This statistic shows the ethnicity share of clinical trial participants worldwide in 2015-2019, by geographic location. Participants with a white ethnic background had a share of 76 percent amongst the clinical trial participants in the United States.

The purest type of electronic clinical data which is obtained at the point of care at a medical facility, hospital, clinic or practice. Often referred to as the electronic medical record (EMR), the EMR is generally not available to outside researchers. The data collected includes administrative and demographic information, diagnosis, treatment, prescription drugs, laboratory tests, physiologic monitoring data, hospitalization, patient insurance, etc.

Individual organizations such as hospitals or health systems may provide access to internal staff. Larger collaborations, such as the NIH Collaboratory Distributed Research Network provides mediated or collaborative access to clinical data repositories by eligible researchers. Additionally, the UW De-identified Clinical Data Repository (DCDR) and the Stanford Center for Clinical Informatics allow for initial cohort identification.

About Dataset:

333 scholarly articles cite this dataset.

Unique identifier: DOI

Dataset updated: 2023

Authors: Haoyang Mi

In this dataset, we have two dataset:

1- Clinical Data_Discovery_Cohort: Name of columns: Patient ID Specimen date Dead or Alive Date of Death Date of last Follow Sex Race Stage Event Time

2- Clinical_Data_Validation_Cohort Name of columns: Patient ID Survival time (days) Event Tumor size Grade Stage Age Sex Cigarette Pack per year Type Adjuvant Batch EGFR KRAS

Feel free to put your thought and analysis in a notebook for this datasets. And you can create some interesting and valuable ML projects for this case. Thanks for your attention.

This survey displays the share of U.S. adults or their family members who participated in a clinical trial by ethnicity, according to a survey conducted in January 2025. Some 20 percent of African-American respondents stated that they or someone from their family ever participated in a clinical trial.

This dataset provides detailed records of clinical trial enrollments, including participant demographics, recruitment sources, eligibility criteria, and enrollment status. It enables comprehensive analysis of recruitment flows, diversity metrics, and participant retention, supporting optimization of trial recruitment strategies and regulatory reporting.

AI-Driven Clinical Trial Diversity Analytics Market Outlook

According to our latest research, the global AI-Driven Clinical Trial Diversity Analytics market size in 2024 stands at USD 1.12 billion, reflecting a robust surge in demand for AI-powered solutions in clinical research. The market is projected to reach USD 4.89 billion by 2033, growing at a compelling CAGR of 17.8% from 2025 to 2033. This growth trajectory is primarily driven by the increasing emphasis on equitable representation in clinical trials and the adoption of advanced analytics to address diversity gaps, as per our comprehensive industry analysis.

The primary growth factor fueling the AI-Driven Clinical Trial Diversity Analytics market is the mounting regulatory and societal pressure to ensure diverse and inclusive clinical trial populations. Regulatory agencies such as the FDA and EMA have introduced stringent guidelines that mandate the inclusion of underrepresented populations in clinical research. This has compelled pharmaceutical companies, contract research organizations, and healthcare institutions to adopt AI-driven analytics for real-time monitoring and enhancement of participant diversity. These advanced solutions enable identification of disparity patterns, prediction of recruitment bottlenecks, and formulation of targeted outreach strategies, thereby improving both trial outcomes and regulatory compliance.

Another significant driver is the escalating complexity and globalization of clinical trials, which often span multiple geographies and demographic segments. Traditional methods of data analysis and recruitment are ill-equipped to manage the volume, velocity, and variety of data generated in such trials. AI-driven platforms, leveraging machine learning and natural language processing, offer actionable insights by integrating data from electronic health records, social determinants of health, and historical trial performance. This capability not only streamlines the recruitment of diverse cohorts but also enhances site selection and patient retention, resulting in more representative and statistically robust clinical outcomes.

Technological advancements in AI and big data analytics are further accelerating market expansion. The integration of cloud computing, real-time dashboards, and predictive analytics empowers stakeholders to monitor diversity metrics continuously and intervene proactively when disparities arise. These innovations also facilitate compliance monitoring, automate reporting for regulatory submissions, and optimize resource allocation. As a result, the adoption of AI-driven diversity analytics is rapidly becoming a standard practice among leading pharmaceutical companies and research organizations, reinforcing the market’s upward trajectory.

