bluuebunny/crossref_metadata_2025_split huggingface dataset:
Application of natural language processing methods to extract coded data from administrative data held in the Scottish Prescribing Information System
Authors: Nangle Clifford , McTaggart Stuart , MacLeod Margaret , Caldwell Jackie , Bennie Marion 
Published: April 2017

ABSTRACT
ObjectivesThe Prescribing Information System (PIS) datamart, hosted by NHS National Services Scotland receives around 90 million electronic prescription messages per year from GP practices across Scotland. Prescription messages contain information including drug name, quantity and strength stored as coded, machine readable, data while prescription dose instructions are unstructured free text and difficult to interpret and analyse in volume.
The aim, using Natural Language Processing (NLP), was to extract drug dose amount, unit and frequency metadata from freely typed text in dose instructions to support calculating the intended number of days’ treatment. This then allows comparison with actual prescription frequency, treatment adherence and the impact upon prescribing safety and effectiveness.
ApproachAn NLP algorithm was developed using the Ciao implementation of Prolog to extract dose amount, unit and frequency metadata from dose instructions held in the PIS datamart for drugs used in the treatment of gastrointestinal, cardiovascular and respiratory disease. Accuracy estimates were obtained by randomly sampling 0.1% of the distinct dose instructions from source records, comparing these with metadata extracted by the algorithm and an iterative approach was used to modify the algorithm to increase accuracy and coverage.
ResultsThe NLP algorithm was applied to 39,943,465 prescription instructions issued in 2014, consisting of 575,340 distinct dose instructions. For drugs used in the gastrointestinal, cardiovascular and respiratory systems (i.e. chapters 1, 2 and 3 of the British National Formulary (BNF)) the NLP algorithm successfully extracted drug dose amount, unit and frequency metadata from 95.1%, 98.5% and 97.4% of prescriptions respectively. However, instructions containing terms such as ‘as directed’ or ‘as required’ reduce the usability of the metadata by making it difficult to calculate the total dose intended for a specific time period as 7.9%, 0.9% and 27.9% of dose instructions contained terms meaning ‘as required’ while 3.2%, 3.7% and 4.0% contained terms meaning ‘as directed’, for drugs used in BNF chapters 1, 2 and 3 respectively.
ConclusionThe NLP algorithm developed can extract dose, unit and frequency metadata from text found in prescriptions issued to treat a wide range of conditions and this information may be used to support calculating treatment durations, medicines adherence and cumulative drug exposure. The presence of terms such as ‘as required’ and ‘as directed’ has a negative impact on the usability of the metadata and further work is required to determine the level of impact this has on calculating treatment durations and cumulative drug exposure.
Social outcomes associated with alcohol-related diagnoses: a population-based analysis using linked administrative data
Authors: Nickel Nathan , MacWilliam Leonard , Eukema Oke , Prior Heather , Nepon Joshua 
Published: April 2017

ABSTRACT
ObjectiveThe objective of this population health research is to identify the social burden associated with having an alcohol related diagnosis.
MethodsWe used linkable population-based administrative data files held in the Population Health Research Data Repository to conduct our research, fiscal years 1990/91 to 2014/15. Data came from several domains including health, social services, justice, and the Canadian Census. We used ICD-9/10-CA codes from the hospital abstract database, medical claims data, and prescription drug data to identify individuals with an alcohol-related diagnosis. Individuals’ socioeconomic status was determined using neighbourhood-level income data from the Canadian Census. We matched, 3:1, diagnosed cases to individuals in our repository using on age, sex, income, and community area. We linked cases and matches to administrative data from justice and social services to identify social outcomes associated with having an alcohol-related diagnosis. Outcomes included receipt of income assistance, residence in publically funded social housing, having a child apprehended by child and family protective services, having a charge for driving under the influence recorded in the justice data, and having a charge for domestic violence recorded in the justice data. We modelled rates using generalized estimating equations from 5 years before date of diagnosis to a maximum of 20 years after date of diagnosis. Models tested for significant differences in rates between cases and matches both before and after diagnosis; as well, we tested for time trends in rates both before and after diagnosis.
ResultsWe identified 52,991 individuals with an alcohol related diagnosis between 1990/91 and 2014/15: 34,145 males and 18,846 females. 80.3% of cases had a mental-health related alcohol diagnosis. Diagnoses followed a socioeconomic gradient with the greatest number of cases coming from low-income neighbourhoods. Cases had a significant spike in rates from one year before to one year after diagnosis date, compared with matches, across all indicators. When we followed individuals for 20 years after diagnosis, we found a significantly elevated rate of social service use and involvement with the justice system, across all outcomes, for all years.
ConclusionReceiving an alcohol-related diagnosis is associated with subsequent increased use of social services and contacts with the justice system. Upstream efforts to reduce alcohol-related diagnoses may result in reduced use of social services and justice contacts.
Secure Privacy Preserving Record Linkage of Large Databases by Modified Bloom Filter Encodings
Authors: Schnell Rainer , Borgs Christian 
Published: April 2017

