bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identify Patients with Congestive Heart Failure through Analyzing Free-Text Clinical Notes
Authors: Yann Margot , Stukel Therese , Jaakkimainen Liisa , Tu Karen 
Published: September 2018

IntroductionA number of challenges exist in analyzing unstructured free text data in electronic medical records (EMRs). EMR text are difficult to represent and model due to their high dimensionality, heterogeneity, sparsity, incompleteness, random errors and the presence of noise.
Objectives and ApproachStandard Natural Language Processing (NLP) tools make errors when applied to clinical notes due to physician use of unconventional language, involving polysemy, abbreviations, ambiguity, misspelling, variations, and negation.
This paper presents a novel NLP framework, “Clinical Learning On Natural Expression” (CLONE), to automatically learn from a large primary care EMR database, analyzing free text clinical notes from primary care practices. CLONE’s predictive clinical models using text mining and neural network approach to extract features to identify patterns. To demonstrate effectiveness, we evaluate CLONE’s ability in a case study to identify patients with a specific chronic condition: congestive heart failure (CHF).
ResultsA random selected sample of 7500 patients from Electronic Medical Record Administrative data Linked Database (EMRALD) is used. In this dataset, each patient’s medical chart includes a reference standard, manually reviewed by medical practitioners. Prevalence of CHF is approximately 2%. The low prevalence leads to another challenging problem in machine learning: imbalanced datasets. After pre-processing, we build deep learning models to represent and extract important medical information from free text to identify CHF patients through analyzing patient charts. We evaluated the effectiveness of CLONE by comparing the predicted labels with the standard references on a holdout test dataset. Comparing it with a number of alternative algorithms, we improve the overall accuracy to over 90% on a test dataset.
Conclusion/ImplicationsAs the role of NLP in EMR data expands, the CLONE natural language processing framework can lead to substantial reduction in manual processing, while improving predictive accuracy.
Commercial use of health data in the dock – results of a public “trial”
Authors: Tully Mary , Hassan Lamiece , Oswald Malcolm , Ainsworth John 
Published: September 2018

IntroductionSurveys suggest that there is a dichotomy in how citizens view research for public good and research for commercial gain. As a consequence, the idea that a research initiative, such as a learning health system, for both public and commercial benefit may be controversial and reduce public trust.
Objectives and ApproachThis study aims to investigate what informed citizens considered to be appropriate uses of health data in a learning health system. Two paired four-day juries were run, with different jurors but the same purpose, expert witnesses and facilitators. Overall, 694 people applied to be jurors; 36 were selected to match criteria based on national demographics and their prior privacy views. Jurors considered whether and why eight exemplar data uses of depersonalised patient data were acceptable. The exemplars were data uses planned by the learning health system initiative to improve care pathways (planned uses), and possible unplanned data uses.
ResultsAll planned uses were considered appropriate by most, but not all, jurors, as they had the potential of benefitting the public through improving care. Positive health outcomes were more acceptable than improved efficiency of services, given jurors prior beliefs about how the NHS operates raising concerns about whether improving efficiency would lead to inequitable distribution or closure of services. The potential uses were considered appropriate where there were improvements in drugs, treatments, or lower NHS costs. Some jurors became more accepting of commercial uses as they understood them better. Commercial uses that prioritised generating profit and did not produce health benefits for the public were unacceptable, regardless of any safeguards for the data. Commercial gain that occurred secondary to achieving public benefit were generally accepted.
Conclusion/ImplicationsJuries elicit more informed and nuanced judgement from citizens than surveys. Jurors tended to be more accepting of data sharing to both private and public sector after the jury process. Many jurors accept commercial gain if public benefit is achieved. Some were suspicious of data sharing for efficiency gains.
Monitoring health service use at the end of life in the Calgary Zone of Alberta: a Population-level analysis linking multiple administrative datasets
Authors: Cai Pin , Fong Andrew , Sinnarajah Aynharan 
Published: September 2018