From a regional perspective, North America currently leads the AI-Driven Clinical Trial Diversity Analytics market, accounting for over 42% of global revenue in 2024. This dominance is attributed to the presence of major pharmaceutical companies, progressive regulatory frameworks, and a mature digital health ecosystem. Europe follows closely, with increasing investments in clinical trial modernization and diversity initiatives. Meanwhile, the Asia Pacific region is emerging as a significant growth hub, driven by expanding clinical research infrastructure and rising awareness of trial inclusivity. Latin America and the Middle East & Africa are also witnessing gradual adoption, supported by international collaborations and capacity-building efforts.

Component Analysis

The Component segment of the AI-Driven Clinical Trial Diversity Analytics market is bifurcated into Software and Services. Software solutions form the backbone of this market, encompassing AI-powered platforms for data integration, diversity analytics, and visualization. These platforms are designed to ingest data from disparate sources, normalize and standardize demographic information, and provide actionable insights through interactive dashboards. The software segment is witnessing rapid innovation, with vendors introducing features such as real-time alerts, automated compliance tracking, and customizable reporting modules. This technological evolution is helping research sponsors meet regulatory requirements and achieve diversity targets more efficiently.

Services, on the other

AI-Driven Clinical Trial Diversity Analytics Market Outlook

According to our latest research, the global AI-Driven Clinical Trial Diversity Analytics market size reached USD 1.42 billion in 2024, with robust adoption across the pharmaceutical and healthcare sectors. The market is experiencing a strong upward trajectory, registering a CAGR of 18.6% from 2025 to 2033. By the end of 2033, the market is forecasted to achieve a value of USD 6.44 billion. This rapid expansion is largely fueled by increasing regulatory pressure to ensure diverse clinical trial populations and the growing need for advanced analytics solutions to optimize trial outcomes and enhance patient recruitment strategies.

A key growth factor driving the AI-Driven Clinical Trial Diversity Analytics market is the heightened regulatory scrutiny and guidance from global health authorities such as the FDA, EMA, and other regional bodies. These organizations are increasingly mandating the inclusion of underrepresented populations in clinical trials, aiming to ensure that new therapies are safe and effective for all demographic groups. As a result, sponsors and contract research organizations (CROs) are embracing AI-powered analytics tools to identify, recruit, and retain diverse patient cohorts. These tools leverage vast datasets, including electronic health records and social determinants of health, to pinpoint disparities and develop targeted outreach strategies. This regulatory push is compelling market participants to invest in innovative AI solutions, significantly accelerating market growth.

Another major driver is the rising complexity of clinical trials and the need for efficient patient recruitment and site selection. The pharmaceutical industry's pipeline is expanding, with more complex, multi-arm, and adaptive trials requiring nuanced approaches to diversity analytics. AI-driven platforms can analyze historical trial data, real-world evidence, and population health statistics to recommend optimal sites and recruitment strategies that enhance diversity. These platforms facilitate real-time monitoring and adjustment of recruitment efforts, minimizing delays and improving trial success rates. As sponsors strive to reduce time-to-market and ensure equitable access to new therapies, the demand for sophisticated AI analytics platforms is expected to surge.

Technological advancements in AI and machine learning are also significantly contributing to market growth. The integration of natural language processing, predictive analytics, and advanced data visualization tools enables stakeholders to gain actionable insights into patient demographics, social determinants, and site performance. These technologies allow for the continuous assessment of diversity metrics throughout the trial lifecycle, supporting proactive compliance monitoring and reporting. The ongoing digital transformation in healthcare, coupled with increasing investments in AI infrastructure, is fostering the adoption of clinical trial diversity analytics solutions worldwide.

Augmented Analytics in Clinical Research is emerging as a transformative force in the AI-Driven Clinical Trial Diversity Analytics market. By leveraging advanced technologies such as machine learning and artificial intelligence, augmented analytics provides deeper insights into clinical data, enabling researchers to uncover patterns and trends that were previously hidden. This approach not only enhances the accuracy and efficiency of data analysis but also empowers researchers to make more informed decisions regarding patient recruitment and trial design. As the complexity of clinical trials continues to grow, the integration of augmented analytics is expected to play a crucial role in optimizing trial outcomes and ensuring that diverse patient populations are adequately represented.

