A set of formatted clinical documents as dictated by the physicians to train medical AI models.

The Open Database of Healthcare Facilities (ODHF) is a collection of open data containing the names, types, and locations of health facilities across Canada. It is released under the Open Government License - Canada. The ODHF compiles open, publicly available, and directly-provided data on health facilities across Canada. Data sources include regional health authorities, provincial, territorial and municipal governments, and public health and professional healthcare bodies. This database aims to provide enhanced access to a harmonized listing of health facilities across Canada by making them available as open data. This database is a component of the Linkable Open Data Environment (LODE).

This dataset provides a comprehensive collection of maternal health data, focusing on key health indicators throughout pregnancy. It includes essential details such as the mother’s age, gravida (number of pregnancies), weight, height, blood pressure, gestational age, and fetal health status. In addition to these primary metrics, the dataset captures important medical test results, including anemia, blood sugar levels, and fetal heart rate, providing a thorough overview of both maternal and fetal well-being. The dataset categorizes pregnancies into high-risk and non-high-risk based on various factors such as abnormal blood pressure readings, test results, and fetal health conditions. This classification can be vital for prenatal care and early risk detection, facilitating interventions for at-risk pregnancies. Collected manually from healthcare records, the dataset ensures data accuracy and reliability. Each entry has been anonymized to protect patient privacy and guarantee ethical standards. This dataset serves as an invaluable resource for research in maternal health, predictive analytics, and pregnancy outcome studies. It can be used for developing models to assess pregnancy risks, guide healthcare interventions, and improve prenatal care strategies globally. Researchers, healthcare professionals, and data scientists can leverage this dataset to gain deeper insights into pregnancy-related health trends and explore potential factors influencing maternal and fetal health outcomes.

On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data on services capacity, inpatient/outpatient utilization, patients, revenues and expenses by type and payer, balance sheet and income statement.

Due to the large size of the complete dataset, a selected set of data representing a wide range of commonly used data items, has been created that can be easily managed and downloaded. The selected data file includes general hospital information, utilization data by payer, revenue data by payer, expense data by natural expense category, financial ratios, and labor information.

There are two groups of data contained in this dataset: 1) Selected Data - Calendar Year: To make it easier to compare hospitals by year, hospital reports with report periods ending within a given calendar year are grouped together. The Pivot Tables for a specific calendar year are also found here. 2) Selected Data - Fiscal Year: Hospital reports with report periods ending within a given fiscal year (July-June) are grouped together.

Various population statistics, including structured demographics data.

Dataset Card for MedMCQA

  Dataset Summary

MedMCQA is a large-scale, Multiple-Choice Question Answering (MCQA) dataset designed to address real-world medical entrance exam questions. MedMCQA has more than 194k high-quality AIIMS & NEET PG entrance exam MCQs covering 2.4k healthcare topics and 21 medical subjects are collected with an average token length of 12.77 and high topical diversity. Each sample contains a question, correct answer(s), and other options which… See the full description on the dataset page: https://huggingface.co/datasets/openlifescienceai/medmcqa.

These datasets provide de-identified insurance data for diabetes. The data is provided by three managed care organizations in Allegheny County (Gateway Health Plan, Highmark Health, and UPMC) and represents their insured population for the 2015 and calendar years. Disclaimer: Users should be cautious of using administrative claims data as a measure of disease prevalence and interpreting trends over time, as data provided were collected for purposes other than surveillance. Limitations of these data include but are not limited to: misclassification, duplicate individuals, exclusion of individuals who did not seek care in past two years and those who are: uninsured, enrolled in plans not represented in the dataset, or were not enrolled in one of the represented plans for at least 90 days.

About this dataset

Context

The dataset tabulates the Medical Lake population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Medical Lake. The dataset can be utilized to understand the population distribution of Medical Lake by age. For example, using this dataset, we can identify the largest age group in Medical Lake.

