This is a peer-reviewed supplementary table for the article 'Healthcare resource utilization, costs and treatment associated with myasthenia gravis exacerbations among patients with myasthenia gravis in the USA: a retrospective analysis of claims data' published in the Journal of Comparative Effectiveness Research.Supplementary Table 1: MG treatment definitionsAim: There are limited data on the clinical and economic burden of exacerbations in patients with myasthenia gravis (MG). We assessed patient clinical characteristics, treatments and healthcare resource utilization (HCRU) associated with MG exacerbation. Patients & methods: This was a retrospective analysis of adult patients with MG identified by commercial, Medicare or Medicaid insurance claims from the IBM MarketScan database. Eligible patients had two or more MG diagnosis codes, without evidence of exacerbation or crisis in the baseline period (12 months prior to index [first eligible MG diagnosis]). Clinical characteristics were evaluated at baseline and 12 weeks before each exacerbation. Number of exacerbations, MG treatments and HCRU costs associated with exacerbation were described during a 2-year follow-up period. Results: Among 9352 prevalent MG patients, 34.4% (n = 3218) experienced ≥1 exacerbation after index: commercial, 53.0% (n = 1706); Medicare, 39.4% (n = 1269); and Medicaid, 7.6% (n = 243). During follow-up, the mean (standard deviation) number of exacerbations per commercial and Medicare patient was 3.7 (7.0) and 2.7 (4.1), respectively. At least two exacerbations were experienced by approximately half of commercial and Medicare patients with ≥1 exacerbation. Mean total MGrelated healthcare costs per exacerbation ranged from $26,078 to $51,120, and from $19,903 to $49,967 for commercial and Medicare patients, respectively. AChEI use decreased in patients with multiple exacerbations, while intravenous immunoglobulin use increased with multiple exacerbations. Conclusion: Despite utilization of current treatments for MG,MG exacerbations are associated with a high clinical and economic burden in both commercial and Medicare patients. Additional treatment options and improved disease management may help to reduce exacerbations and disease burden.

About OPCRD

Optimum Patient Care Research Database (OPCRD) is a real-world, longitudinal, research database that provides anonymised data to support scientific, medical, public health and exploratory research. OPCRD is established, funded and maintained by Optimum Patient Care Limited (OPC) – which is a not-for-profit social enterprise that has been providing quality improvement programmes and research support services to general practices across the UK since 2005.

Key Features of OPCRD

OPCRD has been purposefully designed to facilitate real-world data collection and address the growing demand for observational and pragmatic medical research, both in the UK and internationally. Data held in OPCRD is representative of routine clinical care and thus enables the study of ‘real-world’ effectiveness and health care utilisation patterns for chronic health conditions.

OPCRD unique qualities which set it apart from other research data resources: • De-identified electronic medical records of more than 24.4 million patients • OPCRD covers all major UK primary care clinical systems • OPCRD covers approximately 35% of the UK population • One of the biggest primary care research networks in the world, with over 1,175 practices • Linked patient reported outcomes for over 68,000 patients including Covid-19 patient reported data • Linkage to secondary care data sources including Hospital Episode Statistics (HES)

Data Available in OPCRD

OPCRD has received data contributions from over 1,175 practices and currently holds de-identified research ready data for over 24.4 million patients or data subjects. This includes longitudinal primary care patient data and any data relevant to the management of patients in primary care, and thus covers all conditions. The data is derived from both electronic health records (EHR) data and patient reported data from patient questionnaires delivered as part of quality improvement. OPCRD currently holds over 68,000 patient reported questionnaire data on Covid-19, asthma, COPD and rare diseases.

Approvals and Governance

OPCRD has NHS research ethics committee (REC) approval to provide anonymised data for scientific and medical research since 2010, with its most recent approval in 2020 (NHS HRA REC ref: 20/EM/0148). OPCRD is governed by the Anonymised Data Ethics and Protocols Transparency committee (ADEPT). All research conducted using anonymised data from OPCRD must gain prior approval from ADEPT. Proceeds from OPCRD data access fees and detailed feasibility assessments are re-invested into OPC services for the continued free provision of patient quality improvement programmes for contributing practices and patients.

