We present a model for predicting electrocardiogram (ECG) abnormalities in shortduration 12-lead ECG signals which outperformed medical doctors on the 4th year of their cardiology residency. Such exams can provide a full evaluation of heart activity and have not been studied in previous end-to-end machine learning papers. Using the database of a large telehealth network, we built a novel dataset with more than 2 million ECG tracings, orders of magnitude larger than those used in previous studies. Moreover, our dataset is more realistic, as it consist of 12-lead ECGs recorded during standard in-clinics exams. Using this data, we trained a residual neural network with 9 convolutional layers to map 7 to 10 second ECG signals to 6 classes of ECG abnormalities. Future work should extend these results to cover a large range of ECG abnormalities, which could improve the accessibility of this diagnostic tool and avoid wrong diagnosis from medical doctors. IntroductionCardiovascular diseases are the leading cause of death worldwide [1] and the electrocardiogram (ECG) is a major diagnostic tool for this group of diseases. As ECGs transitioned from analogue to digital, automated computer analysis of standard 12-lead electrocardiograms gained importance in the process of medical diagnosis [2]. However, limited performance of classical algorithms [3,4] precludes its usage as a standalone diagnostic tool and relegates it to an ancillary role [5].End-to-end deep learning has recently achieved striking success in task such as image classification [6] and speech recognition [7], and there are great expectations about how this technology may improve health care and clinical practice [8][9][10]. So far, the most successful applications used a supervised learning setup to automate diagnosis from exams. Algorithms have achieved better performance than a human specialist on their routine workflow in diagnosing breast cancer [11] and detecting certain eye conditions from eye scans [12]. While efficient, training deep neural networks using supervised learning algorithms introduces the need for large quantities of labeled data which, for medical applications, introduce several challenges, including those related to confidentiality and security of personal health information [13].Standard, short-duration 12-lead ECG is the most commonly used complementary exam for the evaluation of the heart, being employed across all clinical settings: from the primary care centers to the intensive care units. While tracing cardiac monitors and long-term monitoring, as the Holter exam, provides information mostly about cardiac rhythm and repolarization, 12-lead ECG can provide a full evaluation of heart, including arrhythmias, conduction disturbances, acute coronary syndromes, cardiac chamber hypertrophy and enlargement and even the effects of drugs and electrolyte disturbances.Machine Learning for Health (ML4H) Workshop at NeurIPS 2018.