Key observations

The largest age group in Medical Lake, WA was for the group of age 30 to 34 years years with a population of 580 (11.77%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Medical Lake, WA was the 85 years and over years with a population of 24 (0.49%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Medical Lake is shown in this column.
• % of Total Population: This column displays the population of each age group 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 Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Medical Lake population by race and ethnicity. The dataset can be utilized to understand the racial distribution of Medical Lake.

Content

The dataset will have the following datasets when applicable

Please note that in case when either of Hispanic or Non-Hispanic population doesnt exist, the respective dataset will not be available (as there will not be a population subset applicable for the same)

• Medical Lake, WA Population Breakdown by Race
• Medical Lake, WA Non-Hispanic Population Breakdown by Race
• Medical Lake, WA Hispanic or Latino Population Distribution by Their Ancestries

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/.

Image : Image was made by me for other International Contest (held by some Medical Institute,USA in the year 2021), 'An intuitive of electromagnetic radiation flowing over epithelial tissue'.This is an open-access page. All the content can be freely downloaded after sign-up. This webpage contains datasets and models, which are in support of my Research claim/discovery and also used in my Invited/Keynote/Featured/Speaker Presentations.To free download, all papers or volumes in one pdf, pls. click on Link 1(free preprint) or paid hardcopy. Link 2 has only abstract.DOI link will be updated after presentation only and some formalities.To see complete Abstract, pls click Presentation title, refer appropriate conference or full manuscript. As clubbed multiple papers, so for 'Section' Abstract - pls refer relevant sections/subsections of the manuscript or Data:B-Bio Models-1 (for Conference 1) or Data:B-Bio Models-1 and Data:B-Bio Models-2 (for Conference 2) or Data: B-Non-Linear Theory (for Conference 3).A. The Iconic Meetings International PresentationsI. Conference 1 My Ist Invited & Keynote, BioMedSummit2025Techniques of Artificial Intelligence, Applied Electrical and Mechanical Engineering in Understanding Biological Processes*Link 1 : Preprint My Last Self Funded Journey, with cover letter and legal disclosure statement (dated 28th Sept, 2024)Link 2 : Conference Proceeding(has only abstract)Sections/Subsections/Chapter (Related to this Conference and webpage). Related attachment - BioMedSummit2025_EM.zip * ElectroMagnetic Radiations : Human Head, SAR MobileElectroMagnetic Radiations : Human Head, MRIElectroMagnetic Radiations : Human Head, WiFi AntennaII. Conference 2My 2nd Link 1 : Preprint My Last Self Funded Journey, with cover letter and legal disclosure statement (dated 28th Sept, 2024)Link 2 : Conference Proceeding(has only abstract)Sections/Subsections/Chapter (Related to this Conference and webpage). To see datasets, either scroll or click on below titles.*III. Conference 3My 3rd Link 1 : Preprint My Last Self Funded Journey, with cover letter and legal disclosure statement (dated 28th Sept, 2024)Link 2 : Conference Proceeding(has only abstract)Sections/Subsections/Chapter (Related to this Conference and webpage). To see datasets, either scroll or click on below titles.*B. Hard/Physical Copy (Paid)The self- published book is the draft version of the above peer-reviewed full manuscript and allowed by the Chair/Panel/Organizer/Jury/Editor-in-Chief to keep the retention of Copyrights for self-publication.A. International Colored Edition My Memoirs : Conferences, My Last Self Funded Journey, VolumesB. Few Nations Colored Edition(for sale only in India, Bangladesh, Pakistan, Nepal, Sri Lanka, Indian Sub-continents and Maldives.)My Memoirs : Conferences, My Last Self Funded Journey, Volumes* The submitted work will be used in my future presentations or future research paper, with same or different titles.

DESCRIPTION This table contains data on the percent of residents aged 16 years and older mode of transportation to work for ...

