dataesr/scientific-paragraphs-categorization huggingface dataset:
This paper presents a user-driven innovation project at Oslo University Hospital, Norway, aimed at developing such technologies. User-driven innovation has been described as a pull away from the traditional technology-centered innovation strategies, towards strategies that aim at achieving a co-evolution of the technical and the social, where users play a crucial role. This development has also been characterised as a democratisation of innovation, and it is interesting to note how both European Actions and priorities (e. g. in H2020) and various national priorities around Europe (and also in other parts of the world) actively seek to stimulate user-driven innovation through financial resources. The project we report from is a result of such an initiative in Norway the Norwegian clinical genetic Analysis Platform (genAP) project was funded by the Norwegian Research Council as a user-driven innovation project in collaboration between the Department of Medical Genetics (DMG) at the Oslo University Hospital and the Department of Informatics at the University of Oslo.
The architecture relies on an Oracle database to store the exchanged information and customization parameters. A JEE application deployed under Apache Tomcat will support the dashboard application, where Introduction Scientific journal publications seldom capture scholarly work so that an external researcher could reproduce reported results. The growing number of platforms for sharing scholarly assets (e. g. Figshare, ReShare, and EPrints for research data) reflects this, presenting science a sizeable opportunity to reuse resources of previously funded projects to make new discoveries, especially across disciplines/ domains. Despite the plethora of platforms and standards, barriers remain to the reuse of scholarly assets due to missing information about datasets and their analyses. Poorly curated data leads to unnecessarily duplicated efforts in reusing scholarly assets and data harmonization and analysis are often manually repeated when reusing data.
We developed the Variable Bank Module which facilitates the import and export of data. Research questions can be asked across multiple datasets created by different communities. The Job Manager module works with data and computational code through Execution Research Objects which capture the process of executing analysis code on data. The eLab has been further developed and successfully used by the STELAR (Study Team for Early Life Asthma Research) consortium to harmonise data from five UK birth cohorts and the iFAAM (integrated approaches to food allergy and allergen management) EU project to harmonise data from food allergy studies. conclusion We have presented Variable Reports and Execution Research Objects as approaches to capturing context and provenance of data and methods and their application within the HeRC eLab.
Our work distinguishes itself from existing approaches, providing: • a way to combine and pool datasets from any domain • data search, combine and export capability from multiple studies automatically, with fewer manual processes to query and deliver data extracts. Often, data warehouses and other approaches, also extract, transform and load data, without capturing the relationships between variables-the essence of any research claim. Variable Report Models advance semantic harmonization-with no further cost for reuse after the initial investment. The use of Minimum Information Checklists with Variable Reports means that software does much of the metadata quality checking. Introduction Diverse and disparate datasets are increasingly being linked and used in research both at scale and at higher clinical resolution.
In biomedical research, a growing 'open' research culture has emphasised the significance of publiclyaccessible metadata the availability of which is critical since researchers use clinical datasets containing personally identifiable information, and are not always able to readily share these data. However, the inability to characterise and evaluate datasets due to insufficient metadata limits the extent to which data may be utilised for research. These challenges are compounded by inconsistencies in the way researchers record and share discovered datasets. This study aimed to identify and evaluate methods to enhance biomedical research data discoverability.
Abstract no. 114 Determining the accuracy of routinely-collected health datasets for identifying neurodegenerative disease cases: UK biobank approach Tim Wilkinson, Amanda Ly, Zoe Harding, Christian Schnier, and Cathie Sudlow, University of Edinburgh, Edinburgh   Introduction Neurodegenerative diseases such as dementia and Parkinson's disease (PD) are major causes of mortality and morbidity. Prospective cohort studies can provide important insights into the determinants of these disorders. UK Biobank (UKB) is a large, population-based, prospective cohort study of over 500,000 participants aged 40-69 years when recruited between 2006 and 2010. Participant follow-up is largely via linkage to routinely-collected health datasets such as hospital admissions, death registrations and, increasingly, primary care data.
Methods We conducted systematic reviews of studies that assessed the accuracy of ascertaining dementia or PD cases from codes in routinely-collected datasets versus a clinical expert diagnostic reference standard. We summarised results for positive predictive value (PPV) and sensitivity. Informed by these results, we performed our own validation study of dementia coding using data from UKB participants and have commenced a similar study for PD. Using published and online resources and clinical judgement we generated a list of ICD-10 and primary care (Read version 2) dementia codes. We identified Edinburghbased UKB participants with a dementia code in hospital admissions, death or primary care data. We extracted relevant letters and investigation results from the electronic medical record (EMR).
