A groundbreaking research initiative at the University of Alberta is harnessing the power of machine learning to foresee the mental and physical health trajectory of Canadians as they age. Spearheaded by Cloud Cao, an associate professor in the department of psychiatry and a Canada Research Chair in Computational Psychiatry, this innovative project aims to provide valuable insights for improved patient care decisions.
Unveiling Two Recent Studies
1. Biological Age Index and Lifestyle Choices
Cao's team unveiled a Biological Age Index, utilizing blood markers to determine an individual's BioAge, which is then compared to their chronological age. Lifestyle factors, social economics, and cognitive function were considered in the study. Notably, poor lifestyle choices can lead to a positive BioAge, indicating potential health challenges, while positive lifestyle choices result in a negative BioAge, signifying better health prospects.
2. Predicting Onset of Depression
The team embarked on a study predicting the onset of depression within three years. By collecting baseline data, including personality measures and perceived health, and conducting follow-ups, the researchers achieved a 70% accuracy rate in predicting participants' likelihood of developing depression based solely on the baseline data.
Bridging the Gap to Practical Implementation
Despite promising results, Cao emphasizes that implementing machine learning for predicting future health in Canada is a significant undertaking that remains on the horizon. The ultimate goal is to leverage this data to make informed predictions about individuals' health and aging status, striving for the best possible outcomes.
Future Endeavors: Refining Models for Practical Application
Looking ahead, Cao envisions refining the models over the next three to five years, incorporating more extensive data, a larger population, and additional influencing factors. This iterative process aims to enhance the accuracy of predictions and move these models beyond the research domain, bringing them closer to practical, real-world applications.
In the quest for improved healthcare outcomes, Cloud Cao and his team exemplify the potential of machine learning to revolutionize how we approach and anticipate the health challenges of an aging population in Canada.