ABSTRACTObjectiveIn most European settings, record linkage across different institutions has to be based on personal identifiers such as names, birthday or place of birth. To protect the privacy of research subjects, the identifiers have to be encrypted. In practice, these identifiers show error rates up to 20% per identifier, therefore linking on encrypted identifiers usually implies the loss of large subsets of the databases. In many applications, this loss of cases is related to variables of interest for the subject matter of the study. Therefore, this kind of record-linkage will generate biased estimates.
These problems gave rise to techniques of Privacy Preserving Record Linkage (PPRL). Many different PPRL techniques have been suggested within the last 10 years, very few of them are suitable for practical applications with large database containing millions of records as they are typical for administrative or medical databases. One proven technique for PPRL for large scale applications is PPRL based on Bloom filters.MethodUsing appropriate parameter settings, Bloom filter approaches show linkage results comparable to linkage based on unencrypted identifiers. Furthermore, this approach has been used in real-world settings with data sets containing up to 100 Million records. By the application of suitable blocking strategies, linking can be done in reasonable time.ResultHowever, Bloom filters have been subject of cryptographic attacks. Previous research has shown that the straight application of Bloom filters has a nonzero re-identification risk. We will present new results on recently developed techniques to defy all known attacks on PPRL Bloom filters. These computationally simple algorithms modify the identifiers by different cryptographic diffusion techniques. The presentation will demonstrate these new algorithms and show their performance concerning precision, recall and re-identification risk on large databases.
A process-driven platform to manage datasets for research
Authors: McAllister Gordon 
Published: April 2017

ABSTRACT
Objectives

Accumulate, manage and control shared access to research data;
Transform and maintain transformation state information about research data;
Analyse and investigate data in related sets using open and bespoke tools;
Publish extracted data to a secure safe haven environment.

ApproachThe Research Data Management Platform (RDMP) is a set of data structures and processes, sharing a core Catalogue, to manage electronic health records, genomic data and imaging data throughout their lifecycle from identification and acquisition to safe disposal or archival and retention in secured Safe Havens (SH).
The architecture components of the RDMP consist of the Catalogue and five internal processes: Data Load, Catalogue Management, Data Quality, Data Summary, and Data Extraction. These are designed to enforce rigorous information governance standards relevant to the processing and anonymisation of personal identifiable data. The Catalogue serves as the single ‘source of truth’ about the datasets which all RDMP processes consult. This facilitates repeatable, reliable and auditable operations on the data.
The novelty of the RDMP is that it dynamically and seamlessly captures and preserves data transformation processes along with the primary research data to promote reuse and curation of continuously accruing research data repositories in a secure SH environment. Thus, the RDMP brings transparency and reproducibility that benefits research programmes in a way that archival of static data objects does not.
ResultsThe RDMP has been in production use since July 1st 2014. There are 107 datasets configured in the Catalogue, with up to 67 dataset extractions for each of 48 research projects. It has provided data for 32 high-impact journal papers published in the last year.
Improvements in turnaround time:

Research project data provision reduced from six months to two weeks;
Data loading reduced from two days to a few hours;
Research query response reduced from days to within a day, due to improved and standardised metadata catalogue

ConclusionThe RDMP is a key component in automating the regular release of datasets and rationalising dataset changes over time to ensure reliable delivery of extracts to research projects. The tools and processes comprising the RDMP not only fulfil the RDM requirements of researchers, but also support seamless collaboration of data cleaning, data transformation, data summarisation and data quality assessment activities by different research groups.
Tomorrow’s Health Data Scientists in the Making: 2 Years of Training & Capacity Building Activities with the Farr Institute
Authors: Anastasiou Athanasios , Moulton Georgina , McCowan Colin , Taylor Paul , Goddard Catharine 
Published: April 2017

