An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study.

Prathyush Sambaturu Parantapa Bhattacharya Jiangzhuo Chen Bryan Leroy Lewis Prashant Rangarajan Srinivasan Venkatramanan Anil Kumar S Vullikanti

Published in: JMIR public health and surveillance (2020)

Our approach, which is an unsupervised machine learning method, can provide new insights into patterns and trends in the disease spread in an automated manner. Our results show that the description complexity is an effective approach for characterizing sets of interest, which can be easily extended to other diseases and regions beyond influenza in the US. Our approach can also be easily adapted for automated generation of narratives.

Keyphrases

machine learning
artificial intelligence
deep learning
big data
high throughput