[PDF] Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification | Semantic Scholar (2024)

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Results show that deep learning can add substantial prognostic information to the interpretation of 12-lead resting ECGs, even in cases that are interpreted as normal by physicians, and identifies at-risk individuals with seemingly normal electrocardiograms.

Results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice.

The value of CAC for predicting mortality extends to both elderly patients and those <45 years old, and Elderly persons with no CAC have a lower MR than younger persons with high CAC.

A medical multimodal large language model (Med-MLLM) for radiograph representation learning is reported, which can learn broad medical knowledge from unlabelled data and can be used for clinical tasks that involve both visual and textual data.

This work introduces an imbalanced classification framework, based on reinforcement learning, for training extremely imbalanced data sets, and extends it for use in multi-class settings, and forms a custom reward function and episode-training procedure for handling multi-class imbalanced training.

A multi-label semi-supervised model (ECGMatch) to recognize multiple CVDs simultaneously with limited supervision is proposed to pave the way for diagnostic systems that achieve robust performance on multi-label CVDs prediction with limited supervision.

A machine learning algorithm using ECGs surrounding the initial shock predicts patients likely to experience refractory VF, and could enable rescuers to preemptively target interventions to potentially improve resuscitation outcome.

This study uses electronic health record data from a large cohort of patients diagnosed with complicated UTIs to demonstrate high predictability of antibiotic resistance across four antibiotics: nitrofurantoin, co-trimoxazole, ciprofloxacin, and levofloxacins.

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