swansea), 2012 (Grampian) and 2014 (Hampshire). results Although we noted some consistencies between datasets and contexts, we also found differences in how the sets had been produced. For example, whereas Grampian had a single biochemistry department controlling lab data for the entire region, the Hampshire dataset only included creatinine values from part of the regional laboratories.
Discussion We successfully replicated an existing AKI phenotyping algorithm in datasets from different regions across the UK. We found significant differences in AKI incidence across regions, potentially caused by variations in population characteristics data are available for the entire cohort from a universal coverage healthcare system and that potential cases present for healthcare assessment. We aimed to establish: (1) the yield of dementia cases from national hospital and death data and from cognitive testing for dementia in the Whitehall II cohort (around 10,000 London-based civil servants, recruited in 1985-1987 aged 35-55 years) and (2) whether risk of dementia diagnosis in linked health-related data was higher among those who did not attend and complete cognitive testing.
Abstract no. 406 Improving the accessibility and interpretation of advanced analytics when monitoring the quality and safety of patient outcomes Heather Dawe, University of Leeds, Leeds Introduction Monitoring clinical pathways and outcomes to measure and assure quality in patient care is a process that has numerous stakeholders within a healthcare economy. Multiple statistical methods have been developed to monitor clinical outcomes from different sources of healthcare data. 1, 2 These methods require knowledge of statistical and data visualisation techniques to develop reports and charts for interpretation. However, the interpretation of such material is often carried out by business users without such knowledge. The objective of this research was to design and develop a prototype application that will enable technically advanced users to declare and import datasets, apply risk-adjustment methods and generate indicators.
Methods The user requirements of the software prototype were initially established through carrying out a series (N=8) of users and a review of relevant software products on the market. By appealing to the concepts described by Wilkinson, 3 the design of data visualisations such as funnel plots 4 and Variable Life Adjusted Displays (VLADs) 5 were abstracted and generalized. Similar concepts were employed to facilitate the declaration of risk-adjustment methods. The established user requirements and abstraction of chart declaration and risk-adjustment methods were combined and used to design a logical model that formed the basis for the prototype. results A prototype was developed that enables the user to define and import datasets, to declare the risk-adjustment models used to generate indicators from the imported datasets, and to generate the charts used to present these indicators.
NTIs are built using a national administrative database of prescriptions dispensed in community pharmacies creating comparative feedback at various organisational levels (for example GP practices within HBs and HBs within Scotland). In the first year, the Scottish Quality Prescribing Initiative (SQPI) provided additional funding to GP practices (approximately £800 for an average sized practice) to achieve specific targeted prescribing changes in two of the 12 NTIs. Method Six of the 12 original NTIs were purposively selected for in-depth evaluation based on the perceived clinical:cost benefits of each (low:high n=1, medium:high n=2, high:medium n=1, high:low n=1, medium:medium n=1 ). Initial analysis used segmented regression analysis of interrupted time series data to examine the hypothesis that NTI and NTI plus SQPI introduction and cessation (if applicable) would be associated with a change in prescribing other known interventions such as regulatory risk communications were also modelled.
Methods Data was extracted from Public Health England's Second Generation Surveillance System (SGSS) of positive isolates of bacteria or fungi from blood and cerebral spinal fluid in babies aged 0-12 months in 2010 to 2015. Data on admissions to NNUs for singleton births was extracted from the Neonatal Research Database (NNRD) and linked to infection episodes in SGSS. We used deterministic linkage to classify records with the same NHS number in each dataset as links. Probabilistic linkage compared records in NNRD and SGSS, and assigned each pair of records a probability that they belonged to the same baby based on agreement on the identifiers: postcode prefix, postcode suffix, date of birth, sex and hospital. To reduce the number of comparison pairs, blocking on prefix, suffix and date of birth was used so only pairs that agreed on at least one of these were included.
Methods We extract clinical data from a number of systems composing the electronic patient records. We collect national registry datasets, patient level primary care data and air pollution data. We obtain omics data from existing studies. The core infrastructure is based on SQL Server Integration Services. We developed software components for accurate patient matching, interpretation of unstructured information and data cleansing before import into a data warehouse, using master data services, data quality services and natural language processing (NLP). Anonymised data are loaded as studies into an enhanced tranSMART. We use metrics to track data completeness for exemplar studies and engage with clinicians to review data quality. results To date, we have collected high granularity and quality data across clinical areas.
Methods We extracted 26378 and 26665 records at the age group of 55 to 65 in 2013 and 2014 respectively, from five hospitals of hospital discharge abstract database (DAD), Alberta, Canada. We applied the association rule mining on the 2013 DAD to extract the rules with support ≥ 0. 0019 and confidence ≥ 0. 5. We applied the association rules on six versions of 2014 DAD with different percentages of code deletion for data quality assessment. results Association rules capture various associations of diagnosis codes in the data. Association rules can correctly rank the DAD records with different levels of completeness across hospitals. For datasets with similar size but poorer data quality, High-quality and efficient health care seems not possible nowadays without the support of information technology (IT).