SUMMARY This table contains data on the percent of residents aged 16 years and older mode of transportation to work for California, its regions, counties, cities/towns, and census tracts. Data is from the U.S. Census Bureau, Decennial Census and American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Commute trips to work represent 19% of travel miles in the United States. The predominant mode – the automobile - offers extraordinary personal mobility and independence, but it is also associated with health hazards, such as air pollution, motor vehicle crashes, pedestrian injuries and fatalities, and sedentary lifestyles. Automobile commuting has been linked to stress-related health problems. Active modes of transport – bicycling and walking alone and in combination with public transit – offer opportunities for physical activity, which is associated with lowering rates of heart disease and stroke, diabetes, colon and breast cancer, dementia and depression. Risk of injury and death in collisions are higher in urban areas with more concentrated vehicle and pedestrian activity. Bus and rail passengers have a lower risk of injury in collisions than motorcyclists, pedestrians, and bicyclists. Minority communities bear a disproportionate share of pedestrian-car fatalities; Native American male pedestrians experience four times the death rate Whites or Asian pedestrians, and African-Americans and Latinos experience twice the rate as Whites or Asians. More information about the data table and a data dictionary can be found in the About/Attachments section.

ind_id - Indicator ID ind_definition - Definition of indicator in plain language reportyear - Year that the indicator was reported race_eth_code - numeric code for a race/ethnicity group race_eth_name - Name of race/ethnic group geotype - Type of geographic unit geotypevalue - Value of geographic unit geoname - Name of a geographic unit county_name - Name of county that geotype is in county_fips - FIPS code of the county that geotype is in region_name - MPO-based region name; see MPO_County list tab region_code - MPO-based region code; see MPO_County list tab mode - Mode of transportation short name mode_name - Mode of transportation long name pop_total - denominator pop_mode - numerator percent - Percent of Residents Mode of Transportation to Work,
Population Aged 16 Years and Older LL_95CI_percent - The lower limit of 95% confidence interval UL_95CI_percent - The lower limit of 95% confidence interval percent_se - Standard error of the percent mode of transportation percent_rse - Relative standard error (se/value) expressed as a percent CA_decile - California decile CA_RR - Rate ratio to California rate version - Date/time stamp of a version of data

The data contained in the table describes COVID-19 in Canada in terms of number of cases and deaths at the provincial and national levels from January 31, 2020 to present time. It also describes the number of tests performed and the number of people recovered. The values displayed in the table are provided by the Public Health Infobase, managed by the Health Promotion and Chronic Disease Prevention Branch (HPCDPB) of the Public Health Agency of Canada (PHAC). The values are updated daily.

Datasets dating from 1986 to the present are available for 93 countries in which data were collect through Household questionnaires, Women's questionnaires, Men's questionnaires, Biomarker's questionnaires, and Fieldworker's questionnaires. The following data types are produced from the collected data : Household Recode, Household Member Recode, Individual Women's Recode, Births Recode, Children's Recode, Men's Recode, Couple's Recode, Geographic Data, Geospatial Covariates. To view surveys and available datasets go to https://dhsprogram.com/data/available-datasets.cfm. Access to datasets for DHS surveys and their supporting documents may be granted to individuals who register at https://dhsprogram.com/data/new-user-registration.cfm and create a new research project request.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Medical Lake: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 66(3.52%) households where the householder is under 25 years old, 941(50.13%) households with a householder aged between 25 and 44 years, 624(33.24%) households with a householder aged between 45 and 64 years, and 246(13.11%) households where the householder is over 65 years old.
• The age group of 45 to 64 years exhibits the highest median household income, while the largest number of households falls within the 25 to 44 years bracket. This distribution hints at economic disparities within the city of Medical Lake, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

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 median household income by age. You can refer the same here