IntroductionAs part of the Alberta Health Services (AHS) Calgary Zone Healthcare planning, a Palliative and End of Life Care Program (PEOLC) dashboard was developed and face validity of the indicators was examined by key stakeholders such as clinicians and decision makers.
Objectives and ApproachAn internal dashboard was developed to explore End of Life (EOL) indicators that could provide evidence to support local PEOLC planning. Multiple administrative datasets available to AHS were used to estimate population needs of palliative care, current state of resource use, and EOL quality indicators. Underlying cause of death in Vital Statistics data was used to calculate minimal and maximal population estimates of palliative care needs between 2000 and 2014. Trends in acute care use during the last year of life were analyzed. Overall rates and geographic variations of selected indicators in Calgary Zone were reported.
ResultsOver the period 2000 to 2014, number of adult deaths increased in Calgary Zone, from 5,094 in 2000 to 6,823 in 2014. In 2015/16, about half of all 10,848 hospital discharges in the last year of life were incurred in the last 60 days of life, and about 40 percent were incurred in the last 30 days. Overall, 11% of decedents visited ED more than once, 7% were discharged from hospital more than once, 19% spent more than 14 days in hospital. According to the ED and inpatient data, 40.7% of decedents, roughly 3,000 people, died in hospital. We observed an urban rural continuum gradient in most of these indicators, with rates varying more than two-fold for ED and hospital discharge related indicators.
Conclusion/ImplicationsThe project demonstrates the feasibility of using existing data to generate information to support the PEOLC program planning in Calgary Zone. With early stakeholder engagement in dashboard design, analysis, interpretation, and dissemination, the dashboard was well received and will be updated as more recent data becomes available.
Factors associated with the breast cancer diagnostic interval across five Canadian provinces: a CanIMPACT study
Authors: McBride Mary , Groome Patti , Decker Kathleen , Grunfeld Eva , Jiang Li , Kendell Cynthia , Urquhart Robin , Vu Khanh , Winget Marcy 
Published: September 2018

IntroductionA long breast cancer diagnostic process can affect patient anxiety and survival. Variations in the length of the diagnostic interval for similar patient presentations can indicate health system inequities and/or inefficiencies.
Objectives and ApproachWe describe the breast cancer diagnostic interval across Canada and factors associated with its length.
We studied breast cancer patients diagnosed from 2004/7 to 2010/11/12 in the Canadian provinces: British Columbia, Alberta, Manitoba, Ontario, and Nova Scotia. Using administrative data, we created parallel population-based, provincial-level datasets and ran common analyses. The diagnostic interval was defined from the screening mammogram to the diagnosis for screen-led and from the first referral/test ordering date to the diagnosis for diagnostic-led patients. Stratified by these two diagnostic routes, we describe the variation in the interval across provinces and report on the province-specific associations between the diagnostic interval and: patient age, comorbid disease burden, socioeconomic status combined with rural residence, and continuity of primary care while controlling for cancer stage.
ResultsThe median diagnostic interval varied by 6 days (29 to 35 days) across provinces. Screen-led patients were diagnosed more quickly (median 2-12 days quicker). The 90th percentile diagnostic interval was 84-126 days longer in diagnostic-led patients. In the diagnostic-led group, increasing comorbid burden was consistently associated with longer diagnostic intervals and being >70 was associated with a shorter interval at the 90th percentile in Manitoba and Ontario. There was no evidence of a clear rural or low socioeconomic status effect and patients without a primary care physician had shorter intervals. In the screen-led group, patients age 40-49 and those in the medium or low income rural areas waited longer for a diagnosis.
Conclusion/ImplicationsDiagnostic wait times differ across Canada and are variably associated with comorbidity, age, area-level socioeconomic status and rural residence. These results point to practice and system-level effects that warrant further study.
Linking pharmacy dispensing data to other administrative health datasets to measure the compliance and effectiveness of RSV immunoprophylaxis
Authors: Moore Hannah , Strunk Tobias , Abdalla Tasnim , Snelling Tom , Richmond Peter , Keil Anthony , De Klerk Nicholas 
Published: September 2018