Regionally, North America dominates the AI-Driven Clinical Trial Diversity Analytics market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The United States, in particular, is at the forefront due to strong regulatory frameworks, a large number of ongoing clinical trials, and significant investments in digital health technologies. Europe is witnessing rapid adoption, driven by the implementation of the European Union Clinical Trials Regulation (EU CTR) and the in

Trial Diversity Analytics Platforms Market Outlook

According to our latest research, the global Trial Diversity Analytics Platforms market size reached USD 1.28 billion in 2024, reflecting a robust growth trajectory driven by the increasing emphasis on diversity and inclusion in clinical research. The market is projected to expand at a CAGR of 13.6% during the forecast period, culminating in a forecasted market size of USD 4.08 billion by 2033. Key growth factors include regulatory mandates for diverse representation in clinical trials, the rising adoption of advanced analytics for trial optimization, and an evolving landscape of pharmaceutical and biotechnology research that prioritizes equitable healthcare outcomes. As per our 2025 research analysis, these dynamics are shaping a highly competitive and innovative market environment globally.

The primary driver for the rapid expansion of the Trial Diversity Analytics Platforms market is the increasing regulatory scrutiny and guidance from global health authorities, such as the FDA and EMA, requiring sponsors to demonstrate representative participant inclusion in clinical trials. This regulatory push has compelled pharmaceutical and biotechnology companies to invest in sophisticated analytics platforms capable of tracking, analyzing, and reporting demographic data throughout the clinical trial lifecycle. The demand for transparency and accountability in participant recruitment and retention has intensified, with sponsors seeking solutions that not only ensure compliance but also enhance the credibility and generalizability of trial outcomes. These factors are fostering an ecosystem where trial diversity analytics platforms are no longer optional, but a critical component of the clinical research infrastructure.

Technological advancements are another major growth catalyst in the Trial Diversity Analytics Platforms market. The integration of artificial intelligence, machine learning, and big data analytics enables real-time monitoring and predictive modeling of participant recruitment strategies, optimizing for diversity across race, ethnicity, gender, age, and socio-economic status. These platforms empower sponsors and contract research organizations (CROs) to identify potential gaps in representation early, adjust recruitment tactics dynamically, and generate comprehensive diversity reports for regulatory submissions. Furthermore, the interoperability of these platforms with electronic health records (EHRs) and other clinical trial management systems enhances data accuracy, reduces manual errors, and streamlines operations. This technological evolution is making diversity analytics platforms indispensable for organizations aiming to achieve both regulatory compliance and scientific rigor.

Another significant growth factor is the rising awareness among stakeholders—ranging from patient advocacy groups to academic institutions—about the ethical and scientific imperatives of diverse clinical trial populations. The growing body of evidence highlighting disparities in drug efficacy and safety across different demographic groups has spurred demand for analytics solutions that can proactively address these gaps. Pharmaceutical and biotechnology companies are increasingly collaborating with community organizations and leveraging diversity analytics platforms to design more inclusive trials, improve participant engagement, and enhance trial retention rates. This trend is further amplified by the competitive advantage gained through the development of therapies that are proven effective across a broader spectrum of the population, reinforcing the market’s upward trajectory.

From a regional perspective, North America currently dominates the Trial Diversity Analytics Platforms market due to its mature clinical research infrastructure, stringent regulatory environment, and high adoption of digital health technologies. However, Europe and Asia Pacific are rapidly emerging as significant markets, driven by expanding pharmaceutical and biotechnology sectors, increasing R&D investments, and rising participation in global clinical trials. The Asia Pacific region, in particular, is witnessing accelerated growth as multinational sponsors seek to tap into its genetically diverse population pools, supported by favorable government initiatives and increasing digitalization in healthcare. Latin America and the Middle East & Africa, while still nascent, are expected to contribute to market growth as awareness of the importance of diversity in clinical

This dataset compiles demographic data on race, ethnicity, sex, and age eligibility from neuromuscular disease studies initiated between January 1, 2004, and December 31, 2024. It includes studies listed on ClinicalTrials.gov that are classified as ‘completed,’ ‘terminated,’ ‘suspended,’ ‘withdrawn,’ or ‘unknown’ under ‘Study Status’ as of December 31, 2024. When data were unavailable on ClinicalTrials.gov, a manual search on PubMed/MEDLINE using National Clinical Trial (NCT) numbers was conducted to retrieve data from relevant publications. The dataset provides structured information to support research on population diversity, health disparities, and epidemiological trends in neuromuscular diseases. Its aim is to facilitate analyses of demographic representation and promote more inclusive, equitable research in this field.