This data set contains anonymised clinical MRI study, or a set of scans, of 515 patients with symptomatic back pains. Each patient data can have one or more MRI studies associated with it. Each study contains slices, i.e., individual images taken from either sagittal or axial view, of the lowest three vertebrae and the lowest three IVDs. The axial view slices are mainly taken from the last three IVDs – including the one between the last vertebrae and the sacrum. The orientation of the slices of the last IVD are made to follow the spine curve whereas those of the other IVDs are usually made in blocks – i.e., parallel to each other. There are between four to five slices per IVD and they begin from the top of the IVD towards its bottom. Many of the top and bottom slices cut through the vertebrae leaving between one to three slices that cut the IVD cleanly and show purely the image of that IVD. In most cases, the total number of slices in axial view ranges from 12 to 15. However, in some cases, there may be up to 20 slices because the study contains slices of more than last three vertebrae. The scans in sagittal view also vary but all contain at least the last seven vertebrae and the sacrum. While the number of vertebrae varies, each scan always includes the first two sacral links.

There are a total 48,345 MRI slices in our dataset. The majority of the slices have an image resolution of 320x320 pixels, however, there are slices from three studies with 320x310 pixel resolution. The pixels in all slices have 12-bit per pixel precision which is higher than the standard 8-bit greyscale images. Specifically for all axial-view slices, the slice thickness are uniformly 4 mm with centre-to-centre distance between adjacent slices to be 4.4 mm. The horizontal and vertical pixel spacing is 0.6875 mm uniformly across all axial-view slices.

The majority of the MRI studies were taken with the patient in Head-First-Supine position with the rests were taken with the patient in in Feet-First-Supine position. Each study can last between 15 to 45 minutes and a patient may have one or more study associated with them taken at a different time or a few days apart.

You can download and read the research papers detailing our methodology on boundary delineation for lumbar spinal stenosis detection using the URLs provided in the Related Links at the end of this page. You can also check out other dataset and source code related to this program from that section.

We kindly request you to cite our papers when using our data or program in your research.

Spine MRI Dataset, Fracture Detection, Anomaly Detection & Segmentation

The dataset consists of .dcm files containing MRI scans of the spine of the person with several dystrophic changes, such as osteochondrosis, spondyloarthrosis, hemangioma, physiological lordosis smoothed, osteophytes and aggravated defects. The images are labeled by the doctors and accompanied by report in PDF-format.

The dataset includes 9 studies, made from the different angles which provide a comprehensive understanding of a several dystrophic changes and useful in training spine anomaly classification algorithms. Each scan includes detailed imaging of the spine, including the vertebrae, discs, nerves, and surrounding tissues.

MRI study angles in the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F62acce9c1d60720bdd396e036718f406%2FFrame%2084.png?generation=1708543957118470&alt=media" alt="">

💴 For Commercial Usage: Full version of the dataset includes 20,000 spine studies of people with different conditions, leave a request on TrainingData to buy the dataset

Types of diseases and conditions in the full dataset:

• Degeneration of discs
• Osteophytes
• Osteochondrosis
• Hemangioma
• Disk extrusion
• Spondylitis
• AND MANY OTHER CONDITIONS

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fd2f21b9ac7dc26a3554e4647db47df57%2F3.gif?generation=1708543677763656&alt=media" alt="">

Researchers and healthcare professionals can use this dataset to study spinal conditions and disorders, such as herniated discs, spinal stenosis, scoliosis, and fractures. The dataset can also be used to develop and evaluate new imaging techniques, computer algorithms for image analysis, and artificial intelligence models for automated diagnosis.

OTHER MEDICAL SPINE MRI DATASETS:

• Spine MRI Dataset -Anomaly Detection
• Spine Segmentation Dataset - MRI Images
• Spinal Vertebrae Segmentation Dataset
• Spine Magnetic Resonance Imaging Dataset

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to discuss your requirements, learn about the price and buy the dataset

Content

The dataset includes:

• ST000001: includes subfolders with 9 studies. Each study includes MRI-scans in .dcm and .jpg formats,
• DICOMDIR: includes information about the patient's condition and links to access files,
• Spine_MRI_2.pdf: includes medical report, provided by the radiologist,
• .csv file: includes id of the studies and the number of files

Medical reports include the following data:

• Patient's demographic information,
• Description of the case,
• Preliminary diagnosis,
• Recommendations on the further actions

All patients consented to the publication of data

Medical data might be collected in accordance with your requirements.

