dataesr/scientific-paragraphs-categorization huggingface dataset:
Unsupervised clustering (PCA) of daily transfer networks separated the highest-performing 10% of days from the lowest-performing 10% in both sites. There is also a clear separation of the transfer networks on weekends vs. weekdays for both sites. For DH, the edges that contribute most to the separation of the best and worst days form clear pathways from admission to discharge with consistently higher or lower flow, whereas in PRUH the differences in flow tend to affect more individual wards. Introduction Recently, the progression towards precision medicine has sought the development of large databases allowing to assess the impact of risk factors or treatments in specific subpopulations. This is usually a problem for classical cohorts, given the difficulty of enrolment and follow-up of a large enough number of patients.
Methods The RDA allows the generic creation of forms and corresponding documents belonging to a specific patient based on the Entity-Attribute-Value (EAV) design. 2 We implemented a local development environment with open source software. The data model still holds the central EAV components but was substantially simplified to focus on only those aspects that are most relevant in the teaching context. results Only 6 of the original over 150 tables of the RDA data model were reused. User registration, security features, project administration, etc. were not taken into consideration. To process forms, patients, and documents, SQL (PostgreSQL and MySQL), a simple REST interface and the developed web interface are made available by means of the PHP Framework Laravel. The database was preloaded with sample data no patient data from the RDA are used.
2 To identify the relevant data fields of the minimal mitochondrial dataset, the definitions are coded and iteratively abstracted by using qualitative content analysis methods. These data fields are mapped to the concepts offered by MIABIS in Step 3. This step shows, to which extent the MIABIS standard can be extended for this project. The initial concept for a novel mitochondrial data model is developed in Step 4. In order to validate the model, an expert-based evaluation is the main objective of Step 5. This may trigger an iterative process, which will lead to a refinement and re-evaluation of the mitochondrial data model.
Discussion The minimal mitochondrial dataset will serve as a basis for harmonizing mitochondrial information and will increase interoperability of mitochondrial research data. With the development of such a minimal dataset, we contribute to a structured representation of mitochondrial data which leads to enhanced comparability and facilitates data exchange. The strongly structured analysis approach will allow generating reproducible results. Nevertheless it remains to be shown to which extent the MIABIS standard can be applied for creating a mitochondrial data model. conclusion The project combines different approaches from interdisciplinary fields of research (e. g. computer science, social sciences) and contributes to the current state of the art of mitochondrial knowledge management to enhance mitochondrial research networking.
The ability to use Process Mining tools to identify the actual clinical pathways of CVD patients and outcomes will be explored using hospital data from MIMIC-III, an anonymised e-health database from the USA. Method Data for patients with a range of CVD conditions was extracted from the full MIMIC-III dataset and prepared for Process Mining. Two tools were used to construct process models -the open source tool ProM and a commercial tool Disco. Initial process discovery used the heuristic and alpha algorithms in ProM. Machine learning approaches will be used in the next stage of this work to classify pathways based on patient characteristics and compared to best practice. Introduction Data analysis platforms used for health informatics research, follow a centralised approach to data science.
Method The GP prescribing data (Apr 2013 -Mar 2016) for 55 general practices within the Northern Ireland Western Health and Social Care Trust was obtained from the Business Service's prescribing and dispensing information systems. The total number of test requests was collected from the laboratory databases of the Altnagelvin Area Hospital, Tyrone County Hospital, and the South West Acute Hospital. Both the number of drug prescriptions and laboratory tests requested in each practice was normalized by the number of registered patients. The variability of drug prescribing data was determined by calculating the coefficient of variation (CV). The degree of correlation between the laboratory test ordering rates and drug prescribing rates was assessed by calculating the Spearman's correlation coefficient (R).
conclusions Our investigation of variability in drug prescribing rates between general practices provides valuable information on practice variation and helps prioritise future research studies to improve the quality of prescribing. We suggest that optimisation of prescribing could be enhanced by conducting appropriate clinical interventions. Abstract no. 657 Investigating the accuracy of parent and self-reported hospital admissions: a validation study using linked hospital episode statistics data Leigh Johnson, Rosie Cornish, Andy Boyd, and John Macleod, University of Bristol, Bristol   Introduction The Avon Longitudinal Study of Parents and Children (ALSPAC) is a large prospective study of around 15,000 children born in and around the city of Bristol in the early 1990s. Participants have been followed up intensively since birth through questionnaires, clinics and linkage to routine datasets.
