New AI Model Predicts Cancer Patient Survival More Accurately Than Previous Methods

Researchers from the University of British Columbia and BC Cancer have created an AI model that predicts cancer patient survival with greater accuracy and using more readily accessible data compared to previous methods.

The AI model utilizes natural language processing (NLP), a field of AI that comprehends human language, to examine oncologists’ notes taken following a patient’s initial consultation. This is the first step in a cancer patient’s journey after diagnosis. The model was able to identify distinctive features for each patient, resulting in survival predictions with over 80% accuracy for 6 months, 36 months, and 60 months. These findings were recently published in the JAMA Network Open.

“Predicting cancer survival is an important factor that can be used to improve cancer care,” said lead author Dr. John-Jose Nunez, a psychiatrist and clinical research fellow with the UBC Mood Disorders Centre and BC Cancer. “It might suggest health providers make an earlier referral to support services or offer a more aggressive treatment option upfront. Our hope is that a tool like this could be used to personalize and optimize the care a patient receives right away, giving them the best outcome possible.”

Traditionally, cancer survival rates have been calculated retrospectively and categorized by only a few generic factors such as cancer site and tissue type. Despite familiarity with these rates, it can be challenging for oncologists to accurately predict an individual patient’s survival due to the many complex factors that influence patient outcomes.

The model developed by Dr. Nunez and his collaborators, which includes researchers from BC Cancer and UBC’s departments of computer science and psychiatry, is able to pick up on unique clues within a patient’s initial consultation document to provide a more nuanced assessment. It is also applicable to all cancers, whereas previous models have been limited to certain cancer types.

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