For more information on OPCRD please visit: https://opcrd.co.uk/

The Real-World Evidence (RWE) Solutions market is experiencing robust growth, projected to reach $828.46 million in 2025 and expand at a compound annual growth rate (CAGR) of 13% from 2025 to 2033. This significant expansion is driven by several key factors. The increasing adoption of RWE in regulatory decision-making, fueled by the need for more efficient and cost-effective drug development, is a primary driver. Furthermore, the rising availability of large, diverse datasets from electronic health records (EHRs), claims databases, and wearable devices provides rich sources of real-world data for analysis. Pharmaceutical companies and healthcare providers are actively investing in RWE solutions to improve clinical trial design, enhance post-market surveillance, and optimize treatment strategies, further bolstering market growth. The market is segmented by type (e.g., software, services) and application (e.g., drug development, post-market surveillance), each exhibiting unique growth trajectories influenced by specific technological advancements and regulatory landscapes. Competitive strategies among leading companies, such as Clinigen Group Plc, ICON Plc, and IQVIA Inc., focus on strategic partnerships, technological innovation, and expansion into new geographical markets. These companies are engaged in developing advanced analytical tools and data integration platforms to cater to growing demands for comprehensive RWE solutions. The North American market currently holds a substantial share, driven by robust regulatory frameworks and advanced healthcare infrastructure. However, other regions, particularly Asia Pacific, are expected to witness significant growth in the coming years due to increasing healthcare expenditure and technological advancements. The restraints on market growth are primarily related to data privacy concerns, regulatory hurdles in accessing and utilizing real-world data, and the need for robust data standardization across different sources. However, proactive measures like developing better data security protocols, clarifying regulatory guidelines, and investing in data harmonization initiatives are mitigating these challenges. The future of the RWE Solutions market hinges on continuous technological innovation, particularly in areas like artificial intelligence (AI) and machine learning (ML), which can enhance data analysis and generate valuable insights from complex datasets. Further growth will depend on fostering collaboration among stakeholders, including regulatory bodies, healthcare providers, and technology companies, to create a more conducive environment for RWE adoption.

This database compiles information from various publically available battery cell datasheets to provide a centralized and accessible repository for technical details of various real-world battery cells, including specifications, performance metrics, and technical characteristics. Our project aims to streamline research efforts, support informed decision-making, and foster advancements in battery technology by collecting these datasheets. We do not assume any liability for the completeness, correctness, and accuracy of the information.

However, it is important to acknowledge the potential challenges of managing such a database given the still early, highly dynamic, and innovative battery market. Among others, ensuring data accuracy, data completeness, and timeliness is critical. Battery cell technologies are constantly evolving, requiring ongoing attention to maintain an up-to-date database with the latest specifications and cells. While we aimed to ensure that all records are complete, incomplete datasheets are limiting this effort and, thus, the full potential of the database. Last, standardization issues may present a challenge due to the absence of standardized reporting formats across manufacturers and countries. See "Notes" columns for comments. Unless otherwise stated, all values and parameters originate exclusively from the datasheets.

Last, we highlight that it is important to consider potential uncertainties when using the information provided in cell datasheets. The values shown are primarily derived from standardized test environments and conditions and may not accurately reflect the actual real-world performance of the cells, which may vary significantly depending on ambient conditions (foremost temperature) and charge-discharge load profiles specific to applications and embedded use cases.

BIGAN is the Big Data project of the Department of Health of the Government of Aragon, created to improve healthcare using data that are routinely collected within the public health system of Aragon. Development of the project has been entrusted to the Aragon Institute of Health Sciences (IACS).

The purpose of the project is to integrate all data collected within the health system on a technological platform, where it can be analysed by healthcare professionals, managers, educators, and researchers. The ultimate goal is to improve the healthcare system and the health of residents in Aragon through data observation. To achieve this, collection, analysis, and sharing of information between all involved stakeholders is vital.

Supplemental Material 01: Search query details

WP3 of CORE-MD investigated how to aggregate and extract maximal value for post-market surveillance from medical device registries, big data, clinical practices and experience, and the internet. This data collection was created by the Task 3.2 of the CORE-MD project, as the result of the proposed methodological framework to transform unstructured and dispersed publicly available safety information (Field Safety Notices, recalls, alerts) into a standardized and harmonized database. The databases includes 137,720 historical safety notices (updated to February 2024) safety notices published by different competent national authorities (16 EU Member States and 5 extra EU jurisdictions).