A neurologist adjudicated on whether dementia was present based on the extracted notes, providing the reference standard to which the coded data were compared. We calculated the PPV for each data source individually and combined. results The systematic reviews revealed a wide variation in methodologies and results across existing studies in the literature. For PD, PPVs ranged from 71-88% in hospital and death datasets, while in a primary care dataset the PPV was 81%, increasing to 90% in patients who also received >1 prescriptions for antiparkinsonian drugs. Sensitivities for PD coding in hospital and death datasets ranged from 53-83%. PPV estimates for dementia coding in hospital and death datasets ranged from 4-100%, with PPVs of 83-92% for primary care data. The use of specific subtype codes or selection of codes in the primary position only resulted in higher PPVs; however, there was a corresponding reduction in case ascertainment.
Introduction The correct identification and classification of use errors is crucial in evaluating the usability and the safety of a medical device. This identification proceeds through a detailed analysis of scientific literature of similar devices, context of use, feedbacks from manufacturers, and incident report databases. The main goal of this study was to identify and classify potential use errors related to the use of innovative pain monitor (ANI Monitor). The calculations have been designed in order to objectively rate the level of the patients' pain and their own comfort/discomfort by means of an electrophysiological signal (Analgesia Nociception Index -ANI). Method Several sources of information have been inspected in agreement with European and International guidelines. We collected data coming from: i) similar devices via journals and conference proceedings as well as safety reports [e.
Discussion In our study, the identification of use errors arises from the integration of different sources (e. g. context of use, observations of the real environment, interviews with experts as well as incident report databases from manufactories), since no literature is available yet, due to the novel nature of this technology. Consequently, by means of FMEA, we systematically classified and prioritized potential failures and their effects on patients to design possible scenarios of user tests. conclusion The integration of different sources, as well as literature review, context of use, manufacturers' feedback and incident report databases, is essential in the user-centred design for the usability validation of medical devices. It allows identifying risks and use errors of the device itself.
Methods We will establish a Cancer e-Lab similar to the already established STELAR Asthma e-lab. 1 This serves as "a data repository populated with a unified dataset from well-defined cohorts, which also provides the computational resources and a scientific social network to support timely, collaborative research across the consortium". For OCTOPUS, the primary endpoint will be cancer-specific survival. We have identified 30 eligible RCTs for CRC and 6 RCTs for EC. Trial leads have been identified, contacted and pledged willingness to partake in the consortium. The federated meta-analysis approach means that data can stay at source and analysis comes to the data. results The e-lab will serve as a 'data safe-haven': "a repository in which useful but potentially sensitive data may be kept securely under governance and informatics systems that are fit-for-purpose and appropriately tailored to the nature of the data being maintained, and may be accessed and utilized by legitimate users".
Data processing was made to obtain the PP dataset from the EHRs, where each one is composed by the clinical observations ordered in time. We define five different types of clinical observations: hospitalisation, outpatient consultations, emergency room visits and laboratory tests for glucose and creatinine. The episodes of a patient are codified and put together in the PP following the timeline of each episode. Then, using the SW-based algorithm a comparison between each pair of PPs is carried out. The comparison has a possible output: cardiopathy complications. In case the PP pair is not ranked, it is not shown. The logic for comparing PP is developed using a Big Data framework called Apache Storm. Different components are defined: a Spout that gathers the tuples from a web queue and passes the data to the logic, and a set of Bolts, each Bolt has a unique function inside the topology, and the Bolts can be largely replicated.
These Bolts work together to join the pathways from the web queue with each one on the database, creating a set of 2 tuples for each database entry: the query patient, and the database patient. After finding local similarities, it is possible to rank the 10 best PP alignments. These are sent as possible outcomes for the query patient. The LPPA system is based on different technologies. The web interface is primarily programmed using HTML and JavaScript, having WordPress as a design tool. The server is programmed in JavaScript, using node. JS to run the system, and various Introduction Healthcare resources are finite and frequent readmissions can overwhelm even developed health systems. Patients with frequent admissions also experience significant psychological stress and financial burden. In the United States, 30-day readmissions are considered an accountability measure and quality indicator.
Data were randomly split into 'training and 'validation' samples with an 80:20 ratio. Algorithm performance was compared using Pearson correlation between predicted and real values from the validation dataset. RMSE was used as a measure for prediction error. results 2036 adult participants (mean age = 25. 14, SD = 8. 51) provided information on all measures. The best algorithm for predicting the actual symptom reports using the full range of the scale was the random forest (r = 0. 28, RMSE = 31. 29). Regression with lasso regularization performed similarly well (r = 0. 22, RMSE = 30. 81). Regression trees (r = 0. 08, RMSE = 30. 68) and SVM (linear: r = 0. 11, RMSE = 47. 34 polynomial: r = 0. 05, RMSE = 47. 82) performed worse at predicting symptom reports using the information based on participants' Facebook 'Likes'.
