ECG2Act: From Abnormality Recognition to Clinical Action Concern Prediction

Electrocardiogram (ECG) analysis informs not only the diagnostic evaluation of cardiac abnormalities but also care planning, which often must proceed before a definitive diagnosis is established. Yet existing ECG deep learning methods mainly focus on abnormality recognition, without explicitly identifying the diagnostic and care-process concerns that guide subsequent clinical assessment and management. We therefore introduce ***clinical action concern prediction***, which jointly identifies problem-oriented diagnostic concerns and care-process concerns from a single ECG to inform subsequent clinical assessment and management. This task presents **two challenges**: the two target families share the same ECG input yet may rely on *different waveform features*, while the concern-level supervision indicates *which concerns are present* without specifying *which ECG characteristics support them*. To address these challenges, we develop **ECG2Act**, which learns a shared ECG representation and uses concern-specific action queries to retrieve target-relevant evidence, while utilizing measurement regression and evidence classification supervision to anchor this representation to interpretable ECG characteristics. On patient-disjoint evaluation of **179,100 ECGs from 82,486 subjects**, ECG2Act achieves diagnostic and care-process AUROCs of **0.938 and 0.713**, respectively. Crucially, care-process concerns remain distinguishable among ECGs sharing the same diagnostic profile, demonstrating that ECG-based care assessment captures information not represented by diagnostic recognition alone. These results support extending current ECG analysis beyond abnormality recognition to jointly assess diagnostic and care-process action concerns.