ECG SEARCH AND INTERPRETATION BASED ON A DUAL ECG AND TEXT EMBEDDING MODEL

Embodiments of the present disclosure provide systems and methods for performing an ECG search based on a dual ECG and text embedding model. A text machine learning (ML) model may be trained to generate a text embedding based on a received text representation of an ECG diagnosis. The text ML model may be used to train an ECG encoding ML model to generate an ECG embedding based on received ECG leads data. A database may be populated with a plurality of ECG embeddings, each of the plurality of ECG embeddings generated based on ECG leads data of previously diagnosed ECGs. In response to receiving a query ECG, the ECG ML model may generate a query embedding and a similarity score between the query embedding and each of the plurality of ECG embeddings may be determined. The top K results may be sorted based on similarity score, and may be displayed/visualized.