IntroductionRespiratory Syncytial Virus (RSV) causes considerable morbidity in children. RSV vaccines are in development, but the only current preventive measure is immunoprophylaxis with monoclonal antibody, palivizumab. Australia has no uniform palivizumab guidelines. In Western Australia palivizumab is licensed for use in high risk children but compliance and effectiveness is unknown.
Objectives and ApproachWe conducted a retrospective cohort study using palivizumab data from multiple pharmacy dispensing datasets which had been linked with routine laboratory, hospital morbidity, emergency department presentations, deaths and perinatal data for a cohort of infants admitted to Level 3 Neonatal Intensive Care Units (NICU) between 2002 and 2013. We identified palivizumab eligible infants as those who were extremely premature (<28 weeks gestation) with bronchopulmonary dysplasia and/or who identified as Indigenous and were NICU inpatients during the annual winter RSV season (May-October). We describe the use of palivizumab in infants that did and did not fit the eligibility criteria.
ResultsThe NICU cohort included 24,367 infants, of which 1754 had at least 1 RSV-confirmed infection before age 5 years. A total of 686 (2.8%) cohort infants were eligible for palivizumab. Palivizumab dispensing data were amalgamated from 5 pharmacy datasets. Overall, 173 of the palivizumab eligible infants (25.2%) had at least 1 palivizumab dose (27% 1 dose, 34% 2 doses, 28% 3 doses and 11% 4 or more doses). From 2011 when palivizumab guidelines were formalised, 143 (75%) had at least 1 dose. Compliance with at least 1 palivizumab dose was highest in 2011 (84.9%). From 2002-2013, 98 infants were given palivizumab outside eligibility criteria (33% 1 dose, 33% 2 doses, 34% 3 or more doses) with annual use increasing since 2008.
Conclusion/ImplicationsThis is the first time pharmacy dispensing data have been linked to other datasets to measure use and effectiveness. Compliance with palivizumab guidelines was high from 2011. These data will be used to measure the effectiveness of palivizumab against RSV-confirmed infections and respiratory infection-related hospitalisations up to age 5 years.
Development and validation of case-finding algorithms for recurrence of breast cancer using routinely collected administrative data
Authors: Xu Yuan , Kong Shiying , Quan May Lynn 
Published: September 2018

IntroductionRecurrence free survival is frequently investigated in cancer outcome studies, however is not explicitly documented in cancer registry data that is widely used for research. Patterns of events after initial treatment such as oncology visits, re-operation, chemotherapy or radiation may herald recurrence.
Objectives and ApproachThis study aimed to develop and validate algorithms for identifying breast cancer recurrence using large administrative data.Two cohorts with high recurrence rates were used: 1) all young (≤ 40 years) breast cancer patients (2007-2010), and 2) all neoadjuvant chemotherapy patients (2012-2014) in Alberta, Canada. Health events after primary treatment were obtained from the Alberta cancer registry, physician billing claims, and vital statistics databases. Positive recurrence status (defined as either locoregional, distant or both) was ascertained by primary chart review. The cohort was divided into a developing (60%) and validating (40%) set. Development of algorithms geared towards high sensitivity, PPV and accuracy respectively were performed using classification and regression tree (CART) models. Key variables in the models included: a new round of chemotherapy, a second mastectomy, and a new cluster of radiologist, oncologist or general surgeon visits occurring after the primary treatment. Compared with chart review data, the sensitivity, specificity, PPV, NPV and accuracy of the algorithms were calculated.
ResultsOf 606 patients, 121 (20%) had recurrence after a median follow-up 4 years. The high sensitivity algorithm had 94.2% (95% CI: 90.1-98.4%) sensitivity, 92.8% (90.5-95.1%) specificity, 76.5% (70.0-88.3%) PPV, 98.5% (97.3-99.6%) NPV and 93.1% (91.0-95.1%) accuracy. The high PPV algorithm had 74.4% (66.6-82.2%) sensitivity, 97.8% (96.5-99.2%) specificity, 90.0% (84.1-95.9%) PPV, 93.6% (91.4-95.7%) NPV and 92.9% (90.9-95.0%) accuracy. The high accuracy algorithm had 88.4% (82.7-94.1%) sensitivity, 97.1% (95.6-98.6%) specificity, 88.4% (82.7-94.1%) PPV, 97.1% (95.6-98.6%) NPV and 95.4% (93.7-97.1%) accuracy.
Conclusion/ImplicationsThe proposed algorithms achieved favourably high validity for identifying recurrence using widely available administrative data. Further study may be needed for improving sensitivity and PPV, and validating the algorithms in larger data for widespread use.
Prevalence and incidence of diagnosed hypertension in Alberta, Canada
Authors: Holitzki Hannah , Quan Hude , Clement Fiona , Leung Alex , Liang Zhiyang 
Published: September 2018