Clinical research focusses on health-related outcomes whereas the eHealth market focusses on customer engagement. Therefore, we aimed to assess both the clinical effectiveness of the technology, and also identify application features that patient want and will continue to use in a self-management app. Methods For clinical effectiveness, we followed Cochrane methodology to systematically review randomised controlled trials (RCTs) of telemedicine in adults/teenagers with asthma, and synthesised data on health outcomes (e. g. asthma control questionnaire and/or exacerbation rate). We searched nine databases and 2 reviewers selected eligible papers, extracted data, and used meta-analysis and narrative synthesis. For patients 'want' features, we systematically searched Google for 'asthma' 'forums' and retrieved posts in which patients discussed 'wanted' features.
Univ. Lille, EA2694, Lille Many epidemiological studies now rely on the reuse of large medico-administrative databases. In those studies, most of the time is consumed in managing data and performing basic statistical analyses, and is not available anymore for complex statistical and medical analysis, so that the potential of such databases is sometimes under exploited. The objective of this work is to build SAF4Big, a statistical analysis framework for big data in healthcare, using literature-based specifications. A literature review was performed on PubMed in 4 different medical domains: caesarean deliveries, cholecystectomies, left implantable ventricular assist devices, and hip replacement surgeries. We identified 43 papers relating analyses of large databases. They reported epidemiological indicators (e.
g. mean age) that were abstracted to features (e. g. univariate description of a quantitative variable), that were implemented through 37 new functions in R programming language (e. g. a function will draw a histogram, compute the mean with confidence interval, quantiles, etc. ): 4 functions for data management, 9 for univariate analysis, 8 for bivariate analysis, 11 for multivariate analysis, and 5 intermediate functions. Those functions were successfully used to analyse a French database of 250 million discharge summaries. The set of R ready-to-use functions defined in this work could enable to secure repetitive tasks, and to refocus efforts on expert analysis.
Abstract no. 202 release of the standard export data format by the japanese circulation society for standardized structured medical information exchange extended storage Masaharu Nakayama and on behalf of the IHE-J Cardiology Team, Tohoku University, Sendai background In the era of big data, utilization and analysis of large amounts of clinical data are important. A substantial clinical database for cardiovascular disease requires a wide range of data including medical records, medication, laboratory tests, physiological examinations, and data from multiple modalities. Although these data are in a digital format, data transition from the hospital information system (HIS) to the database is performed manually in most hospitals, resulting in excess burden for physicians and clinical research coordinators.
Methods and results In Japan, the standardized structured medical information exchange (SS-MIX) was developed in 2006 as a standard data storage format to share clinical data from various vendor-derived HISs and was revised (SS-MIX2) in 2012. Several national database projects and local electronic health records use the SS-MIX2. Moreover, data are retrieved from the SS-MIX2 storage for secondary use. The SS-MIX2 storage is divided into two categories: standardized and extended storage. The standardized storage includes standard structured and coded clinical data, such as basic patient data, prescriptions, and laboratory data transferred from the HIS in Health Level Seven format. All other data in non-standardized formats, such as electrocardiogram (ECG), echocardiography (UCG), and catheter examination data, are stored in the extended storage.
Standardized storages are often utilized for several projects however, extended ones are rarely used. In 2014, the Japanese Circulation Society (JCS) decided to develop structured standard formats to save clinical data in SS-MIX2 storage, in collaboration with an international association that aims to improve the use of computer systems in healthcare through standards, integration healthcare enterprise (IHE), IT vendors, and several academic cardiology societies. In 2015, the Standard Export datA forMAT (SEAMAT) for ECGs, UCGs, and catheter examination data was announced. According to SEAMAT, the item name, unit, and format for SS-MIX2 can be determined. This step would help to effectively establish a nationwide clinical database and reduce the tedious manual data input by clinicians and clinical research coordinators.
However, hospitals may continue to incur significant costs to equip information systems with this format. Thus, a program that enables the conversion of comma-separated data from information systems to SEAMAT is also being developed, which will be a useful and economical tool for transferring huge clinical data to SS-MIX2. conclusions Sharing medical information among hospitals is crucial for patient care and research. In Japan, JCS developed SEAMAT for the secondary use of important clinical data in cardiology. These stepwise implementations are crucial to achieve a nationwide clinical database for cardiology disease in Japan.