Objective: To develop a clinical informatics pipeline designed to capture large-scale structured EHR data for a national patient registry.

Materials and Methods: The EHR-R-REDCap pipeline is implemented using R-statistical software to remap and import structured EHR data into the REDCap-based multi-institutional Merkel Cell Carcinoma (MCC) Patient Registry using an adaptable data dictionary.

Results: Clinical laboratory data were extracted from EPIC Clarity across several participating institutions. Labs were transformed, remapped and imported into the MCC registry using the EHR labs abstraction (eLAB) pipeline. Forty-nine clinical tests encompassing 482,450 results were imported into the registry for 1,109 enrolled MCC patients. Data-quality assessment revealed highly accurate, valid labs. Univariate modeling was performed for labs at baseline on overall survival (N=176) using this clinical informatics pipeline.

Conclusion: We demonstrate feasibility of the facile eLAB workflow. EHR data is successfully transformed, and bulk-loaded/imported into a REDCap-based national registry to execute real-world data analysis and interoperability.

Methods eLAB Development and Source Code (R statistical software):

eLAB is written in R (version 4.0.3), and utilizes the following packages for processing: DescTools, REDCapR, reshape2, splitstackshape, readxl, survival, survminer, and tidyverse. Source code for eLAB can be downloaded directly (https://github.com/TheMillerLab/eLAB).

eLAB reformats EHR data abstracted for an identified population of patients (e.g. medical record numbers (MRN)/name list) under an Institutional Review Board (IRB)-approved protocol. The MCCPR does not host MRNs/names and eLAB converts these to MCCPR assigned record identification numbers (record_id) before import for de-identification.

Functions were written to remap EHR bulk lab data pulls/queries from several sources including Clarity/Crystal reports or institutional EDW including Research Patient Data Registry (RPDR) at MGB. The input, a csv/delimited file of labs for user-defined patients, may vary. Thus, users may need to adapt the initial data wrangling script based on the data input format. However, the downstream transformation, code-lab lookup tables, outcomes analysis, and LOINC remapping are standard for use with the provided REDCap Data Dictionary, DataDictionary_eLAB.csv. The available R-markdown ((https://github.com/TheMillerLab/eLAB) provides suggestions and instructions on where or when upfront script modifications may be necessary to accommodate input variability.

The eLAB pipeline takes several inputs. For example, the input for use with the ‘ehr_format(dt)’ single-line command is non-tabular data assigned as R object ‘dt’ with 4 columns: 1) Patient Name (MRN), 2) Collection Date, 3) Collection Time, and 4) Lab Results wherein several lab panels are in one data frame cell. A mock dataset in this ‘untidy-format’ is provided for demonstration purposes (https://github.com/TheMillerLab/eLAB).

Bulk lab data pulls often result in subtypes of the same lab. For example, potassium labs are reported as “Potassium,” “Potassium-External,” “Potassium(POC),” “Potassium,whole-bld,” “Potassium-Level-External,” “Potassium,venous,” and “Potassium-whole-bld/plasma.” eLAB utilizes a key-value lookup table with ~300 lab subtypes for remapping labs to the Data Dictionary (DD) code. eLAB reformats/accepts only those lab units pre-defined by the registry DD. The lab lookup table is provided for direct use or may be re-configured/updated to meet end-user specifications. eLAB is designed to remap, transform, and filter/adjust value units of semi-structured/structured bulk laboratory values data pulls from the EHR to align with the pre-defined code of the DD.

Data Dictionary (DD)

EHR clinical laboratory data is captured in REDCap using the ‘Labs’ repeating instrument (Supplemental Figures 1-2). The DD is provided for use by researchers at REDCap-participating institutions and is optimized to accommodate the same lab-type captured more than once on the same day for the same patient. The instrument captures 35 clinical lab types. The DD serves several major purposes in the eLAB pipeline. First, it defines every lab type of interest and associated lab unit of interest with a set field/variable name. It also restricts/defines the type of data allowed for entry for each data field, such as a string or numerics. The DD is uploaded into REDCap by every participating site/collaborator and ensures each site collects and codes the data the same way. Automation pipelines, such as eLAB, are designed to remap/clean and reformat data/units utilizing key-value look-up tables that filter and select only the labs/units of interest. eLAB ensures the data pulled from the EHR contains the correct unit and format pre-configured by the DD. The use of the same DD at every participating site ensures that the data field code, format, and relationships in the database are uniform across each site to allow for the simple aggregation of the multi-site data. For example, since every site in the MCCPR uses the same DD, aggregation is efficient and different site csv files are simply combined.

