Deep learning architecture is better suited to processing such large, diverse datasets, such as the SEER program, according to Doppalapudi. Working on these types of datasets requires robust computational capacity. In this study, the researchers relied on ICDS's Roar supercomputer.
With about 800,000 to 900,000 entries in the SEER dataset, the researchers said that manually finding these associations in the data with an entire team of medical researchers would be extremely difficult without assistance from machine learning.
"If it were just three fields, I would say it would be impossible, but, we had about 150 fields," said Doppalapudi. "Understanding all of those different fields and then reading and learning from that information, would just be near impossible."
Back to HCB News
