Abstract: Systems are provided for synthesizing leads of an electrocardiogram (ECG) based on a subject ECG collected from a subject and converting a nonstandard ECG based on a nonstandard placement of electrodes to a standard ECG with a standard placement of electrodes. The described systems may generate simulated ECGs based on simulations of electrical activity of hearts having different heart configurations. From each simulation, simulated ECGs are generated assuming a specification of electrode position(s) for each lead of an ECG. The systems identify a simulated ECG that is similar to the subject ECG. Based on the simulation from which that simulated ECG was generated, the systems identify a synthesized ECG or converted ECG.

Abstract: A system is provided that employs a machine learning (ML) decision tree to provide a treatment analysis for an arrhythmia. The ML decision tree includes decision nodes (non-leaf nodes) and treatment analysis nodes (leaf nodes). Each decision node corresponds to a feature derived from electronic health records and has branches corresponding to feature values. A treatment analysis node corresponds to a treatment analysis based on feature values of a path from the root node to that treatment analysis node. To provide a treatment analysis for a candidate, the system identifies a path from the root node to a treatment analysis node based on a candidate feature vector derived from an electronic health record of the candidate and outputs the treatment analysis of the treatment analysis node of the identified path.