IntroductionThe prevalence of diagnosed hypertension in Canada is projected to increase despite the incidence rate decreasing. Previous work around the world has utilized survey data to provide estimates of prevalence and incidence. Administrative data is population-level, and may provide more reliable estimates of provincial prevalence and incidence than could be achieved using survey data.
Objectives and Approach

To produce age and sex-specific prevalence and incidence estimates of diagnosed hypertension in Alberta from 2007 to 2015,
To project estimates to the fiscal year of 2019/2020.

Data from the Discharge Abstract Database, physician claims database, National Ambulatory Care Reporting System, and provincial health insurance registry will be linked using unique anonymous personal identifier and gender. A validated case definition of diagnosed hypertension for use in administrative datasets will be used to identify annual prevalent and incident cases from claims data. Obstetric cases will be excluded. The provincial health insurance registry will be used to estimate denominator values.
ResultsResults of this analysis are not available for the time of abstract submission as the timeline for this analysis projects completion in April 2018.
Conclusion/ImplicationsMaintained surveillance of diagnosed hypertension is important to inform health policy and spending decisions, to monitor efficacy of public health interventions, and to inform patient care. Furthermore, diagnosis guidelines have been updated since 2017. Providing estimates for the prevalence of diagnosed hypertension in Alberta five years into the future to compare to actual prevalence estimates may indicate whether changes in prevalence are due to actual changes in health status or to changes in diagnosis guidelines.
Primary health care engagement among marginalized people who use drugs in Ottawa, Canada
Authors: Donelle Jessy , Bayoumi Ahmed , Boucher Lisa , Martin Alana , Pineau Dave , Diliso Nicola , Renaud Brad , Boyd Rob , Oickle Pam , Marshall Zack , LeBlanc Sean , Tyndall Mark , Kendall Claire 
Published: September 2018

IntroductionEngagement in primary health care may be lower among marginalized people who use drugs (PWUD) compared to the general population, despite having greater mental and physical healthcare needs as evidenced by higher co-morbidity, and more frequent use of emergency department care.
Objectives and ApproachWe investigated which socio-structural factors were related to primary care engagement among PWUD using rich survey data from the Participatory Research in Ottawa: Understanding Drugs cohort study; these data were deterministically linked to several robust provincial-level health administrative databases held at the Institute for Clinical Evaluative Sciences. We defined primary care engagement over the 2 years prior to survey completion (March-December 2013) as: not engaged (<3 outpatient visits to the same family physician) versus engaged in care (3+ outpatient visits to the same family physician). Multi-variable logistic regression was used to identify factors associated with primary health care engagement.
ResultsAmong 663 participants, characteristics include: mean age of 41.4 years, 75.6% male, 66.7% in the lowest two income quintiles, and 51.1% with 6+ co-morbidities. 372 (56%) were engaged in primary care, with a mean of 15.97 visits per year (SD=20.18). Engagement was significantly associated with the following factors: receiving drug benefits from either the Ontario Disability Support Program (adjusted odds ratio [AOR] 4.48; 95% confidence interval [95%CI] 2.64 to 7.60) or Ontario Works (AOR 3.41; 95%CI 1.96 to 5.91), having ever taken methadone (AOR 3.05; 95%CI 1.92 to 4.87), mental health co-morbidity (AOR 2.93; 95%CI 1.97 to 4.36), engaging in sex work in the last 12 months (AOR 2.05; 95%CI 1.01 to 4.13), and having stable housing (AOR 1.98; 95%CI 1.30 to 3.01).
Conclusion/ImplicationsNearly half of PWUD are not engaged in primary care, representing missed opportunities to improve health. Engagement in primary care may reflect both an increased need for health care, such as mental health disability, and increased access to primary care through other health and social services, such as housing support.
Comparing five comorbidity indices to predict mortality in chronic kidney disease
Authors: McArthur Eric , Bota Sarah , Sood Manish , Nesrallah Gihad , Kim Joseph , Garg Amit , Dixon Stephanie 
Published: September 2018

