Best Healthcare Datasets for Machine Learning & Research 2024
With our growing and ageing population, healthcare services globally are under increasing strain to provide the best treatment as swiftly as possible. To maximize efficiency, and ultimately to improve patient outcomes, many countries are investing in AI and ML for healthcare. The aim is to replace manual processes with machines so that physicians, dentists, nurses and other healthcare professionals can spend precious time with critical cases.
However, AI and ML systems must be 100% reliable in order to be a viable substitute for a human medic. This means that they’re trained using quality data, and lots of it. In a medical context, the consequences of badly trained AI could really be a matter of life and death. How can clinical researchers, medtech entrepreneurs and policy-makers access the healthcare data necessary for reliable machine learning?
We’ve gathered the best healthcare datasets from trusted healthcare data providers. Their datasets are assembled using pharma, electronic health record (EHR), dentistry, patient, drug, demographic and economic data points. They’re aggregated and de-anonymized to protect patient privacy, and statistics are validated against government sources. Researchers and medical developers are using these datasets to deploy AI and ML cogently and improve healthcare worldwide. Check them out and view data samples.
Electronic health record (EHR) dataset from OmniSol. View dataset →
OmniSol collects and aggregates electronic health records to compile datasets on patient’s diagnosis, biology, treatment required, medicine prescribed, hospital or facility visited, and outcomes. This dashboard helps healthcare providers monitor and improve clinical performance, identify areas for intervention, and ensure quality patient care. Researchers and developers use OmniSol’s datasets to train their ML system based on real health records.
Pharma dataset from Centillion. View dataset →
Centillion’s datasets shows monthly spending on drugs and medicare amongst US consumers. It enables researchers to track rising drug demand and facilitates the analysis of historical price fluctuations. For this reason, it can be used to build predictive ML models on the cost and demand for certain treatments based on current medical claims data.
Medical imagery dataset from Pixta AI. View dataset →
Pixta AI is an AI & ML training data provider. Their offering include a rich repository of annotated medical imagery data for various parts of the anatomy. Their multimodal medical images include x-ray scans, CT and MRI datasets, breast mammograms, and regression datasets. Alongside research and ML use cases, Pixta AI’s medical data is used for remote diagnosis and
Electronic health record (EHR) dataset from Syntegra. View dataset →
Syntegra’s dataset capture key data points including a patient’s demography, vital signs, lab results inc. toxicology reports, drugs prescribed, and physicians present. The company’s synthetic data engine is trained on a broadly representative dataset of US patients, made up of deep clinical information of approximately 6 million unique patient records and 18 million encounters over 5 years of history.
Telemedicine dataset from Gambit. View dataset →
Gambit provides a data repository of at-home medical diagnostics, OTC, and telemedicine based wellness product purchases consists of a variety of consumer level purchases spanning multiple companies, products, and test applications including wellness screening, treatments, and diagnosis of multiple ailments. Gambit’s data is aggregated via publicly available sources using proprietary methodologies, so it offers the scale for representative research training a reliable ML model.
Healthcare dataset from HealthWise Data. View dataset →
HealthWise Data collects propensities around an individual's lifestyle when it comes to health, with indicators such as diet, tobacco use, exercise and more. The dataset spans 265 million US adults. This predictive clinical data offers a more holistic view of the patient as a person and can be used for predictive ML modelling.
Medical claims dataset from Diaceutics. View dataset →
Diaceutics’ dataset spans 81 million patients covering all medical claims including testing, treatments and procedures. The data is labelled with disease, biomarker, testing methodology, as well as key labels specific to oncology. It’s used for developing treatments, as well as enhancing medicare and insurance options.
With our growing and ageing population, healthcare services globally are under increasing strain to provide the best treatment as swiftly as possible. To maximize efficiency, and ultimately to improve patient outcomes, many countries are investing in AI and ML for healthcare. The aim is to replace manual processes with machines so that physicians, dentists, nurses and other healthcare professionals can spend precious time with critical cases.
However, AI and ML systems must be 100% reliable in order to be a viable substitute for a human medic. This means that they’re trained using quality data, and lots of it. In a medical context, the consequences of badly trained AI could really be a matter of life and death. How can clinical researchers, medtech entrepreneurs and policy-makers access the healthcare data necessary for reliable machine learning?
We’ve gathered the best healthcare datasets from trusted healthcare data providers. Their datasets are assembled using pharma, electronic health record (EHR), dentistry, patient, drug, demographic and economic data points. They’re aggregated and de-anonymized to protect patient privacy, and statistics are validated against government sources. Researchers and medical developers are using these datasets to deploy AI and ML cogently and improve healthcare worldwide. Check them out and view data samples.
Electronic health record (EHR) dataset from OmniSol. View dataset →
OmniSol collects and aggregates electronic health records to compile datasets on patient’s diagnosis, biology, treatment required, medicine prescribed, hospital or facility visited, and outcomes. This dashboard helps healthcare providers monitor and improve clinical performance, identify areas for intervention, and ensure quality patient care. Researchers and developers use OmniSol’s datasets to train their ML system based on real health records.
Pharma dataset from Centillion. View dataset →
Centillion’s datasets shows monthly spending on drugs and medicare amongst US consumers. It enables researchers to track rising drug demand and facilitates the analysis of historical price fluctuations. For this reason, it can be used to build predictive ML models on the cost and demand for certain treatments based on current medical claims data.
Medical imagery dataset from Pixta AI. View dataset →
Pixta AI is an AI & ML training data provider. Their offering include a rich repository of annotated medical imagery data for various parts of the anatomy. Their multimodal medical images include x-ray scans, CT and MRI datasets, breast mammograms, and regression datasets. Alongside research and ML use cases, Pixta AI’s medical data is used for remote diagnosis and
Electronic health record (EHR) dataset from Syntegra. View dataset →
Syntegra’s dataset capture key data points including a patient’s demography, vital signs, lab results inc. toxicology reports, drugs prescribed, and physicians present. The company’s synthetic data engine is trained on a broadly representative dataset of US patients, made up of deep clinical information of approximately 6 million unique patient records and 18 million encounters over 5 years of history.
Telemedicine dataset from Gambit. View dataset →
Gambit provides a data repository of at-home medical diagnostics, OTC, and telemedicine based wellness product purchases consists of a variety of consumer level purchases spanning multiple companies, products, and test applications including wellness screening, treatments, and diagnosis of multiple ailments. Gambit’s data is aggregated via publicly available sources using proprietary methodologies, so it offers the scale for representative research training a reliable ML model.
Healthcare dataset from HealthWise Data. View dataset →
HealthWise Data collects propensities around an individual's lifestyle when it comes to health, with indicators such as diet, tobacco use, exercise and more. The dataset spans 265 million US adults. This predictive clinical data offers a more holistic view of the patient as a person and can be used for predictive ML modelling.
Medical claims dataset from Diaceutics. View dataset →
Diaceutics’ dataset spans 81 million patients covering all medical claims including testing, treatments and procedures. The data is labelled with disease, biomarker, testing methodology, as well as key labels specific to oncology. It’s used for developing treatments, as well as enhancing medicare and insurance options.
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