The Clinical Trials Registry and Results Database compiles information on publicly and privately supported clinical trial studies on a wide range of diseases and conditions. Its main goal is to provide an easy access to both privately and publicly funded clinical trials information for patients, their family members, healthcare professionals, researchers, and the public.

MIMIC-III is a large, freely-available database comprising deidentified health-related data associated with over forty thousand patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. The database includes information such as demographics, vital sign measurements made at the bedside (~1 data point per hour), laboratory test results, procedures, medications, caregiver notes, imaging reports, and mortality (including post-hospital discharge).MIMIC supports a diverse range of analytic studies spanning epidemiology, clinical decision-rule improvement, and electronic tool development. It is notable for three factors: it is freely available to researchers worldwide; it encompasses a diverse and very large population of ICU patients; and it contains highly granular data, including vital signs, laboratory results, and medications.

The National Database for Clinical Trials Related to Mental Illness (NDCT) is an extensible informatics platform for relevant data at all levels of biological and behavioral organization (molecules, genes, neural tissue, behavioral, social and environmental interactions) and for all data types (text, numeric, image, time series, etc.) related to clinical trials funded by the National Institute of Mental Health. Sharing data, associated tools, methodologies and results, rather than just summaries or interpretations, accelerates research progress. Community-wide sharing requires common data definitions and standards, as well as comprehensive and coherent informatics approaches for the sharing of de-identified human subject research data. Built on the National Database for Autism Research (NDAR) informatics platform, NDCT provides a comprehensive data sharing platform for NIMH grantees supporting clinical trials.

Abstract The Medical Information Mart for Intensive Care (MIMIC)-IV database is comprised of deidentified electronic health records for patients admitted to the Beth Israel Deaconess Medical Center. Access to MIMIC-IV is limited to credentialed users. Here, we have provided an openly-available demo of MIMIC-IV containing a subset of 100 patients. The dataset includes similar content to MIMIC-IV, but excludes free-text clinical notes. The demo may be useful for running workshops and for assessing whether the MIMIC-IV is appropriate for a study before making an access request.

Background The increasing adoption of digital electronic health records has led to the existence of large datasets that could be used to carry out important research across many areas of medicine. Research progress has been limited, however, due to limitations in the way that the datasets are curated and made available for research. The MIMIC datasets allow credentialed researchers around the world unprecedented access to real world clinical data, helping to reduce the barriers to conducting important medical research. The public availability of the data allows studies to be reproduced and collaboratively improved in ways that would not otherwise be possible.

Methods First, the set of individuals to include in the demo was chosen. Each person in MIMIC-IV is assigned a unique subject_id. As the subject_id is randomly generated, ordering by subject_id results in a random subset of individuals. We only considered individuals with an anchor_year_group value of 2011 - 2013 or 2014 - 2016 to ensure overlap with MIMIC-CXR v2.0.0. The first 100 subject_id who satisfied the anchor_year_group criteria were selected for the demo dataset.

All tables from MIMIC-IV were included in the demo dataset. Tables containing patient information, such as emar or labevents, were filtered using the list of selected subject_id. Tables which do not contain patient level information were included in their entirety (e.g. d_items or d_labitems). Note that all tables which do not contain patient level information are prefixed with the characters 'd_'.

Deidentification was performed following the same approach as the MIMIC-IV database. Protected health information (PHI) as listed in the HIPAA Safe Harbor provision was removed. Patient identifiers were replaced using a random cipher, resulting in deidentified integer identifiers for patients, hospitalizations, and ICU stays. Stringent rules were applied to structured columns based on the data type. Dates were shifted consistently using a random integer removing seasonality, day of the week, and year information. Text fields were filtered by manually curated allow and block lists, as well as context-specific regular expressions. For example, columns containing dose values were filtered to only contain numeric values. If necessary, a free-text deidentification algorithm was applied to remove PHI from free-text. Results of this algorithm were manually reviewed and verified to remove identified PHI.

Data Description MIMIC-IV is a relational database consisting of 26 tables. For a detailed description of the database structure, see the MIMIC-IV Clinical Database page [1] or the MIMIC-IV online documentation [2]. The demo shares an identical schema and structure to the equivalent version of MIMIC-IV.

Data files are distributed in comma separated value (CSV) format following the RFC 4180 standard [3]. The dataset is also made available on Google BigQuery. Instructions to accessing the dataset on BigQuery are provided on the online MIMIC-IV documentation, under the cloud page [2].

