搜索医学 AI 论文与资源

The PhysioNet/CinC Challenge 2012 dataset contains ICU time-series records used for mortality prediction and patient-specific outcome modeling. It remains a useful benchmark for clinical time-series modeling, missingness-aware learning, and early warning model development.

UK Biobank Imaging provides large-scale imaging phenotypes linked to genetic, lifestyle, and health outcome data. It is used for population-scale medical imaging AI, disease risk prediction, representation learning, multimodal biomedical modeling, and epidemiological AI studies.

The Cancer Genome Atlas is a large cancer genomics resource with molecular, clinical, and pathology-related data across many cancer types. It is a foundation dataset for oncology AI, survival prediction, subtype discovery, multimodal cancer modeling, and translational biomarker research.

OASIS provides open-access neuroimaging datasets for studying normal aging, dementia, and brain structure. It is useful for brain MRI segmentation, age prediction, dementia classification, longitudinal modeling, and neuroimaging method benchmarking.

ADNI provides longitudinal neuroimaging, biomarker, clinical, and cognitive data for Alzheimer disease research. It supports disease progression modeling, dementia diagnosis, multimodal prediction, biomarker discovery, and clinical translation studies.

Sleep-EDF Expanded contains polysomnographic sleep recordings with EEG and related physiological signals. It is used for sleep stage classification, biosignal time-series modeling, self-supervised learning on physiological signals, and clinical sleep research benchmarks.

PTB-XL is a large public 12-lead electrocardiography dataset with diagnostic statements and waveform records. It is a standard benchmark for ECG classification, cardiac abnormality detection, clinical signal representation learning, and robust evaluation of biosignal models.

The eICU Collaborative Research Database is a multicenter critical care database containing deidentified ICU data from many hospitals. It is commonly used for external validation, ICU outcome prediction, temporal modeling, and cross-site generalization studies in clinical AI.

MIMIC-IV-ECG is a large deidentified electrocardiogram dataset linked to the MIMIC-IV clinical data ecosystem. It supports ECG classification, arrhythmia detection, representation learning, and multimodal modeling with structured EHR context.

MIMIC-IV-Note provides deidentified clinical notes linked to MIMIC-IV hospital data. It supports clinical NLP tasks such as note representation learning, discharge summary modeling, information extraction, summarization, and multimodal EHR-text modeling.

MIMIC-IV is a large deidentified electronic health record dataset from Beth Israel Deaconess Medical Center, covering hospital and ICU data for critical care research. It is a core benchmark source for clinical prediction, temporal EHR modeling, phenotyping, and healthcare AI method development.

Large publicly available 12-lead ECG waveform dataset with diagnostic labels, hosted on PhysioNet.

Deidentified EHR data for ICU and hospital patients at Beth Israel Deaconess Medical Center, distributed through PhysioNet with credentialed access.

Multimodal clinical data foundation and benchmark introduced at ICML 2025 for clinical foundation model research.