Study Cohort

This study was approved by the MGB IRB. Search of the EHR was performed to identify patients diagnosed with MCC between 1975-2021 (N=1,109) for inclusion in the MCCPR. Subjects diagnosed with primary cutaneous MCC between 2016-2019 (N= 176) were included in the test cohort for exploratory studies of lab result associations with overall survival (OS) using eLAB.

Statistical Analysis

OS is defined as the time from date of MCC diagnosis to date of death. Data was censored at the date of the last follow-up visit if no death event occurred. Univariable Cox proportional hazard modeling was performed among all lab predictors. Due to the hypothesis-generating nature of the work, p-values were exploratory and Bonferroni corrections were not applied.

Additional file 1. pubmed_literature_observational_Japan_3_years_human_2020. The list of articles identified through PubMed search includes article title, authors, journal, data source, therapeutic area, patient number, and study design.

We validated the predictive accuracy of HSUVs estimated by including clinical data from Japanese patients with MBC into the Lloyd model. The study consisted of two phases. In the first phase, we constructed a database of clinical data and HSUVs for Japanese patients with MBC in a real-world setting to evaluate the predictive accuracy of HSUVs calculated using the Lloyd model. In the second phase, we assessed how accurately predicted HSUVs (based on the Lloyd model) correlated with actual HSUVs obtained using preference-based health status measures in Japanese patients with MBC.

Health-state utility values and patient-reported outcomes (PROs)

The first phase of our study involved developing a comprehensive database of HSUVs and PROs for Japanese patients with MBC, which is linked to patients’ social background and treatment history, and PRO surveys of adverse events from anti-cancer agents using a questionnaire. The study sample included patients who attended the Outpatient Breast Clin...

The NNRD is a national resource holding real-world clinical data captured in the course of care on all admissions to NHS neonatal units in England, Wales, Scotland and the Isle of Man. Neonatal units submit data through their Electronic Patient Record system supplier. At present, there is information on around one million babies and 10 million days of care in the NNRD.

The NNRD is available to support audit, evaluations, bench-marking, quality improvement and clinical, epidemiological, health services and policy research to improve patient care and outcomes. Data in the NNRD comprise the Neonatal Data Set (ISB1595), an approved NHS Information Standard and include demographic details, daily records of interventions and treatments throughout the neonatal inpatient stay, information on diagnoses and outcomes, and follow-up health status at age two years.

The Neonatal Data Analysis Unit was founded to support the management and development of the National Neonatal Research Database (NNRD) established in 2007 by Professor Modi, and related research.

More information can be found at https://www.imperial.ac.uk/neonatal-data-analysis-unit

LifeSnaps Dataset Documentation

Ubiquitous self-tracking technologies have penetrated various aspects of our lives, from physical and mental health monitoring to fitness and entertainment. Yet, limited data exist on the association between in the wild large-scale physical activity patterns, sleep, stress, and overall health, and behavioral patterns and psychological measurements due to challenges in collecting and releasing such datasets, such as waning user engagement, privacy considerations, and diversity in data modalities. In this paper, we present the LifeSnaps dataset, a multi-modal, longitudinal, and geographically-distributed dataset, containing a plethora of anthropological data, collected unobtrusively for the total course of more than 4 months by n=71 participants, under the European H2020 RAIS project. LifeSnaps contains more than 35 different data types from second to daily granularity, totaling more than 71M rows of data. The participants contributed their data through numerous validated surveys, real-time ecological momentary assessments, and a Fitbit Sense smartwatch, and consented to make these data available openly to empower future research. We envision that releasing this large-scale dataset of multi-modal real-world data, will open novel research opportunities and potential applications in the fields of medical digital innovations, data privacy and valorization, mental and physical well-being, psychology and behavioral sciences, machine learning, and human-computer interaction.