In 2013 ALSPAC extracted information on a pilot group of consenting participants from the Hospital Episode Statistics (HES) database. The aim of this study was to validate parent-reported and self-reported data on hospital admissions against HES-recorded hospital admissions. Methods Subjects included in this study were 3,195 individuals enrolled in ALSPAC who had consented to linkage to their health data before the end of 2012. In nine questionnaires completed when the children were aged between 6 months and 13 years old, parents were asked if their child had been admitted to hospital in the time since the issue of the previous questionnaire (the periods covered varied, ranging from 6 months to 4 years). Additionally, when the participants were 18 years old they were asked whether they had been involved in a road traffic accident in the past year and, if so, whether they had been to accident and emergency (A&E) or stayed overnight in hospital.
We compared this information to data recorded in the HES database for the corresponding time period and calculated sensitivities, specificities and predictive values of the questionnairereported admissions and A&E attendances, using HES records as the reference standard. results Up to 10% of individuals had been admitted to hospital during the time periods covered by the questionnaires. Among those whose parent reported a hospital admissions, at least 60% had one or more corresponding admission in the HES data. Where a hospital admission was not indicated on the questionnaire, an admission was found in the HES data for between 1. 4% and 3. 6% of the participants. Initial analysis suggests that some of the parent-reported admissions may have actually occurred prior to the period referred to in the questionnaire.
Discussion & conclusions We found that the specificities and negative predictive values of parent-reported hospital admissions were high at all ages. The sensitivities and positive predictive values were lower. There are several possible explanations for this. A proportion of respondents may have interpreted the questions about admission to hospital as including visits to A&E and/or outpatient appointments. The HES database only includes A&E data from April 2007 (when the ALSPAC children were aged 15-16 years old) and outpatient data from April 2003 (when they were 11-12) so it was not possible to examine whether this explained the low sensitivities. Further, some hospital admissions would be to non-NHS providers which are not recorded in HES. Conclusions will be drawn when the additional analyses outlined above have been carried out.
As part of the Joint Action on Monitoring Injuries in Europe (JAMIE) project, and now the BRIDGE-Health (BRidging Information and Data Generation for Evidence-based Health Policy and Research) development the European Commission (EC) funded the development of a standardised European Injury Data Base (IDB). The IDB comprises two datasets: the Full DataSet (FDS) and Minimum DataSet (MDS). Although the MDS collects less detail than the FDS it is simpler for countries to adopt, and still sufficient to allow enumeration of injuries in key areas such as the home, leisure, work, road, falls, sports, and self-harm. Training, guides and rigorous quality checks, ensure consistency across participating countries.
Patient information was extracted from the EHR of the Hospital Italiano in Buenos Aires, Argentina. In order to have a training and a validation dataset, two samples of patients from different years (2005-2015 total n = 2463) were extracted. The only inclusion criterion was age (≥40 <80 years old by 1/1/2005 and by 1/1/2015 for each sample). The sampling was carried out using simple randomization. The training set (2005) featured 1663 patients. The validation set (.
positives. The methods described here are available as open source software at: http://github. com/christie-nhs-data-science/social-factors-classifierresults To explore how general the rules are, the system was evaluated on three manually annotated datasets coming from: Informatics for Integrating Biology and the Bedside (i2b2) 2006 smoking challenge (i2b2/2006), i2b2/2014 heart disease risk factors identification challenge, and The Christie NHS Foundation Trust clinical notes (Christie). The results (see Tablebelow) show that the method achieved a high-level of performance by reaching 0. 90 score for micro-averaged F1-measure.