ABSTRACT
ObjectivesThe poster will showcase the Training & Capacity Building Programme (TCBP) established by the Farr network that is available to PhD students and Early Career Researchers (ECRs) both from within and outside the network.
ApproachThe authors are using a mixed-methods approach to present the current state of the art that has motivated the structure of the TCBP and how the programme has been received by students, academic and industry leaders.
Since 2014, the Farr network education leads have worked on a unique education programme that aims to equip students with key professional and methodological skills required by the roles they are likely to take in their future careers.
The content of the programme has been informed by current research in the fields of data linkage and analysis of big datasets as well as the immediate experience of researchers, and healthcare and industry leads working actively in the field.
We have focussed on developing a community of practice through the provision of an environment that has enabled students to share experiences and knowledge and to start building their own networks of collaboration across the Farr centres.
ResultsThe Farr network of PhD students comprises of approximately 66 students from a variety of backgrounds including bioinformatics, computer science, epidemiology, mathematics, psychology and statistics.
The Farr network hosted 2 main events in 2015 that provided the foundation for 34 PhD students to come together to develop skills in written and oral communication, Public and Patient Involvement (PPI), problem-solving, requirements gathering, data visualisation and spatial data analysis.
Formal feedback collected from each event suggests that the programme is received well by students who see it as a set of activities that complement their specialised PhD related education. Feedback from supervisors and future employers also suggests that this programme has facilitated the development of skills that are useful for their PhD and employment.
ConclusionThe Farr network has laid the foundation for the community of practice around the analysis of real-world health datasets across the UK, and will continue to cultivate this through the enrichment of the programme.
Validating Earnings in the German National Educational Panel Study – Do Interviewers Have an Impact on Measurement Accuracy?
Authors: Antoni Manfred , Vicari Basha , Bela Daniel 
Published: April 2017

ABSTRACTObjectivesWe investigate characteristics of respondents and interviewers influencing the accurateness of reported income by comparing survey data with administrative data. Questions on sensitive topics like respondents' income often produce relatively high rates of item nonresponse or measurement error. In this context several analyses have been done on item nonresponse, but little is known about accuracy of reporting. Existing evidence shows that it is unpleasant for respondents to report very low or very high income. In presence of an interviewer income questions might produce incorrect responses due to social desirability bias. On the other hand side interviewers can create a trustful atmosphere in which respondents give more accurate answers.
ApproachUsing linked survey and administrative data we are able to measure the extent of deviation between reported and recorded incomes and explore the influence of respondent and interviewer characteristics on it. The starting point for the linkage is data from the German National Educational Panel Study (NEPS), Starting Cohort 6, which surveys adults from birth cohorts 1944 to 1986. More than 90% of the respondents consented to a linkage of their survey information with administrative data from the German Federal Employment Agency. These longitudinal earnings data are highly reliable as they are based on mandatory notifications of employers to the social security system.We include interviewer and respondent characteristics as well as their interactions into our model to estimate their respective impact on the incidence and size of any bias in reported incomes. This allows us to control for latent interviewer traits that might have influenced the respondent's answering behavior during each interview of a given interviewer.
ResultsThe average deviation of reported from administrative earnings is relatively small (less than 10% of median earnings). Descriptive evidence shows only small variation of deviation across subgroups. Most importantly, female respondents show higher report accuracy. Multivariate results hint at a negligible influence of interviewer characteristics. The major predictors for deviation in respondents' characteristics are their sex, their absolute monthly personal income, their educational level and being born abroad.
ConclusionAlthough the average measurement accuracy is rather high, there are some differences in deviations by subgroups. The impact of these deviations depends on the research question at hand. Research with a strong focus on the respondent’s earnings, e.g. when using them as a dependent variable, should use the linked data rather than only the NEPS survey data.
Probabilistic Linkage of Two National Databases to Study Emergency Care and Hospital Readmission in Children who Survive Traumatic Injury
Authors: Bennett Tellen D. , Dewitt Peter E. , Juarez-Colunga Elizabeth , Cook Lawrence J. 
Published: April 2017