An additional file is included: demo_subject_id.csv. This is a list of the subject_id used to filter MIMIC-IV to the demo subset.

Usage Notes The MIMIC-IV demo provides researchers with the opportunity to better understand MIMIC-IV data.

CSV files can be opened natively using any text editor or spreadsheet program. However, as some tables are large it may be preferable to navigate the data via a relational database. We suggest either working with the data in Google BigQuery (see the "Files" section for access details) or creating an SQLite database using the CSV files. SQLite is a lightweight database format which stores all constituent tables in a single file, and SQLite databases interoperate well with a number software tools.

Code is made available for use with MIMIC-IV on the MIMIC-IV code repository [4]. Code provided includes derivation of clinical concepts, tutorials, and reproducible analyses.

Release Notes Release notes for the demo follow the release notes for the MIMIC-IV database.

Ethics This project was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center (Boston, MA) and the Massachusetts Institute of Technology (Cambridge, MA). Requirement for individual patient consent was waived because the pr...

MIMIC-II documents a diverse and large population of intensive care unit patient stays and contains comprehensive and detailed clinical data, including physiological waveforms and minute-by-minute trends for a subset of records. It establishes a unique public-access resource for critical care research, supporting a diverse range of analytic studies spanning epidemiology, clinical decision-rule development, and electronic tool development. The MIMIC-II Clinical Database, although de-identified, still contains detailed information regarding the clinical care of patients, and must be treated with appropriate care and respect.

Clinical Trials Database (CTA), is providing to the public a listing of specific information relating to phase I, II and III clinical trials in patients. The database is managed by Health Canada and provides a source of information about Canadian clinical trials involving human pharmaceutical and biological drugs.

Objective: In public health, access to research literature is critical to informing decision making and to identify knowledge gaps. However, identifying relevant research is not a straightforward task since public health interventions are often complex, can have positive and negative impacts on health inequalities and are applied in diverse and rapidly evolving settings. We developed a ‘living’ database of public health research literature to facilitate access to this information using Natural Language Processing tools. Materials and Methods: Classifiers were identified to identify the study design (e.g. cohort study or clinical trial) and relationship to factors that may be relevant to inequalities using the PROGRESS-Plus classification scheme. Training data was obtained from existing MEDLINE labels and from a set of systematic reviews in which studies were annotated with PROGRESS-Plus categories. Results: Evaluation of the classifiers showed that the study type classifier achieved average precision and recall of 0.803 and 0.930 respectively. The PROGRESS-Plus classification proved more challenging with average precision and recall of 0.608 and 0.534. The FAIR database uses information provided by these classifiers to facilitate access to inequality-related public health literature. Discussion: Previous work on automation of evidence synthesis has focussed on clinical areas rather than public health, despite the need being arguably greater. Conclusion: The development of the FAIR databased demonstrates that it is possible to create a publicly accessible and regularly updated database of public health research literature focused on inequalities. The database is freely available (https://eppi.ioe.ac.uk/eppi-vis/Fair). Methods 1978 papers that had been included in systematic reviews previously were identified for training and testing the machine learning model. Please see the paper and website for further information.

Abstract

The MarketScan® Research Databases aggregate claims & enrollment data from commercial, federal, state and public health plans, linking paid claims to real-world data for healthcare research, economics and treatment outcomes for ~300m patients.

Methodology

The Merative™ MarketScan® Research Databases capture person-specific clinical utilization, expenditures, and enrollment across inpatient, outpatient, prescription drug, and carve-out services. The data come from a selection of large employers, health plans, and government and public organizations. The MarketScan Research Databases link paid claims and encounter data to detailed patient information across sites and types of providers and over time. The annual medical databases include private-sector health data from approximately 350 payers. Historically, more than 20 billion service records are available in the MarketScan databases. These data represent the medical experience of insured employees and their dependents for active employees, early retirees, Consolidated Omnibus Budget Reconciliation Act (COBRA) continuees, and Medicare-eligible retirees with employer-provided Medicare Supplemental and Medicare Advantage plans.