Method We searched electronic medical databases (including Scopus, Web of Science, OvidSP, MEDLINE and PsychINFO) for potentially relevant studies up to and including September 2016 and written in English. We used the following MeSH search terms: "dementia" (including its subtypes), "electronic health records" (variations thereof) and "primary care". Additionally, grey literature was searched including reports released by the government, councils and relevant major UK charities. results We identified and reviewed 31 studies. In total 35 risk factors and 147 potential markers of early cognitive decline were identified. There was considerable variability across studies as to whether markers were classed as confounders, risk factors, early markers or co-morbidities. Markers predominantly fell within cognitive, affective, motor and autonomic symptoms, prescription patterns of both dementia and non-dementia medication and health system utilization, including type of consultation, frequency of contact and duration.
Methods We used 83 scientific articles from parasitology literature to answer four CLQs automatically: 1-host's name, 2host's gender 3-host's strain, 4-parasite's name. The article title, abstract and beginning of the method sections were used to search for the answers. We utilized the CLQs keywords to search the BK and used standard TM techniques while taking into account the structure of the article. Three Named Entity Recognition tools and two large databases were used to extract the answers. We calculated the co-occurrence of the host and its strain in the abstract and method sections to increase the answers confidence.
The various users are being listed on a leader-board with their respective scores which is also based on location. There is also a leader-board for the various routes in the area. They are displayed based on their rating and distance away from the user. Furthermore, there are some social features that allows the users to find other people in the area, where the idea is that the users can physically meet up and do activities together. The users are able to communicate via messages in the app as well as creating their own social profile which is available to people in the nearby area. The application has been developed for Android devices by use of Xamarin, which is a cross platform development tool. The application's back-end consists of Azure Cloud Services, where both SQL database and server is provided by the host.
A major strength of this study is the use of a national linked dataset, although under-ascertainment of adversity in HES may lead to risk being underestimated. Introduction My Diabetes My Way (MDMW We extracted linked maternity baby hospital data from Hospital Episode Statistics for births between 2009 and 2012 in England. Indicators of maternal adversity were identified using ICD10 diagnosis codes for drug/alcohol abuse, violence or self-harm from any hospital admission up to two years before delivery. Mothers were categorised as current young (<21 years at current birth), prior young (<21 at first birth but not current birth) or never young (>=21 at first birth). Multivariable regression.
1. 1 International Classification of Health Intervention: Alpha2 Version 2016 http://mitel. dimi. uniud. it/ichi/docs/ 2. EN ISO 1828 2012. http://www. iso. org/iso/iso_catalogue/catalogue_tc/catalogue_detail. htm?csnumber=52388 3. SNOMED CT® Editorial Guide July 2016 International Release (US English). https://confluence. ihtsdotools. org/display/DOCEG/ SNOMED+CT+Editorial+Guide Abstract no. 199 enhancing nationwide medico-administrative databases analysis with sAf4big, a statistical analysis framework for big data in healthcareAlexandre Georges, Alexandre Caron, Jean-Baptiste Beuscart, Cécile Bonte, and Nicolas Girier,CHU Lille, Lille.
results A total of 1799 patients (58. 5% males and 41. 5% females) were included for further score calculation. The Brier score for APACHE II and SAPS II were 0. 17 and 0. 196, respectively. Both scoring systems were associated with acceptable AUCs (APACHE II = 0. 745 and SAPS II = 0. 751). However, none of prediction models were fitted to dataset (H-L ρ value < 0. 01). reference 1. H. Arksey and L. O'Malley (2005) "Scoping studies: towards a methodological framework", Int. J. Soc. Res. Methodol. , 8(1):19-32. Abstract no. University of Medical Sciences, Mashhad Zahra Rahmatinezhad, Breast Cancer Research Center, ACECR, Tehran Saeid Eslami, Student Research Committee, Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, MashhadMasoumeh Sarbaz, Shahid Beheshti UMS, TehranIntroduction Accurate outcome prediction by the means of available clinical contributing factors will support researchers and administrators in realistic planning, workload determination, resource optimization, and evidence-based quality control process.
