Katharina Buczek
“These documents have many details like the age of the patient, the type of cancer, underlying health conditions, past substance abuse, and family histories. The AI brings all of this together to paint a more complex picture of patient outcomes,” he said.
In order to train and test the model, the researchers used data from 47,625 patients across six BC Cancer sites located in British Columbia. All patient data also remained securely stored at BC Cancer and was anonymously presented to protect privacy.
So, unlike chart evaluations conducted by humans, the AI model also offers the added benefit of complete patient confidentiality.
Now, the model was trained using British Columbia data– meaning that it is a robust tool for cancer survival predictions in that province. Moving forward, though, the researchers are confident that the technology could also be applied in other cancer clinics around the globe.
“The great thing about neural NLP models is that they are highly scalable, portable, and don’t require structured data sets,” Dr. Nunez detailed.
“We can quickly train these models using local data to improve performance in a new region. I would suspect that these models provide a good foundation anywhere in the world where patients are able to see an oncologist.”
To read the study’s complete findings, which have since been published in JAMA Network Open, visit the link here.
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