Clinical and Demographic Patient Data.

Clinical and demographic data of participants (data presented as mean ± SD).

Clinical Trial Data Repository Market Outlook

The global clinical trial data repository market size was estimated to be approximately $1.8 billion in 2023 and is projected to grow at a compound annual growth rate (CAGR) of 9.5% to reach around $4.1 billion by 2032. The primary growth factors include the increasing volume and complexity of clinical trials, rising need for efficient data management systems, and stringent regulatory requirements for data accuracy and integrity. The advent of advanced technologies such as artificial intelligence and big data analytics further drives market expansion by enhancing data processing capabilities and providing actionable insights.

The growth of the clinical trial data repository market is significantly influenced by the increasing number of clinical trials being conducted globally. With the rise in chronic diseases, the need for innovative treatments and therapies has surged, leading to an upsurge in clinical trials. This increase in clinical trials necessitates robust data management systems to handle vast amounts of data generated, thereby propelling the demand for clinical trial data repositories. Moreover, the complexity of modern clinical trials, which often involve multiple sites and diverse patient populations, further amplifies the need for sophisticated data management solutions.

Another critical driver for the market is the stringent regulatory landscape governing clinical trial data. Regulatory bodies such as the FDA, EMA, and other local authorities mandate rigorous data management standards to ensure data integrity, accuracy, and accessibility. These regulations necessitate the adoption of advanced data repository systems that can comply with regulatory requirements, thereby fueling market growth. Additionally, regulatory frameworks are becoming increasingly stringent, prompting pharmaceutical and biotechnology companies to invest in state-of-the-art data management systems to avoid compliance issues and potential financial penalties.

Technological advancements play a pivotal role in the market's growth. The integration of artificial intelligence, machine learning, and big data analytics into data repository systems enhances data processing and analysis capabilities. These technologies enable real-time data monitoring, predictive analytics, and improved decision-making, thereby improving the efficiency of clinical trials. Furthermore, the shift towards cloud-based solutions offers scalability, flexibility, and cost-effectiveness, making advanced data management systems accessible to even small and medium-sized enterprises.

Regionally, North America dominates the clinical trial data repository market owing to its robust healthcare infrastructure, high R&D investments, and presence of major pharmaceutical and biotechnology companies. Europe follows closely due to stringent regulatory standards and a strong focus on clinical research. The Asia Pacific region is expected to witness the highest growth rate during the forecast period due to increasing clinical trial activities, growing healthcare expenditure, and the rising adoption of advanced technologies. Latin America and the Middle East & Africa are also likely to experience growth, albeit at a slower pace, driven by improving healthcare systems and increasing focus on clinical research.

Component Analysis

The clinical trial data repository market is segmented by components into software and services. The software segment is anticipated to hold a significant share of the market due to the essential role software plays in data management. Advanced software solutions offer capabilities such as data storage, management, retrieval, and analysis, which are critical for effective clinical trial management. The integration of AI and machine learning algorithms into these software systems further enhances their efficiency by enabling predictive analytics and real-time monitoring, thus driving the software segment's growth.

Software solutions in clinical trial data repositories also offer interoperability, enabling seamless integration with other clinical trial management systems (CTMS) and electronic data capture (EDC) systems. This interoperability is crucial for ensuring data consistency and accuracy across different platforms, thereby enhancing overall data management. Additionally, the increasing adoption of cloud-based software solutions provides scalability, cost-effectiveness, and remote acce