The key components of our software stack include: Data Ingestion and Schematisation (using Apache AVRO), Database Storage and Data Interface, Data Analytics, Front-end Ecosystem, Privacy and Security. resultsWehave utilised the Confluent Platform as a core component, this is a new open source suite of tools built on top of Apache Kafka. At fixed intervals of time, the patients' data sources collect data representing the patient's attributes by passive (e. g. from hardware sensor streams) or active (e. g. questionnaires, assessments apps) ways. These attributes are then ingested via an HTTPS interface which translates REST calls in native Kafka calls. After a restructuring phase, data (both realtime and historical) are simultaneously analysed and persisted. Two different data warehouse layers (cold and hot storage) are deployed to provide low latency and high performance data access via controlled interfaces.
positives. The methods described here are available as open source software at: http://github. com/christie-nhs-data-science/social-factors-classifierresults To explore how general the rules are, the system was evaluated on three manually annotated datasets coming from: Informatics for Integrating Biology and the Bedside (i2b2) 2006 smoking challenge (i2b2/2006), i2b2/2014 heart disease risk factors identification challenge, and The Christie NHS Foundation Trust clinical notes (Christie). The results (see Tablebelow) show that the method achieved a high-level of performance by reaching 0. 90 score for micro-averaged F1-measure. i2b2/2006 i2b2/2014 Christie Smoking category Total # P R F Total # P R F Total # P R F Current Smoker 11 0. 58 0. 64 0. 61 33 0. 57 0. 70 0. 63 4 0. 76 0. 50 0. 57 Past Smoker 11 0.
Study population A total of 2500 BRCA1 pathogenic variant carriers was drawn from 20 centers from North America, Europe and Australia as reported previously. 14 Eligibility criteria for study participants included the following: (1) female carriers of pathogenic BRCA1 variants; (2) at least 18 years of age at recruitment; and (3) Caucasian self-reported ancestry. BRCA1 pathogenic variant carriers selected for the study were stratified into two groups consisting of women diagnosed with invasive breast cancer when younger than 40-years old (n = 1250) and women who had not developed breast cancer or who had developed a first ovarian cancer when 35 years of age or older (n = 1250). All BRCA1 pathogenic variants are listed in Supplementary Table  S3  and deposited in the ClinVar database (Submission ID -SUB1994380; http://www.
CNV detection and quality control All DNA samples were genotyped with the Human610-Quad BeadChip (Illumina, Inc, San Diego, CA, USA) with ~610 000 markers (including ~20 000 non-polymorphic markers) for SNP and CNV analysis. Data for each array were normalized using GenomeStudio 2011. 1 software (Illumina). Probe information including, genomic location, signal intensity (Norm R), allele frequency (Norm theta), Log R Ratios (LRRs), B allele frequencies (BAFs) for each sample was calculated and exported from GenomeStudio. CNV calls were generated using four algorithms: PennCNV (version 2009 Aug27),  15  QuantiSNP (v2. 1),  16  CNVPartition (v2. 3. 4, Illumina Inc. ) and GNOSIS (a CNV detection algorithm within the CNV analysis package, CNVision, (http://sourceforge. net/projects/cnvision/files/).
Quality control procedures were performed to remove poor quality array data (Supplementary Figure  S3 ). Samples were excluded if they met the following criteria: PennCNV measures of log R ratio s. d. 40. 28, BAF drift 40. 01, waviness factor deviating from 0 by 40. 05; QuantiSNP measures of BAF outliers 40. 1, LogR outliers ≥ 0. 1, BAF s. d. ≥ 0. 2, LogR s. d. ≥ 0. 4. A total of 2319 samples passed quality control steps and were assessed in the study. CNV calling results of all four algorithms were parsed and then merged using CNVision. To reduce false positives, CNV calls were excluded if ≥ 1000 kb in size, and/or were predicted by only one algorithm. Nine further CNVs called within the multi-histocompatibility complex on chromosome 6 were excluded from the study, as both a deletion and a duplication were predicted by two algorithms.
