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Aim Reduce


AIM    REDUCE program:

stands for An Intelligent Machine-learning Model for Real-time Early Detection of Undesirable Cancer Events

  • Project 1: Natural language processing (NLP) methodologies to extract accurate and detailed information from radiology reports

  • Project 2: NLP methodologies to identify the presence of precancerous lesions in surgical pathology reports


Empowering Medicine Through  Machine Learning :AIM2REDUCE

As cancer care improves and patients live longer, there has been an increased emphasis on quality-of-life factors, particularly for symptom and toxicity management. Recent research has shown that rapid reporting and management of symptoms and toxicities is associated with a 30% improvement in survival for patients with advanced-stage cancer, rivalling the most effective novel therapeutics.


At The Princess Margaret, we have vast amounts of complex clinical data that, historically, has been difficult to access, curate, and leverage for research. Harnessing real-world evidence, particularly relating to cancer symptoms and treatment toxicities, has the power to change how patients are treated at our institution and beyond.


2BLAST: Biostatistical and Bioinformatic Longitudinal Analysis of Symptoms and Toxicities

In collaboration withThe Princess Margaret Data Science Team, we have completed the 2BLAST Data Catalyst Project to build institutional infrastructure for accessing, collecting, and validating real-world clinical evidence for research.Through this work, our team developed approaches for curating clinical data from many different sources. We now have raw clinical data such as notes, radiology reports, and laboratory values for more than 60,000 patients with cancer over a period of 18 years, making this the richest dataset of this nature in Canada.


AIM2REDUCE:An Intelligent Machine-learning Model for Real-time Early Detection ofUndesirableCancerEventsLeveraging the vast clinical data now available, we are building a computational core facility, supported by other researchers and clinicians with relevant expertise, to develop tools that use artificial intelligence to accurately extract clinical data from free-text notes.So far, our computer science students have developed natural language processing(NLP) methodologies that can successfully extract accurate and detailed information from radiology reports, as well as identify the presence of precancerous lesions in surgical pathology reports. This enables the identification and retrieval of archival tissue for foundational research into molecular biomarkers of pre-cancer, with the goal of preventing cancer entirely.


Recognizing that large-scale data analysis is the future of improved healthcare, we are now starting two major initiatives that leverage the 2BLAST dataset for state-of-the-art artificial intelligence algorithms to predict and prevent, in real time, adverse cancer outcomes–such as emergency room visits and death–through early and appropriate intervention.We are in need of transitional funding to support our pilot efforts until we are able to obtain peer-review funding. Eventually we hope to leverage these methods across our CARMA-BROS network across Canada

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