BackgroundDisparities in cancer outcomes persist between racial, ethnic, and socioeconomic groups. One potential cause is lack of appropriate representation in dose-finding clinical trials. We investigated the extent of disparities in phase I clinical trials and recent changes in the setting of institutional efforts to mitigate disparities, legislative interventions, FDA guidance for sponsors and the COVID-19 pandemic.MethodsWe performed a retrospective review of patients enrolled in phase I clinical trials at the University of Colorado Cancer Center in 2018–2019 and 2022-2023. We collected demographics, area deprivation index (ADI), tumor type and other clinical variables. Differences between cohorts were evaluated with t-tests, chi-Square test, or Fisher exact test. Progression-free survival (PFS) and overall survival (OS) were calculated using the Kaplan-Meier method. Hazard ratios (HR), confidence intervals (CI) and p-values were derived using the Cox-proportional hazards method.ResultsA total of 361 patients were included (209 and 152 in the 2018–2019 and 2022–2023 cohorts, respectively). The population consisted of 85.0% White, 3.3% Asian, 1.4% Black, 0.3% Native Hawaiian or Pacific Islander and no American Indian/Alaskan Native (AIAN) patients by race, and 9.1% Hispanic by ethnicity. The most common tumor type was colorectal cancer (18.3%). Compared to 2018-2019, we observed increases in non-English speakers from 1.9% (4/209) to 6.6% (10/152) (p = 0.028) and in translated informed consent forms (ICFs) from 1.4% (3/209) to 5.9% (9/152) (p = 0.033) in 2022-2023. There were no significant changes in race, ethnicity, insurance, or tumor type, although there was a moderate increase in Hispanic patients from 8.1% to 10.5%. There were no differences in clinical outcomes by race, ethnicity, or ADI scores in the overall study population. However, in the most common cancer type, colorectal cancer, higher ADI scores were associated with decreased median PFS and OS.ConclusionThe interventions resulted in an increase in accrual of non-English speaking patients, however, there was not yet a significant change in overall race and ethnicity. Our study confirms poorer outcomes for patients with higher ADI scores. Further research is warranted to understand disparities in clinical trial accrual, and intervention is needed to improve outcomes for disadvantaged patients.

Patient Matching for Clinical Trials Market Outlook

According to our latest research, the global patient matching for clinical trials market size reached USD 1.12 billion in 2024, driven by increasing adoption of digital health technologies and the growing complexity of clinical research. The market is expected to expand at a robust CAGR of 12.8% from 2025 to 2033, reaching a forecasted value of USD 3.38 billion by 2033. This accelerated growth is primarily attributed to the rising demand for precision medicine, a surge in the number of clinical trials globally, and the need for efficient patient recruitment and retention strategies. As per our comprehensive analysis, the integration of advanced data analytics and artificial intelligence (AI) in healthcare systems is significantly enhancing the accuracy and efficiency of patient matching solutions, further fueling market expansion.

One of the fundamental growth drivers for the patient matching for clinical trials market is the increasing complexity and specificity of clinical trial protocols. Modern clinical trials, especially those targeting rare diseases and personalized therapies, require highly precise patient cohorts. Traditional recruitment methods often fall short in identifying suitable candidates, leading to delays and increased costs. The deployment of sophisticated patient matching software, powered by AI and machine learning algorithms, allows for the rapid analysis of vast datasets from electronic health records (EHRs), genomics, and other sources. This enables sponsors and investigators to identify eligible participants more efficiently, ensuring that trials are populated with the right patients who meet stringent inclusion and exclusion criteria. The result is improved trial outcomes, reduced timelines, and cost savings, which collectively drive the adoption of patient matching solutions across the pharmaceutical and biotechnology sectors.

Another significant factor underpinning the growth of the patient matching for clinical trials market is the increasing emphasis on patient-centric approaches in clinical research. Regulatory agencies and sponsors are placing greater importance on diversity, equity, and inclusion in clinical trials, aiming to ensure that study populations accurately reflect the broader patient population. Patient matching technologies facilitate the identification and engagement of underrepresented groups by leveraging demographic, social, and behavioral data. This not only supports regulatory compliance but also enhances the generalizability of trial results. Furthermore, the integration of patient engagement platforms and digital recruitment strategies, such as social media outreach and mobile health applications, is streamlining the recruitment process, reducing patient burden, and improving retention rates throughout the trial lifecycle.

The rapid digital transformation of healthcare infrastructure globally is also playing a pivotal role in the expansion of the patient matching for clinical trials market. The proliferation of interoperable EHR systems, health information exchanges (HIEs), and cloud-based data repositories has made it possible to aggregate and analyze patient data from diverse sources. This data liquidity enables real-time patient identification and matching, even across geographically dispersed sites. Additionally, partnerships between healthcare providers, academic research institutes, and technology vendors are fostering innovation in data integration and analytics. These collaborations are accelerating the development of next-generation patient matching platforms that offer improved scalability, security, and compliance with data privacy regulations such as GDPR and HIPAA.

