# ECG for high-throughput screening of multiple diseases: Proof-of-concept using multi-diagnosis deep learning from population-based datasets

arXiv: https://arxiv.org/abs/2210.06291 · HTML: https://mlsanity.com/paper/2210.06291/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 23 references that are in the map.
- First posted on arXiv 6 Oct 2022.

## Builds on (most cited first, 2 of 2)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Automatic diagnosis of the 12-lead ECG using a deep neural network](https://mlsanity.com/paper/1904.01949/) (2019)

## Built on by (most cited first, 4 of 4)

- [CLEF: Clinically-Guided Contrastive Learning for Electrocardiogram Foundation Models](https://mlsanity.com/paper/2512.02180/) (2025)
- [Federated Learning and Differential Privacy Techniques on Multi-hospital Population-scale Electrocardiogram Data](https://mlsanity.com/paper/2405.00725/) (2024)
- [Prospects for AI-Enhanced ECG as a Unified Screening Tool for Cardiac and Non-Cardiac Conditions -- An Explorative Study in Emergency Care](https://mlsanity.com/paper/2312.11050/) (2023)
- [Improving ECG-based COVID-19 diagnosis and mortality predictions using pre-pandemic medical records at population-scale](https://mlsanity.com/paper/2211.10431/) (2022)

## Abstract (arXiv)

Electrocardiogram (ECG) abnormalities are linked to cardiovascular diseases, but may also occur in other non-cardiovascular conditions such as mental, neurological, metabolic and infectious conditions. However, most of the recent success of deep learning (DL) based diagnostic predictions in selected patient cohorts have been limited to a small set of cardiac diseases. In this study, we use a population-based dataset of >250,000 patients with >1000 medical conditions and >2 million ECGs to identify a wide range of diseases that could be accurately diagnosed from the patient's first in-hospital ECG. Our DL models uncovered 128 diseases and 68 disease categories with strong discriminative performance.