The following instructions will get you started with the LifeSnaps dataset and are complementary to the original publication.

Data Import: Reading CSV

For ease of use, we provide CSV files containing Fitbit, SEMA, and survey data at daily and/or hourly granularity. You can read the files via any programming language. For example, in Python, you can read the files into a Pandas DataFrame with the pandas.read_csv() command.

Data Import: Setting up a MongoDB (Recommended)

To take full advantage of the LifeSnaps dataset, we recommend that you use the raw, complete data via importing the LifeSnaps MongoDB database.

To do so, open the terminal/command prompt and run the following command for each collection in the DB. Ensure you have MongoDB Database Tools installed from here.

For the Fitbit data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c fitbit

For the SEMA data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c sema

For surveys data, run the following:

mongorestore --host localhost:27017 -d rais_anonymized -c surveys

If you have access control enabled, then you will need to add the --username and --password parameters to the above commands.

Data Availability

The MongoDB database contains three collections, fitbit, sema, and surveys, containing the Fitbit, SEMA3, and survey data, respectively. Similarly, the CSV files contain related information to these collections. Each document in any collection follows the format shown below:

{ _id:

Technical notes and documentation

The Compendium of Healthcare Costs in Spain (CONCEPT-COSTS Database) is a database of Spanish healthcare unit costs estimated from different national sources.

Version 3.0 of the CONCEPT-COSTS Database contains costs estimates (expresed in EUR 2024) for a core set of service items commonly used in the chronic health problems evaluated in CONCEPT Project.

It is a living document planned to be regularly updated and expanded in terms of the covered service over time.

Aims of CONCEPT-COSTS project:

CONCEPT-COSTS is part of the coordinated CONCEPT Project, which comprises four subprojects whose objective is to analyse the effectiveness and efficiency of care pathways (CP) in three chronic health problems of high prevalence and socioeconomic impact, which are diabetes mellitus type 2, breast cancer and ischemic stroke. As a common denominator, CONCEPT shares the innovative perspective of focusing its analysis on CP as a key determinant of healthcare adequacy, adherence to treatment, health outcomes and economic consequences. CONCEPT-COSTS' first objective is to complement the results produced by each CONCEPT clinical cohort, with a broad proposal of economic analyses based on real-world data (RWD), including incurred costs, avoidable costs and efficiency evaluation of identified CP. These results will be used to inform the clinical and management decisions about those CP to be promoted or avoided. As a second objective, CONCEPT-COSTS will identify the ethodological and logistical challenges faced by economic evaluations based on RWD, to develop a framework that will include recommendations for improvements related to feasibility, validity and transferability of results.

Files included in this publication:

CONCEPT_COSTS_Database_v3.csv

CONCEPT_COSTS_Database_v3.html

Readme_v3.doc

What's new

Costs updated to 2024

Some sources updated

DEMAND: Diverse Environments Multichannel Acoustic Noise Database

A database of 16-channel environmental noise recordings

Introduction

Microphone arrays, a (typically regular) arrangement of several microphones, allow for a number of interesting signal processing techniques. The correlation of audio signals from microphones that are located in close proximity with each other can, for example, be used to determine the spatial location of sound source relative to the array, or to isolate or enhance a signal based on the direction from which the sound reaches the array.

Typically, experiments with microphone arrays that consider acoustic background noise use controlled environments or simulated environments. Such artificial setups will in general be sparse in terms of noise sources. Other pre-existing real-world noise databases (e.g. the AURORA-2 corpus, the CHiME background noise data, or the NOISEX-92 database) tend to provide only a very limited variety of environments and are limited to at most 2 channels.

The DEMAND (Diverse Environments Multichannel Acoustic Noise Database) presented here provides a set of recordings that allow testing of algorithms using real-world noise in a variety of settings. This version provides 15 recordings. All recordings are made with a 16-channel array, with the smallest distance between microphones being 5 cm and the largest being 21.8 cm.

License

This work, the audio data and the document describing it, is licensed under a Creative Commons Attribution-ShareAlike 3.0 Unported License.

The data

A description of the data and the recording equipment is provided in the file DEMAND.pdf. All recordings are available as 16 single-channel WAV files in one directory at both 48 kHz and 16 kHz sampling rates. All files are compressed into "zip" files.