From a regional perspective, North America continues to dominate the patient matching for clinical trials market, accounting for the largest share in 2024. This leadership position is attributed to the region's advanced healthcare IT infrastructure, high clinical trial activity, and supportive regulatory environment. Europe follows closely, benefiting from strong government initiatives to promote clinical research and data interoperability. The Asia Pacific region is emerging as a high-growth market, propelled by expanding pharmaceutical R&D investments, increasing adoption of digital health technologies, and a large, diverse patient pool. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as local governments and healthcare organizations invest in

Context

This dataset captures key gameplay, card, and player-level information from Clash Royale to support fast exploration, comparisons, and analytical insights.

Content

Includes battle statistics, card attributes, player performance metrics, and outcome labels designed for EDA, modeling, and trend analysis.

Descriptive statistics of demographic and clinical characteristics.

Percentages are proportions of the total cohort (1,504,535).Percentages are proportions of the cohort of patients with any FH recorded (283,715).Percentages are proportions of the cohort of patients with positive FH of CHD recorded (140,058).Age was missing for 0.96% of the total sample.Townsend Score was missing for 6.70% of the total sample.

Overview: The AIDS Clinical Trials Group Study 175 Dataset, initially published in 1996, is a comprehensive collection of healthcare statistics and categorical information about patients diagnosed with AIDS. This dataset was created with the primary purpose of examining the performance of two different types of AIDS treatments: zidovudine (AZT) versus didanosine (ddI), AZT plus ddI, and AZT plus zalcitabine (ddC). The prediction task associated with this dataset involves determining whether each patient died within a specified time window.

Dataset Details: - Number of rows: 2139 - Number of columns: 24

Purpose of Dataset Creation: The dataset was created to evaluate the efficacy and safety of various AIDS treatments, specifically comparing the performance of AZT, ddI, and ddC in preventing disease progression in HIV-infected patients with CD4 counts ranging from 200 to 500 cells/mm3. This intervention trial aimed to contribute insights into the effectiveness of monotherapy versus combination therapy with nucleoside analogs.

Funding Sources: The creation of this dataset was funded by: - AIDS Clinical Trials Group of the National Institute of Allergy and Infectious Diseases - General Research Center units funded by the National Center for Research Resources

Instance Representation: Each instance in the dataset represents a health record of a patient diagnosed with AIDS in the United States. These records encompass crucial categorical information and healthcare statistics related to the patient's condition.

Study Design: - Study Type: Interventional (Clinical Trial) - Enrollment: 2100 participants - Masking: Double-Blind - Primary Purpose: Treatment - Official Title: A Randomized, Double-Blind Phase II/III Trial of Monotherapy vs. Combination Therapy With Nucleoside Analogs in HIV-Infected Persons With CD4 Cells of 200-500/mm3 - Study Completion Date: November 1995

Study Objectives: To determine the effectiveness and safety of different AIDS treatments, including AZT, ddI, and ddC, in preventing disease progression among HIV-infected patients with specific CD4 cell counts.

Additional Information: The dataset provides valuable insights into the HIV-related clinical trials conducted by the AIDS Clinical Trials Group, contributing to the understanding of treatment outcomes and informing future research in the field.

Attributes Description:

1. Patient Information:

2. Censoring Indicator (label):Binary indicator (1 = failure, 0 = censoring) denoting patient status.

3. Temporal Information:

4. Time to Event (time): Integer representing time to failure or censoring.

5. Treatment Features:

   • Treatment Indicator (trt): Categorical feature indicating the type of treatment received (0 = ZDV only, 1 = ZDV + ddI, 2 = ZDV + Zal, 3 = ddI only).