Inference models in clinical research and CDSS can benefit from this by defining queries to a valid dataset with known structure and constraints. Introduction Alzheimer's disease, the most common neurodegenerative disorder, is expected to cause 1 million new sufferers per year as early as 2050. Similarly, numbers of people suffering from Parkinson's disease are expected to rise to between 8. 7 and 9. 3 million by 2030, making it the second most common neurodegenerative disorder. As such, these diseases have become a major focus of global biomedical research in an effort to develop a detailed understanding of causes and pathology that will lead to novel treatments and improved care. The ApiNATOMY project (http://apinatomy. org/home) aims to contribute to our understanding of neurodegenerative disorders by manually curating and abstracting data from the vast body of literature amassed for these illnesses.
Methods A comprehensive literature review was conducted based on the scoping studies approach 1. Where applicable, the systematic review methods were used to narrow and increase the quality of the final results. The MEDLINE database and the Cochrane Library were accessed between March and August 2016. Out of original 277 yielded publications, 58 were included for further analysis. Considering the manual search queries, 5 relevant papers were added to the final results. results PA is an indicator of the performed level of physical activity in everyday life, as well as during cardiac rehabilitation. Attention was paid to the perspectives from which the term was considered in the selected studies. In regard to adherence, the types and settings of physical-activity-related behaviour were determined and classified.
Method In order to ensure the reproducibility of research results and for preserving raw data, the general trend favours the pooling of research data in standardized formats. The emerging HL7 Fast Healthcare Interoperability Resources (FHIR) aims to increase interoperability, information integrity, as well as the implementability of data exchange tasks in the healthcare and research IT environment. With focus on the research platform system CentraXX (Kairos GmbH) for biobank and study management, HL7 FHIR is used in this work to improve the semantic integration, (re-)use of relevant data and for connecting point-ofcare medical data. The transition from the research platform to the central clinical repository is desired to be simple, robust, and direct. In order to align and to map the proprietary CentraXX data model to FHIR resources, the underlying relational database schemata were analysed to specify the scope and the requirements for the desired mapping.
references Introduction The University of Oxford is engaged with some of the world's largest population-based, prospective studies. The UK Biobank (UKB) and China Kadoorie Biobank (CKB), each of which recruited over half a million participants, will be based at the University of Oxford's Big Data Institute (BDI). The BDI will be directed at obtaining and characterising large datasets to significantly alter our understanding of the causes and treatment of disease. Both studies will have a unique opportunity to share what they have learnt from each other and to identify new opportunities in the future. Methods UKB and CKB both recruited half a million middle aged participants between 2004 and 2010. Participants were selected from different regions across the UK and China, and have undergone extensive baseline measures, provided blood and urine for future analysis and gave detailed information about themselves.
Participants' health outcomes will be closely followed over the next few decades through linkage with established death and disease registries, and national health databases. Both studies run periodic repeat assessments on a subset of their cohorts and implement project enhancements such as genotyping, physical activity assessment, biochemistry, proteomics and new web-based questionnaires to collect even more data on their participants. results Collectively, both biobanks have combined health-related data on over a million people, stored over 3. 5 million blood samples and have linked participants to 45,000 death records, 100,000 cancer records and 1. 5 million hospital admission and health records. UKB is an open access resource with over 3,619 registered researchers, 343 projects underway and 1,256 released datasets.
Discussion Whilst the two studies began with separate operating and funding models, we have identified many opportunities to share methodologies such as genetic assays, record linkage, encryption, anonymisation and delivery of research datasets. We are looking for further opportunities to explore new and novel big data opportunities by working closely together. We have begun sharing our knowledge and experience with integration of new data sources such as physical activity monitors, electronic data capture devices, imaging data, outcome adjudication and linkage of our participants to air pollution and other meteorological data. We are also collaborating and attracting the attention of other departments within the university to develop machine learning techniques on data such as the ECG to predict future outcomes for our participants and validate these through our continual follow-up.
Method We developed a pilot ontology that specifies inclusion and exclusion criteria, along with relationships between quality indicators and categorises of indicators. Our ontology is intended to make components of the indicators searchable. We recorded metadata, sourced from NHS Digital, for a set of 222 quality indicators 2 in the ontology. We explain further detail about conceptualisation of the ontology in a separate article. 3 results Inconsistent/incomplete metadata for some indicators contributed to inconsistent definitions for properties, including Source and Formula. Some indicators were part of a named set. Some were single indicators. Indicator set names were not standardised. The source for one set was sometimes listed as 'UK Renal Registry'. Other times, it was listed as 'National Renal Dataset'.