Other information

The MATLAB scripts listed in the documentation can be found in the file scripts.zip.

The Authors

This work was created by Joachim Thiemann (IRISA-CNRS), Nobutaka Ito (University of Tokyo), and Emmanuel Vincent (Inria Rennes - Bretagne Atlantique). It was supported by Inria under the Associate Team Program VERSAMUS.

SEDE is a dataset comprised of 12,023 complex and diverse SQL queries and their natural language titles and descriptions, written by real users of the Stack Exchange Data Explorer out of a natural interaction. These pairs contain a variety of real-world challenges which were rarely reflected so far in any other semantic parsing dataset. The goal of this dataset is to take a significant step towards evaluation of Text-to-SQL models in a real-world setting. Compared to other Text-to-SQL datasets, SEDE contains at least 10 times more SQL queries templates (queries after canonization and anonymization of values) than other datasets, and has the most diverse set of utterances and SQL queries (in terms of 3-grams) out of all single-domain datasets. SEDE introduces real-world challenges, such as under-specification, usage of parameters in queries, dates manipulation and more.

BackgroundClinical data is instrumental to medical research, machine learning (ML) model development, and advancing surgical care, but access is often constrained by privacy regulations and missing data. Synthetic data offers a promising solution to preserve privacy while enabling broader data access. Recent advances in large language models (LLMs) provide an opportunity to generate synthetic data with reduced reliance on domain expertise, computational resources, and pre-training.ObjectiveThis study aims to assess the feasibility of generating realistic tabular clinical data with OpenAI’s GPT-4o using zero-shot prompting, and evaluate the fidelity of LLM-generated data by comparing its statistical properties to the Vital Signs DataBase (VitalDB), a real-world open-source perioperative dataset.MethodsIn Phase 1, GPT-4o was prompted to generate a dataset with qualitative descriptions of 13 clinical parameters. The resultant data was assessed for general errors, plausibility of outputs, and cross-verification of related parameters. In Phase 2, GPT-4o was prompted to generate a dataset using descriptive statistics of the VitalDB dataset. Fidelity was assessed using two-sample t-tests, two-sample proportion tests, and 95% confidence interval (CI) overlap.ResultsIn Phase 1, GPT-4o generated a complete and structured dataset comprising 6,166 case files. The dataset was plausible in range and correctly calculated body mass index for all case files based on respective heights and weights. Statistical comparison between the LLM-generated datasets and VitalDB revealed that Phase 2 data achieved significant fidelity. Phase 2 data demonstrated statistical similarity in 12/13 (92.31%) parameters, whereby no statistically significant differences were observed in 6/6 (100.0%) categorical/binary and 6/7 (85.71%) continuous parameters. Overlap of 95% CIs were observed in 6/7 (85.71%) continuous parameters.ConclusionZero-shot prompting with GPT-4o can generate realistic tabular synthetic datasets, which can replicate key statistical properties of real-world perioperative data. This study highlights the potential of LLMs as a novel and accessible modality for synthetic data generation, which may address critical barriers in clinical data access and eliminate the need for technical expertise, extensive computational resources, and pre-training. Further research is warranted to enhance fidelity and investigate the use of LLMs to amplify and augment datasets, preserve multivariate relationships, and train robust ML models.

A significant percentage of patients receiving anti-tumor necrosis factor alpha (anti-TNFα) agents lose clinical response over time. This study aims to provide representative real-world data on anti-TNFα drug sustainability, prevalence and predictors of anti-TNFα dose escalation.In this nationwide, retrospective study, patients receiving infliximab or adalimumab therapy between 2013 and 2016 were included using the administrative claims database of the Hungarian National Health Insurance Fund. Demographic characteristics, drug sustainability, dose escalation, use of parallel medications were analyzed.476 infliximab and 397 adalimumab patients were included. Dose escalation was observed in 7%, 9% and 22% of patients receiving originator/biosimilar infliximab and adalimumab during the complete follow-up, respectively. Dose escalation was associated with shorter disease duration (OR = 1.75, p = 0.026) and corticosteroid use. Drug retention rates were 62.7%, 72.3%, 75.4% after 1 year follow-up for Remicade®, Inflectra® and Humira®, which decreased to 38.3% and 52.1% for Remicade® and Humira® at 3 years. Drug sustainability was affected by steroid use prior biologic initiation in adalimumab treated patients (HR = 2.04, p < 0.001), while in infliximab treated patients dose escalation (HR = 0.51, p = 0.02) and gender (HR = 1.39, p = 0.033) were predictors of treatment discontinuation.Dose escalation rates were lower in this real-world administrative database study for both adalimumab and infliximab compared to published data. Drug retention rates were overall satisfactory, with no apparent difference between the legacy and biosimilar infliximab.