TrainingData provides high-quality data annotation tailored to your needs

keywords: visual, label, positive, negative, symptoms, clinically, sensory, varicella, syndrome, predictors, diagnosed, rsna cervical, image train, segmentations meta, spine train, mri spine scans, spinal imaging, radiology dataset, neuroimaging, medical imaging data, image segmentation, lumbar spine mri, thoracic spine mri, cervical spine mri, spine anatomy, spinal cord mri, orthopedic imaging, radiologist dataset, mri scan analysis, spine mri dataset, machine learning medical imaging, spinal abnormalities, image classification, neural network spine scans, mri data analysis, deep learning medical imaging, mri image processing, spine tumor detection, spine injury diagnosis, mri image segmentation, spine mri classification, artificial intelligence in radiology, spine abnormalities detection, spine pathology analysis, mri feature extraction, tomography, cloud

SNOMED CT is one of a suite of designated standards for use in U.S. Federal Government systems for the electronic exchange of clinical health information and is also a required standard in interoperability specifications of the U.S. Healthcare Information Technology Standards Panel.

53,054 anonymized adult chest x-ray dataset in 1024 x 1024 pixel DICOM format with corresponding anonymized free-text reports from Dunedin Hospital, New Zealand between 2010 - 2020. Corresponding radiology reports generated by FRANZCR radiologists were manually annotated for 46 common radiological findings mapped to Unified Medical Language System (UMLS) and RadLex ontology. Each of the multiclassification annotations contains 4 types of labels, namely positive, uncertain, negative and not mentioned. In the provided dataset, image filenames contain patient index (enabling analysis requiring grouping of images by patients), as well as anonymized date of acquisition information where the temporal relationship between images is preserved. This dataset can be used for training and testing for deep learning algorithms for adult chest x rays.

To access the data, an ethics training process is required and is divided into 2 steps: 1. An online ethics course at https://globalhealthtrainingcentre.tghn.org/ethics-and-best-practices-sharing-individual-level-data-clinical-and-public-health-research/. You will need to register an account to be able to take the free online ethics course. Once you finished the course quiz, please send the course certificate to Sijing.Feng@southerndhb.govt.nz 2. Signing the Data Use Agreement. It can be accessed at Data_Use_Agreement_-_Hospital_Signed.pdf (trello-attachments.s3.amazonaws.com). Once you signed the Data Use Agreement, please also send the signed copy to Sijing.Feng@southerndhb.govt.nz

After successfully completion of both of above steps, a private link to download the dataset will be sent.

Oral diseases affect nearly 3.5 billion people, with the majority residing in low- and middle-income countries. Due to limited healthcare resources, many individuals are unable to access proper oral healthcare services. Image-based machine learning technology is one of the most promising approaches to improving oral healthcare services and reducing patient costs. Openly accessible datasets play a crucial role in facilitating the development of machine learning techniques. However, existing dental datasets have limitations such as a scarcity of Cone Beam Computed Tomography (CBCT) data, lack of matched multi-modal data, and insufficient complexity and diversity of the data. This project addresses these challenges by providing a dataset that includes 574 CBCT images from 389 patients, multi-modal data with matching modalities, and images representing various oral health conditions.

This dataset contains Hospital Chargemasters with prices in effect as of June 1 of their reporting year. Chargemasters consists of a list of average charges for 25 common outpatient procedures, and the estimated percentage change in gross revenue due to price changes each July 1.

For more on HCAI Chargemaster Data.