15 Open Healthcare Datasets — 2024 Update
The healthcare industry is undergoing a digital transformation driven by the availability of open-source datasets. These datasets provide data scientists, researchers, and medical professionals with valuable insights to improve patient outcomes, streamline operations, and foster innovative treatments. Here are 15 top open-source healthcare datasets that are making a significant impact in healthcare research and can be helpful for those working in AI and data science.
This is an updated version of our popular 2022 article on open healthcare datasets.
Open-Source Healthcare Datasets
MIMIC-III (Medical Information Mart for Intensive Care)
MIMIC-III is a comprehensive healthcare dataset containing de-identified health-related data from intensive care unit (ICU) patients. It includes a variety of data types, such as demographics, clinical outcomes, and treatment records. MIMIC-III is widely used for developing predictive models and analyzing ICU practices.
eICU Collaborative Research Database
The eICU Collaborative Research Database is a comprehensive healthcare dataset of clinical data collected from critical care patients across multiple hospitals. Established in 2014, it has grown exponentially, encompassing data from hundreds of hospitals and millions of patients. The database enables researchers to conduct robust studies on a wide range of critical care topics, including ICU practices, patient monitoring, and treatment outcomes.
Found on Kaggle, this dataset of over 100,000 chest X-ray images is a valuable resource for advancing medical imaging and diagnostics. It covers a wide range of 14 chest diseases and is meticulously labeled for accurate identification. The dataset empowers AI models to analyze chest X-rays with remarkable precision and efficiency, pushing forward healthcare by enabling earlier detection, more accurate diagnosis, and personalized treatment plans.
Radiologists, medical professionals, and healthcare institutions can utilize this healthcare dataset to develop new AI-powered tools that assist in image interpretation and facilitate telehealth applications.
The Cancer Genome Atlas (TCGA)
The Cancer Genome Atlas (TCGA) is a comprehensive collection of genomic, epigenomic, transcriptomic, and proteomic data across various cancer types. TCGA data has been used to identify cancer-causing mutations, develop biomarkers for cancer diagnosis and prognosis, and study the expression of genes and proteins in cancer cells. TCGA has played an instrumental in improving cancer care by identifying new biomarkers for cancer diagnosis, the development of new cancer treatments, and a better understanding of how cancer evolves.
The UK Biobank is a new biomedical research initiative healthcare dataset that houses genetic and health data from approximately half a million participants. It offers an invaluable resource for scientists, enabling them to study a wide range of diseases and health conditions. The longitudinal nature of the UK Biobank allows researchers to track health and genetic changes over time, facilitating the study of disease progression and early disease markers. Its diversity ensures the generalizability of findings to diverse populations. Stringent quality control measures guarantee the accuracy and reliability of the data.
PhysioNet serves as a comprehensive healthcare dataset of physiological signals, notably electrocardiogram (ECG) and electroencephalogram (EEG) data. ECG signals provide insights into cardiac activity, enabling the detection and analysis of cardiovascular abnormalities. EEG data offers a window into brain activity, facilitating the study of neurological disorders. PhysioNet’s extensive database includes signals from diverse sources, allowing for broad research questions and comparative studies.
Human Connectome Project (HCP)
HCP offers high-resolution neuroimaging data, including MRI and functional MRI scans, to study brain connectivity. This dataset is invaluable for neuroscience research, allowing deeper insights into brain function and disorders.
BioASQ (Biomedical Semantic Indexing and Question Answering) is a prominent healthcare dataset designed to facilitate advancements in NLP and text mining applications within the healthcare domain. It comprises a rich collection of biomedical articles and annotated text, providing a valuable resource for researchers, data scientists, and developers working in the field of medical AI.
COVID-19 Open Research Dataset (CORD-19)
The CORD-19 dataset contains a wealth of scholarly articles related to COVID-19, including data on the virus, treatments, and outcomes. This dataset played a central role in accelerating research and response efforts during the pandemic. But it hasn’t stopped there. It’s now playing an important role in educating future data scientists and professionals as many courses have added the data set into their curriculums to provide learners with recent and wild-ranging data.
OpenfMRI is a valuable resource in neuroscience, providing a vast collection of functional magnetic resonance imaging (fMRI) datasets. The repository includes resting-state and task-based fMRI data from various experimental paradigms, cognitive tasks, and clinical populations. OpenfMRI’s open-access nature facilitates collaboration and knowledge sharing, leading to advancements in understanding brain activity and mental health treatments.
HCUP (Healthcare Cost and Utilization Project)
The Healthcare Cost and Utilization Project (HCUP) is a collaborative effort between AHRQ and the CDC that provides a collection of U.S. healthcare databases for studying healthcare utilization, costs, and outcomes. HCUP data is collected from various sources, standardized, and made available to researchers through data files, reports, and online tools.
So far, HCUP data has been used to study a wide range of healthcare topics and has been instrumental in informing decisions about healthcare delivery, improving healthcare care quality, and reducing healthcare costs.
National Sleep Research Resource (NSRR)
The National Sleep Research Resource (NSRR) is a comprehensive healthcare dataset of sleep study data, including polysomnography (PSG) signals. PSG data allows researchers to analyze sleep patterns, identify sleep disorders, and assess sleep-related issues. The NSRR contributes to advancing sleep research, developing diagnostic tools and treatments, and improving healthcare outcomes for individuals affected by sleep disorders.
CheXpert is a comprehensive healthcare dataset of over 220,000 chest X-ray images, annotated with a wide range of 14 observations, including lung diseases and fractures. The annotations are meticulously performed by experienced radiologists, ensuring high-quality and reliable labels. CheXpert has revolutionized radiology by enabling the development of AI models that can accurately identify and classify thoracic abnormalities.
OMOP (Observational Medical Outcomes Partnership) Common Data Model
The Observational Medical Outcomes Partnership (OMOP) is playing a major role in transforming healthcare research by harmonizing data from diverse observational sources into a standardized format. OMOP’s common healthcare dataset model and standardized vocabulary enable seamless integration of data from various sources, facilitating cross-study comparisons and the identification of patterns and trends. Researchers can investigate a wide range of healthcare questions using OMOP data, contributing to advancements in medical knowledge and evidence-based guidelines.
Genome Aggregation Database (gnomAD)
The Genome Aggregation Database (gnomAD) is a healthcare dataset for exome and genome sequencing data from diverse populations worldwide. It offers an extensive collection of data, including exome sequences from over 140,000 individuals and whole-genome sequences from over 15,000 individuals, representing a broad range of ethnicities and ancestries. The data undergoes stringent quality control measures, ensuring accuracy and reliability. gnomAD provides detailed annotations of variants, including frequency, predicted functional effects, and associations with known traits and diseases.
This healthcare dataset enables researchers to study genetic variation, identify disease-causing mutations, understand the genetic basis of complex traits, and develop personalized medicine approaches.
Conclusion on Healthcare Datasets
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Originally posted on OpenDataScience.com
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