This test dataset comprised of 800 24-hour Holter recordings from the test set of the randomized clinical cohort in the paper. Each recording includes the RR-interval data that are extracted from a 24-hour dynamic 12-lead ECG recording captured by a Holter machine (DMS Holter Company, Stateline, NV, USA) at three campuses (Main Campus, Optical Valley Campus, and Sino-French New City Campus) of the Cardiac Function Examination Center (Division of Cardiology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, China). All the data were initially interpreted by primary cardiologists, then were further reviewed by three senior board-certified cardiologists to ensure the correctness of the base diagnostic labels. The cardiologist committees discussed by consensus the annotated records and provided a reference standard for model evaluation. Each atrial fibrillation(AF) episode included the accurately labeled start time and end time for patients with paroxysmal AF (PAF). The start and end times of each PAF episode were the corresponding time of the first atrial wave with an atrial rate greater than 350 beats/min and the corresponding time of the first P-wave with sinus rhythm after the termination of AF. Specifically, PAF episodes lasting more than 30 s were accurately labeled, and those lasting less than 30 s were labeled as accurately as possible. Moreover, each interval of premature beat or tachycardia was marked as “A” (atrium event) or “V” (ventricle event), and the long RR interval caused by QRS wave dropping was marked as “B” to further label Second-degree atrioventricular block. The 800 recordings included 200 whole-course AF (WAF), 200 PAF and 400 NAF recordings. For WAF patient data, whole recording included only AF signals. For NAF patients, the entire recording data had no AF signals but included normal sinus rhythm, sinus arrhythmia, atrial arrhythmia, ventricular arrhythmia, atrioventricular block and so on. The data of PAF patients included both AF episodes and NAF signals. Both WAF and PAF patients were considered to be AF patients. The type of each recording is indicated by its file name and the comments on the contents can be seen in the picture "Note.jpg".

dblp.zip

This database is used for tasks related to disambiguating author names. It contains 69,574,243 records and 10 columns and was obtained from the DBLP repository and has been preprocessed to extract all possible combinations of pairs of authors (2,665,634) unique authors) from 5,299,929 papers in the database. There are some are in the database where a single author is duplicated.

Attributes

    Record ID
    Publication ID
    Target Author
    Target Author's First Name
    Target Author's Last Name
    Co-author's First Name
    Co-author's Last Name
    Publication Title
    Year of Publication
    Source (Venue)

Note that Target Author = Target Author's First Name + Target Author's Last Name + Suffix. The suffix is added to the target author's name to ensure that it refers to a specific, unique person in the real world.

Example:

Given the following reference string:

Boukhers, Zeyd, and Asundi, Nagaraj Bahubali. "Deep Author Name Disambiguation Using Bibliographic Data." International Conference on Theory and Practice of Digital Libraries. Springer, Cham, 2022.

The following records are extracted:

    Record ID
    Publication ID
    Target Author
    Target Author's First Name
    Target Author's Last Name
    Co-author's First Name
    Co-author's Last Name
    Publication Title
    Year of Publication
    Source (Venue)


    1
    1
    Zeyd Boukhers
    Zeyd
    Boukhers
    Zeyd
    Boukhers
    Deep Author Name Disambiguation Using Bibliographic Data
    2022
    International Conference on Theory and Practice of Digital Libraries


    2
    1
    Zeyd Boukhers
    Zeyd
    Boukhers
    Nagaraj Bahubali
    Asundi
    Deep Author Name Disambiguation Using Bibliographic Data
    2022
    International Conference on Theory and Practice of Digital Libraries


    3
    1
    Nagaraj Bahubali Asundi001
    Nagaraj Bahubali
    Asundi
    Zeyd
    Boukhers
    Deep Author Name Disambiguation Using Bibliographic Data
    2022
    International Conference on Theory and Practice of Digital Libraries