The isolates were sent to Bath University for genome sequencing. The identifiable data was sent to a trusted third part within the NHS to assign an anonymised linking field alongside the sample ID. This information was then transferred to the Secure Anonymised Information Linkage dataset to enable linkage to general practitioner and hospital records. This system then allows bacterial genome data linked to an encrypted sample ID to be linked to the patient medical records. This pilot study selected those patients who had cancer (using GP/hospital data) matched for age, gender and socio-economic status with those who had benign tumours. Their encrypted study IDs were unencrypted by the trusted third party to inform the genome sequencing laboratory which samples to genotype. This formed a nested matched case control study.
Introduction This study aims to quantify the misdiagnosis of chronic obstructive pulmonary disease (COPD) in asthma patients in the UK using electronic health record databases. The specific objectives of this study are to calculate the PPV, NPV, sensitivity and specificity of a COPD diagnosis recorded by a general practitioner in patients with a confirmed asthma diagnosis. Asthma is difficult to assess in health-care database epidemiological studies as the diagnostic criteria are based on non-specific respiratory symptoms and variable expiratory airflow limitation which are often not recorded in electronic medical records. Specifically asthma in older patients can be confused with COPD.
This paper describes the process of determining ePROS of importance, and presents the dataset included in the beta-app piloted. Methods Interviews were held with a range of stakeholders (10 RA practitioners, 12 RA researchers, 21 RA patients). Interviews determined ePROSs for inclusion, recording frequency, and the value of a free text diary. Initially, interviews were conducted with practitioners and researchers regarding their preferences. Key ePROS identified were tabulated and discussed with the PPI (patient and public involvement) group, working alongside the research team, and the table refined. Subsequently, patients were interviewed regarding their preferences and also asked to feedback on tabulated suggestions. Ultimately, components which had widespread consensus across the stakeholder groups were incorporated into the app.
Components without consensus, or beyond the scope of the study, were documented with a view to incorporating them in later versions. PPI group members reviewed and commented on the suitability of the final components prior to their incorporation into the beta app. results All stakeholder groups wanted to capture information on changes in disease activity and impact of the disease (physically and emotionally). Practitioners and researchers wanted routine data that had been recorded consistently using existing validated tools, but saw the value of a diary for recording triggers and alleviators of disease activity. Patients mainly suggested recording notable events (such as flares) as they occurred, but could see the benefits of recording data routinely. The final dataset comprised the following: Daily question set: Pain, difficulty with physical activities, fatigue, sleep difficulties, physical and emotional wellbeing, coping (10 point visual analogue scale), morning stiffness (7 categories) Weekly question set: Number of tender and swollen joints (numeric value 0-28), global assessment of wellbeing (10 point visual analogue scale), employment status (yes/no response -radio button), description of flare (free text box) Monthly question set: Health Assessment Questionnaire (HAQ) impact of disease on daily activities, including function and mobility (fixed point scales -radio button) plus free text entry box.
Recorded service user information will be generated lively from shared databases based on the group definition; furthermore the group definition can be further used in the report function. The report function includes 15 types of report that create visual result of group definition. While the analytics required draws on complex real-world data, it is of prime importance to assure that the decisions are transparent and made with correct assumptions. In order to provide transparency and auditability of the tool findings, we have designed a data provenance module within Atmolytics to capture the full audit trail of the data transformations, leading to better understanding of the context of data production.
Method The extended auditing/logging capacity was realized by employing PROV-DM together with provenance template, specifying the structure of data provenance to be captured. The storage solution is designed based on graph database. results Initial analysis confirms that capturing provenance in visual analytics should not only describe automated processes but also human actions relevant to the data and models in the system -interactive steps etc. -that are more commonly associated with usability studies. Additionally, current auditing/logging capacity in a typical visual analytics system is insufficient for tracing or representing human actions and supporting a meaningful process mining, more specific it lacks a connectivity of recorded messages. The prototype resulted graph now connects the activities of analytic process within the system.
Discussion Capturing provenance in a visual analytics system such as Atmolytics is not a trivial task. Each of its subsystems relies on a separate data store, which communicates with others exclusively via a service bus architecture. Furthermore, disconnected auditing/logging functions expose different levels of events. In order to overcome these issues, we are employing provenance templates as higher-level abstractions over provenance graph data, implemented through a dedicated module that communicates with all other parts of the system. Future research plan is provenance data visualization by clustering and RDF database comparison on provenance use cases. conclusion We find provenance template approach to be a realistic and promising solution to improving auditing/logging capability in enterprise visual analytics software, and we are currently in the process of developing the provenance visualization tool.