Sixteen FDA-approved drugs were investigated to elucidate their mechanisms of action (MOAs) and clinical functions by pathway analysis based on retrieved drug targets interacting with or affected by the investigated drugs. Protein and gene targets and associated pathways were obtained by data-mining of public databases including the MMDB, PubChem BioAssay, GEO DataSets, and the BioSystems databases. Entrez E-Utilities were applied, and in-house Ruby scripts were developed for data retrieval and pathway analysis to identify and evaluate relevant pathways common to the retrieved drug targets. Pathways pertinent to clinical uses or MOAs were obtained for most drugs. Interestingly, some drugs identified pathways responsible for other diseases than their current therapeutic uses, and these pathways were verified retrospectively by in vitro tests, in vivo tests, or clinical trials. The pathway enrichment analysis based on drug target information from public databases could provide a novel approach for elucidating drug MOAs and repositioning, therefore benefiting the discovery of new therapeutic treatments for diseases.

Abstract

The MarketScan® Research Databases aggregate claims & enrollment data from commercial, federal, state and public health plans, linking paid claims to real-world data for healthcare research, economics and treatment outcomes for ~300m patients.

Methodology

The Merative™ MarketScan® Research Databases capture person-specific clinical utilization, expenditures, and enrollment across inpatient, outpatient, prescription drug, and carve-out services. The data come from a selection of large employers, health plans, and government and public organizations. The MarketScan Research Databases link paid claims and encounter data to detailed patient information across sites and types of providers and over time. The annual medical databases include private-sector health data from approximately 350 payers. Historically, more than 20 billion service records are available in the MarketScan databases. These data represent the medical experience of insured employees and their dependents for active employees, early retirees, Consolidated Omnibus Budget Reconciliation Act (COBRA) continuees, and Medicare-eligible retirees with employer-provided Medicare Supplemental and Medicare Advantage plans.

This dataset is the main file to construct the FDA (U.S. Food and Drug Administration) Postmarketing Requirements and Commitments searchable database. Postmarketing requirements refers to studies required to be conducted under statutes or regulations after product approval. Postmarketing commitments are not required studies that sponsors conduct. Official FDA's website has an available database to provide public detailed information on postmarketing requirements and commitments studies.

Data format and access

The database consists of full-text patient reviews, reflecting their dissatisfaction with healthcare quality. Materials in Russian have been posted in the «Review list» of the site infodoctor.ru. Publication period: July 2012 to August 2023. The database consists of 18,492 reviews covering 16 Russian cities with population of over one million. Data format: .xlsx.

Data access: 10.5281/zenodo.15257447

Data collection methodology

Based on the fact that negative reviews may be more reliable than positive ones, the authors carried out negative reviews from 16 Russian cities with a population of over one million, for which it was possible to collect representative samples (at least 1000 reviews for each city). We have extracted reviews from the one-star section of this site's guestbook, as they are reliably identified as negative. Duplicates were removed from the database. Personal data in comment texts have been replaced with "##########". The author's gender was determined manually based on his/her name or gender endings in the texts of reviews. Otherwise, we indicated "0" - gender cannot be determined.

For Moscow reviews, classification was carried out using manual markup methods - based on the majority of votes for the review class from 3 annotators (if at least one annotator indicated that it was impossible to determine, the review was classified as #N/A - impossible to clearly determine). For reviews from other cities, classification was made into 3 classes using machine learning methods based on logistic regression. The classification accuracy was 88%.

The medical specialties were distributed into large groups for the convenience of further analysis. The correspondence of medical specialties to large groups is presented in detail in Appendix 1.

Sample structure and description of variables

· CITY – the name of a city with a population of over a million (on a separate sheet – Moscow), the other 15 are Volgograd, Voronezh, Yekaterinburg, Kazan, Krasnodar, Krasnoyarsk, Nizhny Novgorod, Novosibirsk, Omsk, Perm, Rostov-on-Don, Samara, St. Petersburg, Ufa, Chelyabinsk

· TEXT – review text

· GENDER – gender of the review author (2 – female, 1 – male, 0 – cannot be determined)

· CLASS_1 – group of reasons for dissatisfaction with medical care (M – issues of medical content, O – issues of organizational support and economic aspect, C – mixed (combined) class, #N/A – cannot be clearly determined)[1]

· CLASS_2 – group of reasons for dissatisfaction with medical care (0 – issues of medical content, 1 – issues of organizational support and economic aspect, 2 – mixed (combined) class, #N/A – cannot be clearly determined)

· DAY – day of the month the review was posted

· MONTH – month the review was posted

· YEAR – year the review was posted

· DOCTOR_OR_CLINIC – what or who is the review dedicated to – the doctor or the clinic

· SPEC – physician specialty (for observations where the review is dedicated to the physician)

