Abstract: Embodiments of the present disclosure utilize DNNs for ECG interpretation, where original ECG waveforms are directly ingested by the DNNs using paired interpretation labels for training, without the need for explicatory feature extraction or rule-based criteria.

Abstract: Disclosed are techniques for training a machine learning model. A training data set comprising a plurality of electrocardiogram (ECG) measurements and corresponding analyte measurements for each of the plurality of ECG measurements is provided. For each ECG measurement of the training data set, an estimated analyte level at a time of the ECG measurement is determined based on the corresponding set of analyte measurements. The estimated analyte level may be determined using statistical estimation techniques. If it is determined that the estimated analyte level at the time of the ECG measurement meets a certainty threshold, the ECG measurement is labeled based on the estimated analyte level at the time of the ECG measurement. A machine learning is trained to predict a level of an analyte based on ECG data, where the training is done using each of the plurality of ECG measurements that are labeled.

Abstract: Disclosed systems include an electrocardiogram sensor and a processing device operatively coupled to the electrocardiogram sensor. The processing device receives electrocardiogram data from the electrocardiogram sensor and applies a machine learning model to the received electrocardiogram data. The machine learning model has been trained based on previous electrocardiogram data of a plurality of subjects. The electrocardiogram data of the plurality of subjects have one or more associated analyte measurements. The processing device may determine an indication of a level of the analyte based on the electrocardiogram data.

Abstract: Embodiments of the present disclosure utilize DNNs for ECG interpretation, where original ECG waveforms are directly ingested by the DNNs using paired interpretation labels for training, without the need for explicatory feature extraction or rule-based criteria.

Abstract: 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.

Abstract: Disclosed systems include an electrocardiogram sensor and a processing device operatively coupled to the electrocardiogram sensor. The processing device receives electrocardiogram data from the electrocardiogram sensor and applies a machine learning model to the received electrocardiogram data. The machine learning model has been trained based on previous electrocardiogram data of a plurality of subjects. The electrocardiogram data of the plurality of subjects have one or more associated analyte measurements. The processing device may determine an indication of a level of the analyte based on the electrocardiogram data.

Abstract: In methods and systems, a textual portion of an electronic message from a first user device to a second user device is received. One or more contextual tags to represent a context of the textual portion are recommended. The recommending the one or more contextual tags is based at least in part on the receiving the textual portion. One or more rich media elements are recommended to represent the context of the textual portion. The recommending the one or more rich media elements is based at least in part on the one or more contextual tags. The one or more rich media elements and the textual portion are integrated into a rich media message. The rich media message is provided to the second device.