IntroductionMany reports which use healthcare databases adjust the results for patient comorbidity. Several different indices were developed in the general population to summarize patient comorbidity. How well these indices predict one-year all-cause mortality in individuals with chronic kidney disease (CKD) is not well known.
Objectives and ApproachWe accrued three groups of patients in Ontario, Canada between the years 2004 and 2014, at the time they first received a kidney transplant, received maintenance dialysis, or were confirmed to have an estimated glomerular filtration rate (eGFR) less than 45 mL/min per 1.73 m2. We compared five comorbidity indices: Charlson comorbidity index, end-stage renal disease-modified Charlson comorbidity index, Johns Hopkins’ Aggregated Diagnosis Groups score, Elixhauser score, and Wright-Khan index. Each group was randomly divided 100 times into derivation and validation samples. Model discrimination was assessed using median c-statistics from logistic regression models, and calibration was evaluated graphically using calibration plots.
ResultsWe identified 4,111 kidney transplant recipients, 23,897 individuals receiving maintenance dialysis, and 181,425 individuals with moderate CKD. Within one year, 108 (2.6%), 4,179 (17.5%), and 17,898 (9.9%) in each group had died, respectively. In the validation sample, model discrimination was inadequate for all five comorbidity indices, with median c-statistics less than 0.7 for all three groups. Calibration was also poor for all models.
Conclusion/ImplicationsExisting comorbidity indices do not accurately predict one-year mortality in patients with CKD. Current indices could be modified with additional risk factors to improve their performance in CKD, or a new index could be developed for this population.
Distributed NLP Framework to create new federated data-sets
Authors: Thompson Simon 
Published: September 2018

IntroductionAlthough healthcare systems generate significant amounts of structured data, there remains a untapped wealth of unstructured narrative data. In the UK, 70% of all NHS digital information is in unstructured form. The NHS has no plans to computerise this data, as it is simply would not be cost effective.
Objectives and ApproachOur aim was to make all digitised free text within partner organisations accessible for NLP processing for research, while overcoming information governance challenges. We developed a distributed GATE-based NLP platform enabling NLP models to be automatically distributed and materialised against the free text data in each organisation to create new conventional datasets, which can then be transmitted back using an established governance model.
This work adds NLP capability to the UK’s National Research Data Appliances, deployed throughout Wales and beyond and uses many open source components enabling a deployment without additional software licence costs, leading to increased potential use cases.
ResultsWe have been able to demonstrate a fully federated network of analytical nodes into NHS Wales, which takes the analytical NLP model to the free text data, as opposed to the data having to travel. Under a common, acceptable, governance model, an approval system enables organisations such as health boards to give permission for projects and NLP models to be used against their data.
In a proof of concept project, we have run a number of NLP models over large numbers of documents, which the platform has ingested, converted and analysed.
We have developed a proposal for a common NLP model definition format to enable models to be interchangeable between different research groups and systems. Sharing/discovery of established NLP models is key deliverable.
Conclusion/ImplicationsThe implications of being able to send the query to the data, enables access to this untapped data source, finally enabling the realisation of new datasets, while abiding by any IG framework. The low cost and simplicity will enable a many research opportunities, some of which are already being realised.