· GROUP_SPEC – a large group of a physician’s specialty

· ID – observation identifier

Database application

The data are suitable for analyzing patient dissatisfaction trends with medical services in Russia over the period from July 2012 to August 2023. This dataset could be particularly useful for healthcare providers, policymakers, and researchers interested in understanding patient experiences and identifying areas for quality improvement in Russian healthcare. Some potential applications include:

• Analyzing geographic patterns of patient complaints across different cities in Russia
• Examining trends in patient dissatisfaction over time
• Identifying common reasons for dissatisfaction with medical care
• Comparing dissatisfaction levels between different medical specialties
• Assessing gender differences in patient complaints

The database provides rich qualitative data through full-text review texts, allowing for in-depth analysis of patient experiences. The structured variables like city, date, doctor/clinic information, etc. enable quantitative analysis as well. This combination of qualitative and quantitative data makes it possible to gain a comprehensive understanding of patient dissatisfaction patterns in Russia's healthcare system over more than a decade.

For researchers specifically interested in healthcare quality issues, this dataset could serve as an important resource for studying patient experiences and outcomes in Russia's medical system. The longitudinal nature of the data (2012-2023) also allows for analysis of changes over time in patient satisfaction.

Overall, this database provides valuable insights into patient perceptions of healthcare quality that could inform policy decisions, quality improvement

Digitization of healthcare data along with algorithmic breakthroughts in AI will have a major impact on healthcare delivery in coming years. Its intresting to see application of AI to assist clinicians during patient treatment in a privacy preserving way. While scientific knowledge can help guide interventions, there remains a key need to quickly cut through the space of decision policies to find effective strategies to support patients during the care process.

Offline Reinforcement learning (also referred to as safe or batch reinforcement learning) is a promising sub-field of RL which provides us with a mechanism for solving real world sequential decision making problems where access to simulator is not available. Here we assume that learn a policy from fixed dataset of trajectories with further interaction with the environment(agent doesn't receive reward or punishment signal from the environment). It has shown that such an approach can leverage vast amount of existing logged data (in the form of previous interactions with the environment) and can outperform supervised learning approaches or heuristic based policies for solving real world - decision making problems. Offline RL algorithms when trained on sufficiently large and diverse offline datasets can produce close to optimal policies(ability to generalize beyond training data).

As Part of my PhD, research, I investigated the problem of developing a Clinical Decision Support System for Sepsis Management using Offline Deep Reinforcement Learning.

MIMIC-III ('Medical Information Mart for Intensive Care') is a large open-access anonymized single-center database which consists of comprehensive clinical data of 61,532 critical care admissions from 2001–2012 collected at a Boston teaching hospital. Dataset consists of 47 features (including demographics, vitals, and lab test results) on a cohort of sepsis patients who meet the sepsis-3 definition criteria.

we try to answer the following question:

Given a particular patient’s characteristics and physiological information at each time step as input, can our DeepRL approach, learn an optimal treatment policy that can prescribe the right intervention(e.g use of ventilator) to the patient each stage of the treatment process, in order to improve the final outcome(e.g patient mortality)?

we can use popular state-of-the-art algorithms such as Deep Q Learning(DQN), Double Deep Q Learning (DDQN), DDQN combined with BNC, Mixed Monte Carlo(MMC) and Persistent Advantage Learning (PAL). Using these methods we can train an RL policy to recommend optimum treatment path for a given patient.

Data acquisition, standard pre-processing and modelling details can be found here in Github repo: https://github.com/asjad99/MIMIC_RL_COACH

Global Cancer Registry Software Market size valued at US$ 85.14 Million in 2023, set to reach US$ 204.07 Million by 2032 at a CAGR of about 10.2% from 2024 to 2032.

IMRD is an NHS Health Research Authority (HRA) approved research database, containing longitudinal non-identified patient electronic medical records (EMR) from 6 million patients in England, United Kingdom (UK) since 1900. EMR data is supplied from UK General Practitioner (GP) clinical systems via the IQVIA Medical Research Extraction Scheme and used for medical and public health research and treatment analysis. Data is collected from a diverse patient population across different ages, genders and ethnicities, providing a representative picture of GP-managed care within the UK. The average length of patient follow-up is 10 years, with most practices recording data for over 20 years.