Defining CNV regions that may contribute to modification of risk To identify new genomic loci contributing to breast or ovarian cancer risk in BRCA1 pathogenic variant carriers, common and rare deletions that overlapped gene regions were assessed using a genome-wide approach. Our study focused primarily on genomic deletions that overlapped gene regions for several reasons: (1) inter-individual analysis of CNVs is not straightforward as these variants do not typically occur in discrete genomic regions. The start and end coordinates of gene sequences were therefore used as a non-redundant approach to define CNV regions across the genome. (2) In contrast to duplications or copy-number gains, the genomic location of a deletion can be predicted from the array data. These data were not able to show the genomic location of a duplicated region, thus gene(s) or other functional genomic regions that are potentially disrupted by these structural events remain undetermined.
(3) Whole or partial gene deletions are known to be potentially disruptive by causing haploinsufficiency or truncation of the expressed protein, and (4) common and rare CNVs that have previously been reported to be associated with breast and ovarian cancer risk have typically been deletion events. 11, 12, 17, 18 e annotated 39 544 UCSC RefSeq (NCBI36/Hg18) transcripts using the SOURCE database  19  and defined the genomic intervals for a total of 18 791 unique genes (Supplementary Figure  S4 ). Thus, each gene interval encompassed the start and end of all corresponding alternate transcripts. 1] [22]  All CNVs used for this study are deposited in the dbVar database (https://www. ncbi. nlm. nih. gov/dbvar) with the accession number nstd132.
RESULTS Genome-wide CNV analysis was performed on 2319 individuals with pathogenic BRCA1 pathogenic variants, including 1202 breast cancer cases (1117 non-breast cancer affected) and 357 ovarian cancer cases (1962 non-ovarian cancer affected), using published genotype data from Illumina 610K SNP arrays. 14 A total of 60 893 CNVs were called across the study participants using four different algorithms (PennCNV, QuantiSNP, GNOSIS and CNVPartition) that passed the data quality threshold (see Methods and Materials). Of these, 89% and 94% CNVs were predicted by PennCNV and QuantiSNP, respectively, compared with a lower prediction rate from GNOSIS (35%) and CNVPartition (42%; Supplementary Table  S4 ). The average number of CNVs observed per individual was 26. 3 (range 4-203) that ranged in size from 314 to 999 990 bases.
006) for breast and ovarian cancer, respectively. Eight loci (PABPC4L, APBA2, FAM189A1, FUT7, ENTPD2, NPDC1, C9orf139 and L1CAM) were associated with risks for both breast cancer and ovarian cancer (Po0. 05). SNP arrays are well known for low accuracy when assessing CNVs, compared with other platforms such as bacterial artificial chromosome array and oligonucleotide arrays. 25 We therefore attempted to validate CNV regions using Nanostring technology, qPCR and data from the recently published Human CNV Map. 7  Twenty-nine predicted CNV loci were selected for validation including the most common deletions (41% frequency) found to be associated with breast or ovarian cancer risk in the BRCA1 pathogenic variant carrier cohort. Eight of these 29 (28%) CNV loci were confirmed by qPCR and/or Nanostring analysis, including four loci that were associated with breast cancer (GTF2H2, ZNF385B, NAALADL2 and PSG5) and two loci that were associated with ovarian cancer (CYP2A7 and OR2A1; Table  1 ).
Nanostring analysis of eight putative CNV loci from Table 1 (ZNF385B, CALCRL, TFPI, GTF2H2, SLCO1B1 FGFR1OP2, TM7SF3 and ALX1) in 48 study samples only found seven deletions not identified by the calling algorithms, suggesting a low false negative rate (2% (7 Nanostring calls / 352 Negative bioinformatic calls ); Supplementary Table  S5 ). The strongest association with a validated deletion was observed for ovarian cancer, detected in 75/1962 (3. 8%) unaffected carriers and 4/357 (1. 1%) affected carriers (RR = 0. 50, P = 7 × 10 -3 ) overlapping the CYP2A7 locus (19q13. 2; Supplementary Figure  S2 ). To assess the functional relevance of the validated CNV deletion overlapping the CYP2A7 locus, the genomic landscape at this region was investigated using publicly available genomic data from ENCODE  26  and the Roadmap Epigenomics Consortium 27 (Figure  1 ).