Name
Abstract | ||
|---|---|---|
Electronic health record (EHR) data is collected by individual institutions and often stored across locations in silos. Getting access to these data is difficult and slow due to security, privacy, regulatory, and operational issues. We show, using ICU data from 58 different hospitals, that machine learning models to predict patient mortality can be trained efficiently without moving health data out of their silos using a distributed machine learning strategy. We propose a new method, called Federated-Autonomous Deep Learning (FADL) that trains part of the model using all data sources in a distributed manner and other parts using data from specific data sources. We observed that FADL outperforms traditional federated learning strategy and conclude that balance between global and local training is an important factor to consider when design distributed machine learning methods , especially in healthcare. |
Year | Venue | DocType |
|---|---|---|
2018 | arXiv: Computers and Society | Journal |
Volume | ISSN | Citations |
abs/1811.11400 | Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 | 0 |
PageRank | References | Authors |
0.34 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dianbo Liu | 1 | 2 | 2.72 |
| Tim Miller | 2 | 63 | 8.17 |
| Raheel Sayeed | 3 | 0 | 1.01 |
| Kenneth D. Mandl | 4 | 275 | 67.17 |