This dataset includes specific information on 3,000 COVID-19 recovery patients from Iraq's Kurdistan Region. The data includes 46 features, 15 of which were rigorously vetted by qualified COVID-19 clinicians and the remaining 31 by committed researchers. The dataset provides a comprehensive picture of the patients' health, recovery progress, and a variety of demographic and clinical characteristics. The use of expert-collected data assures a high level of precision and dependability when analyzing the patients' conditions. Researchers and healthcare professionals can use this comprehensive dataset to gain valuable insights into the recovery patterns of COVID-19 patients in the Kurdistan region, contributing to a better understanding of the virus and enhancing the development of targeted interventions and treatment plans. --Demographic 1.Age 2.Height 3.Gender 4.Blood group 5.Weight 6.Address

--Past medical history 7.Smoking 8.Blood Pressure 9.Past Surgical 10.Diabetes 11.Sensitivity 12.Tuberculosis (T.B) 13.Asthma 14.Hypertension

--Diagnosis 15.Vaccine type 16.Vaccination 17.Expose start date
18.Expose End date 19.Investigation
20.Chest X-ray 21.Red Blood Cells (RBC) 22.Complete blood count (CBC) 23.Polymerase Chain Reaction (PCR) 24.C-reactive protein (CRP)

--Symptoms during COVID 25.Anxiety 26.Cough 27.Sore throat 28.Fever 29.Joint pains 30.Losing taste or smell 31.Headache

--Present illness 6 months after covid 32.Loss of interest 33.Cough 34. Dyspnea 35.Low Mood 36.Chest Pain 37.Depression 38.Short term Memory Loss 39.Disturb sleep 40.Fatigue

--Clinical parameter 41.Hospitalized 42.LV fluid 43. Blood Oxygen Level (SPO2)
44.Dates of emergency treatment 45.Medicine 46.NO-OF-TESTS

GP Practices - are UK wide and we cover the Practice Manager, the Senior GP and Senior Nurses. In every practice one of these is nominated as the main contact (our 'Chief Officer' category), to allow you to reach one person per practice if required. This will normally be the Practice Manager and is the contact for which we list an email address.

The National Health Service is the largest employer in the UK but is not a single homogenous organisation. Following devolution and major re-organisations in the past few years, the ways in which it is organised in England, Scotland, Wales and Northern Ireland are continuing to diverge.

Our database covers senior and mid-level posts across all functions and areas of the NHS. This includes both the Management and Medical/Clinical sides.

England - the NHS has undergone considerable re-organisation since 2011 with Strategic Health Authorities and Primary Care Trusts being replaced by a new structure of healthcare provision. The vast majority of services are now provided or commissioned at a local level via groups of GP Surgeries, known as Clinical Commissioning Groups (CCG's), or at a secondary care level via Hospital Trusts. Public Health services are now provided by Local Authorities who also work with CCG's via Health and Wellbeing Boards to commission services jointly. There are also a number of new 'Community Healthcare' providers, in the form of Health and Care Trusts (NHS organisations) and Community Interest Companies (Social Enterprises). These organisations provide a range of community, mental health, primary care and nursing functions and sit alongside Local Authorities, CCG's and Secondary Care providers in many areas. These, along with some Secondary Care Acute Trusts which inherited them following the dissolution of PCT's run Community Hospitals, Clinics, Walk in Centres and some Dental services.

Scotland - has a simplified structure with Scottish Health Boards having control of all operational responsibilities within their geographical area. The Community Health Partnerships provide a range of community health services and they work closely with primary health care professionals as well as hospitals and local councils.

Wales - has established Local Health Boards and with the exception of one remaining NHS Trust, they deal with all Primary and Secondary Healthcare services.

Northern Ireland - also has single organisations - Health & Social Care Trusts, which along with several other national bodies, deal with co-ordinating and providing all the regions Healthcare services.

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 24.9 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 24.9 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 Texas Department of Insurance, Division of Workers' Compensation (DWC) maintains a database of institutional medical billing services (SV2). It contains charges, payments, and treatments billed on a CMS-1450 form (UB-92, UB-04) by hospitals and medical facilities that treat injured employees, excluding ambulatory surgical centers, with dates of service more than five years old. For datasets from the past five years, see institutional medical billing services (SV2) header information. The header identifies insurance carriers, injured employees, employers, place of service, and diagnostic information. The bill header information groups individual line items reported in the detail section. The bill selection date and bill ID must be used to group individual line items into a single bill. Find more information in our institutional medical billing services (SV2) header data dictionary. See institutional medical billing services (SV2) detail information- historical for the corresponding detail records related to this dataset. Go to our page on DWC medical state reporting public use data file (PUDF) to learn more about using this information.

Electronic databases searched.