6. Baseline Health Metrics:

7. Age (age): Patient's age in years at baseline.

8. Weight (wtkg): Continuous feature representing weight in kilograms at baseline.

9. Hemophilia (hemo): Binary indicator of hemophilia status (0 = no, 1 = yes).

10. Sexual Orientation (homo): Binary indicator of homosexual activity (0 = no, 1 = yes).

11. IV Drug Use History (drugs): Binary indicator of history of IV drug use (0 = no, 1 = yes).

12. Karnofsky Score (karnof): Integer on a scale of 0-100 indicating the patient's functional status.

13. Antiretroviral Therapy History:

14. Non-ZDV Antiretroviral Therapy Pre-175 (oprior): Binary indicator of non-ZDV antiretroviral therapy pre-Study 175 (0 = no, 1 = yes).

15. ZDV in the 30 Days Prior to 175 (z30): Binary indicator of ZDV use in the 30 days prior to Study 175 (0 = no, 1 = yes).

16. ZDV Prior to 175 (zprior): Binary indicator of ZDV use prior to Study 175 (0 = no, 1 = yes).

17. Days Pre-175 Anti-Retroviral Therapy (preanti): Integer representing the number of days of pre-Study 175 anti-retroviral therapy.

18. Demographic Information:

19. Race (race): Integer denoting race (0 = White, 1 = non-white).

20. Gender (gender): Binary indicator of gender (0 = Female, 1 = Male).

21. Treatment History:

22. Antiretroviral History (str2): Binary indicator of antiretroviral history (0 = naive, 1 = experienced).

23. Antiretroviral History Stratification (strat): Integer representing antiretroviral history stratification.

24. Symptomatic Information:

25. Symptomatic Indicator (symptom): Binary indicator of symptomatic status (0 = asymptomatic, 1 = symptomatic).

26. Additional Treatment Attributes:

27. Treatment Indicator (treat): Binary indicator of treatment (0 = ZDV only, 1 = others).

28. Off-Treatment Indicator (offtrt): Binary indicator of being off-treatment before 96+/-5 weeks (0 = no, 1 = yes).

29. Immunological Metrics:

30. CD4 Counts (cd40, cd420): Integer values representing CD4 counts at baseline and 20+/-5 weeks.

31. CD8 Counts (cd80, cd820): Integer values representing CD8 counts at baseline and 20+/-5 weeks.

Original Dataset Website: [h...

Detailed dataset comprising health and demographic data of 100,000 individuals, aimed at facilitating diabetes-related research and predictive modeling. This dataset includes information on gender, age, location, race, hypertension, heart disease, smoking history, BMI, HbA1c level, blood glucose level, and diabetes status.

Dataset Use Cases

This dataset can be used for various analytical and machine learning purposes, such as:

1. Predictive Modeling: Build models to predict the likelihood of diabetes based on demographic and health-related features.
2. Health Analytics: Analyze the correlation between different health metrics (e.g., BMI, HbA1c level) and diabetes.
3. Demographic Studies: Examine the distribution of diabetes across different demographic groups and locations.
4. Public Health Research: Identify risk factors for diabetes and target interventions to high-risk groups.
5. Clinical Research: Study the relationship between comorbid conditions like hypertension and heart disease with diabetes.

Potential Analyses

• Descriptive Statistics: Summarize the dataset to understand the central tendencies and dispersion of features.
• Correlation Analysis: Identify the relationships between features.
• Classification Models: Use machine learning algorithms to classify individuals as diabetic or non-diabetic.
• Trend Analysis: Analyze trends over the years to see how diabetes prevalence has changed.

A Cross-Sectional Study of Demographic Representativeness of Glaucoma Patient Populations in Clinical Trials from 2006 to 2022

About this dataset

Context

The dataset tabulates the population of Medical Lake by race. It includes the population of Medical Lake across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to understand the population distribution of Medical Lake across relevant racial categories.

Key observations

The percent distribution of Medical Lake population by race (across all racial categories recognized by the U.S. Census Bureau): 87.38% are white, 3.21% are Black or African American, 0.12% are American Indian and Alaska Native, 1.70% are Asian, 0.12% are Native Hawaiian and other Pacific Islander, 4.43% are some other race and 3.04% are multiracial.

https://i.neilsberg.com/ch/medical-lake-wa-population-by-race.jpeg" alt="Medical Lake population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (excluding ethnicity) for the Medical Lake
• Population: The population of the racial category (excluding ethnicity) in the Medical Lake is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Medical Lake total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Medical Lake Population by Race & Ethnicity. You can refer the same here