搜索医学 AI 论文与资源

ICLR 2026 Poster accepted paper at ICLR 2026. Healthcare data, such as Intensive Care Unit (ICU) records, comprise heterogeneous multivariate time series sampled at irregular intervals with pervasive missingness. However, clinical applications demand predictive models that are both accurate and interpretable. We present our Graph Attention-based Relational Learning for Intensive Care (GARLIC) model, a novel neural network architecture that imputes missing data through a learnable exponential-decay encoder, captures inter-sensor dependencies via time-lagged summary graphs, and fuses global patterns with cross-dimensional sequential attention. All attention weights and graph edges are learned end-to-end to serve as built-in observation-, signal-, and edge-level explanations.

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.

VinDr-CXR is a chest X-ray dataset with radiologist annotations from Vietnamese hospitals. It supports abnormality classification, lesion localization, radiology object detection, and robustness studies across clinical sites and populations.

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-CXR is a large deidentified chest radiograph dataset with associated free-text radiology reports. It is widely used for chest X-ray classification, report generation, image-text representation learning, radiology retrieval, and medical multimodal foundation model evaluation.

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.

JPG-formatted chest radiographs with labels derived from free-text reports, hosted by PhysioNet.

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

PhysioNet Challenge 2026 on detecting cognitive impairment from polysomnography and related clinical signals.