ABSTRACT
ObjectivesTrauma and, in particular, traumatic brain injury (TBI) cause enormous morbidity and mortality worldwide. Children who survive trauma have many health burdens, but how often those burdens lead to emergency care or hospital readmission is unknown. Readmissions occur frequently in adult TBI survivors: approximately 21-28% within one year. No single existing U.S. database contains the necessary variables to study readmissions of children hospitalized after trauma. We previously demonstrated that probabilistic linkage with Markov chain Monte Carlo parameter refinement could accurately link records of children with severe TBI without using protected health information. That work was presented at the IHDLN Conference in 2014. The objectives of this study are to 1) expand the previous linkage to include children with any type of trauma and 2) to determine the 1-year rates of Emergency Department (ED) visit and hospital readmission in children with TBI versus other trauma.
ApproachUsing the algorithm we reported previously, we linked the records of children hospitalized after trauma during 2007-2012 in the U.S. National Trauma Data Bank (NTDB) with the Pediatric Health Information System (PHIS) database.
The NTDB contains the Glasgow Coma Scale (GCS), necessary to categorize TBI. The PHIS database contains a unique identifier for each patient at each hospital, making longitudinal studies possible. Probabilistic linkage was performed using LinkSolv (Strategic Matching, Inc., Morrisonville, NY). We identified ED visits and hospital readmissions of the index cohort in the PHIS database through March 31, 2015.
ResultsAmong children hospitalized after acute trauma for >= 2 days, we found that 83% of NTDB records linked accurately. Of the children hospitalized at 28 U.S. hospitals in 19 states, 49,477 were discharged alive; 15,438 had TBI. This represents approximately 10% of U.S. pediatric TBI hospitalizations during that period. The median age was 8 years and 64% were male. The 1-year hospital readmission rate was higher (12.2%, 283/2,306) in those with both TBI and other injuries than
those with isolated TBI (8.5%, 1,120/13,132) or non-TBI injuries (8.1%, 2,790/34,039, p < 0.01 across groups). The 1-year ED visit rate differed only modestly between those three groups (14.1% versus 15.4% versus 14.2%, p < 0.01).
ConclusionsThis study leverages two linked national datasets to investigate emergency care and hospital readmission in children who survive trauma. Multivariable analyses are ongoing and will be completed by August 2016. We will generate hypotheses about potentially modifiable causes of emergency care and readmission.
Probabilistic Record Linkage (PRL) advances between Surveillance Data and HES datasets using the Expectation-Maximization (EM) algorithm
Authors: Panagiotopoulos Nikolaos , Minaji Mehdi , Pebody Richard 
Published: April 2017

ABSTRACT
ObjectivesThis study shows how to improve PRL when using a limited number of personal identifiable information (PIIs) available in HES such as NHS number, DOB, Hospital Number, Gender and Postcode. The focus is on new approaches in categorisation of m-u probabilities for fields related to Postcode. In addition, a brief outline of our procedure to generate an appropriate training dataset will be presented as well as reasons for splitting HES dataset into single and multi-admissions, and proposed techniques to avoid computational cost.
ApproachPIIs such as DOB and Postcode can be categorised in order to increase the number of potential comparison vectors which is necessary in order to deal with the multi-million size of the HES dataset. We propose a multinomial approach for comparison between postcodes both for agreement and disagreement case scenarios, based on geographical information and a similarity score resulting from a Probit model. In the absence of a ‘gold-standard’ dataset, generating an appropriate training set is possible by combining two-way Deterministic Linkage (DL) and simple random sampling. Splitting HES into single and multi-admissions is recommended for reasons related to computational performance and demographic differences of the underlying population. Selection of the lower and upper thresholds regarding linkage weights can be done in a practical way by using conditional Normal mixture modelling, or more formally by using sampling techniques based on Fellegi-Sunter decision rule. We show how the latter can be further improved avoiding the computational cost by introducing appropriate mapping functions.
ResultsWe show the advantages of PRL over DL between Surveillance Laboratory Flu Data and HES. Preliminary results indicate an additional 10% of matches for < 1% expected false positives. EM convergence problems when treating missing values as separate outcome category will be discussed comparing results with the suggested re-normalisation approach in the bibliography.
ConclusionPRL between Laboratory Surveillance Data and Hospital Episode Statistics (HES) is a common practice and performed in a number of epidemiological studies conducted at Public Health England (PHE). Therefore, improving linkage will subsequently benefit those studies. Furthermore, this study proposes new approaches for Postcode comparison, and the utilisation of mapping functions to avoid the computational cost of the Fellegi-Sunter decision rule. These approaches could be used in a wide range of applications. Finally, we suggest a structural way of handling the HES dataset in order to achieve high efficiency and robustness of linkage.