    4
    1
    Nagaraj Bahubali Asundi001
    Nagaraj Bahubali
    Asundi
    Nagaraj Bahubali
    Asundi
    Deep Author Name Disambiguation Using Bibliographic Data
    2022
    International Conference on Theory and Practice of Digital Libraries

data.zip

It contains pickle files in the format

[

For example, 4_T Akutsu.pickle contains

['T Akutsu', 2274176, 2276257, 2290454, 2347757]

indices.zip

It contains two dictionaries:

index2auth.pickle: The real world author (not the name) is retrieved given the index

auth2index.pickle: The author index is retrieved given the real world author (not the name)

Utils.zip

It contains other necessary pickle files:

author_list.pickle: it contains the list of all authors (2665634 authors)

author_abbvs.pickle: it contains the list of atomic names of all authors (2665634 authors)

author_names.pickle; it contains the list of full names of all authors (2665634 authors)

unique.pickle: it contains the list of unique atomic names (1555517 atomic names)

unique_full_names.pickle: it contains the list of unique full names (2629851 full names)

indices.pickle: it contains the indices of authors who share the atomic name (1555517 atomic names)

Evaluation_Data.zip

It contains an example of training, validation and test data.

x_train.pickle: contains the indices of the training records

x_validate.pickle: contains the indices of the validation records

x_test.pickle: contains the indices of the testing records

y_train.pickle: contains the indices of the corresponding authors to the records in x_train.pickle

y_validate.pickle: contains the indices of the corresponding authors to the records in x_validate.pickle

y_test.pickle: contains the indices of the corresponding authors to the records in x_test.pickle

Miscs.zip

It contains data extracted from the database to be used for training and testing. The files contains data for one example atomic name. It contains the following pickle files:

records_train.pickle: contains the training records in the following format:

combination_id reference_id target_author author_fname author_lname coauthor_fname coauthor_lname title year abbr_journal journal

ref_train.pickle: contains the indices of the training records.

old_authors_ids.pickle

new_authors_ids.pickle

Initially this data is collected during a patient's time at hospital as part of the Commissioning Data Set (CDS). This is submitted to NHS Digital for processing and is returned to healthcare providers as the Secondary Uses Service (SUS) data set and includes information relating to payment for activity undertaken. It allows hospitals to be paid for the care they deliver. This same data can also be processed and used for non-clinical purposes, such as research and planning health services. Because these uses are not to do with direct patient care, they are called 'secondary uses'. This is the SUS data set. SUS data covers all NHS Clinical Commissioning Groups (CCGs) in England, including: • private patients treated in NHS hospitals • patients resident outside of England • care delivered by treatment centres (including those in the independent sector) funded by the NHS Each SUS record contains a wide range of information about an individual patient admitted to an NHS hospital, including: • clinical information about diagnoses and operations • patient information, such as age group, gender and ethnicity • administrative information, such as dates and methods of admission and discharge • geographical information such as where patients are treated and the area where they live NHS Digital apply a strict statistical disclosure control in accordance with the NHS Digital protocol, to all published SUS data. This suppresses small numbers to stop people identifying themselves and others, to ensure that patient confidentiality is maintained.

Who SUS is for SUS provides data for the purpose of healthcare analysis to the NHS, government and others including:

The Secondary Users Service (SUS) database is made up of many data items relating to A&E care delivered by NHS hospitals in England. Many of these items form part of the national Commissioning Data Set (CDS), and are generated by the patient administration systems within each hospital. • national bodies and regulators, such as the Department of Health, NHS England, Public Health England, NHS Improvement and the CQC • local Clinical Commissioning Groups (CCGs) • provider organisations • government departments • researchers and commercial healthcare bodies • National Institute for Clinical Excellence (NICE) • patients, service users and carers • the media

Uses of the statistics The statistics are known to be used for: • national policy making • benchmarking performance against other hospital providers or CCGs
• academic research • analysing service usage and planning change • providing advice to ministers and answering a wide range of parliamentary questions • national and local press articles • international comparison More information can be found at https://digital.nhs.uk/data-and-information/data-tools-and-services/data-services/hospital-episode-statistics https://digital.nhs.uk/data-and-information/publications/statistical/hospital-accident--emergency-activity"