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Medical imaging (X-ray, CT, MRI), electronic health records, clinical trials, ECG/EEG, pathology
13,569 datasets
15,000 patient studies from the MIMIC-CXR database featuring chest radiograph images and associated clinical metadata. These records provide a foundation for developing diagnostic algorithms and performing medical imaging research on a manageable scale.
768 labeled records across 8 medical predictor variables and one binary outcome class. These entries document physiological measurements from Pima Indian women to identify risk factors for diabetes onset.
3 categories of agricultural imagery covering weed detection, panicle segmentation, and paddy leaf disease identification. The dataset provides visual samples for diagnosing crop health and identifying invasive species in rice fields.
Multiple categories of neurological symptom data organized for the classification of migraine types. These structured clinical features support medical machine learning tasks and headache disorder prediction.
Two categories of skin lesion images facilitate binary classification between Monkeypox and Non-monkeypox cases. The collection includes specific visual examples of Chickenpox and Measles to support differential diagnosis of viral rashes.
300 uterine electrohysterogram (EHG) recordings categorized into 262 term and 38 preterm deliveries. Each record includes three-channel EHG signals sampled at 20 Hz along with clinical metadata such as maternal age and pregnancy duration.
3 labeled skin tone categories including White, Brown, and Black classes for image classification. The dataset provides a structured framework for training models to distinguish between specific human skin pigmentation levels.
Two data modalities, ultrasound images and numeric clinical records, are categorized into Thrombus and Non-Thrombus classes for lower limb diagnosis. The dataset is sourced from the ThrombUS+ clinical site to facilitate automated deep vein thrombosis detection.
Multiple chest X-ray images across several pneumonia pathology classes. The data enables the classification of specific infectious etiologies through labeled medical imagery.
Two categories of geographic sales data covering multiple cities and countries. Commercial performance metrics are documented for a restaurant company's international operations.
30 disease categories mapped to synthetic patient symptom profiles for classification tasks. The data provides a structured mapping between clinical signs and diagnostic labels to facilitate automated medical categorization.
Clinical and socioeconomic patient records categorized by medical history and social determinants for 30-day hospital readmission prediction. The data enables the development of risk assessment models that integrate health outcomes with environmental factors.
780 breast ultrasound images categorized into normal, benign, and malignant classes. The dataset includes corresponding ground truth segmentation masks for each image to facilitate automated lesion detection and boundary delineation.
Retina images for diabetic retinopathy detection, pre-processed with Gaussian filtering and resized to 224x224 pixels. These standardized images facilitate the training of medical diagnostic models without requiring additional preprocessing steps.
10,015 dermatoscopic images of pigmented skin lesions across seven diagnostic categories. The dataset provides ground truth labels for both multi-class classification and pixel-level lesion segmentation tasks.
Financial performance records and strategic review metrics for the leading pharmaceutical entities in Bangladesh. The data categorizes fiscal points to enable analysis of the sector's top market participants.
Patient health records and clinical risk factors are the primary categories used to analyze heart attack occurrences. The data focuses on clinical indicators and health metrics associated with cardiovascular events. It is designed to support the identification of patterns in patient health that precede heart attack incidents.
Patient records categorized by demographics, health metrics, and engagement patterns. The data enables the identification of distinct patient cohorts for personalized healthcare delivery and resource optimization.
Synthetic patient-level records spanning hospital journey and readmission risk categories. These longitudinal clinical pathways facilitate healthcare simulation and outcome analysis.
4-class MRI image dataset provides categorized brain scans across four distinct dementia severity levels. These labels support the development of multi-stage diagnostic models for Alzheimer's disease progression and clinical classification.