Applying Artificial Intelligence for Real-Time Early Detection of Undesirable Cancer Events

Cancer and its treatment can cause undesirable cancer events (UCEs) that occur frequently and significantly burden patients, their families, and hospitals. UCEs can include worsening symptoms or treatment toxicities (patient-reported), dangerous drops in blood cell counts, acute kidney injury, emergency department visits or hospitalizations, and increased risk of death. Detecting these UCEs early could reduce the frequency and severity of these events and improve patient safety and quality of life by alerting healthcare teams to intervene.

Under the leadership of Dr. Robert Grant, medical oncologist and researcher at the Princess Margaret Cancer Centre, CDI worked to overcome missed or delayed detection of UCEs by developing the first automated early warning system (AIM2REDUCE) at the Cancer Centre. AIM2REDUCE applied machine learning to detect UCEs using wide-ranging data from the electronic medical record (EMR), including treatment data, patient-reported outcomes, and laboratory test results.

AIM2REDUCE generated prediction models for 15 UCEs using historical, longitudinal population-level data from patients with gastrointestinal (GI), lung, and head and neck cancers. The results of these prediction models are novel and very promising. The next stage of this work includes evaluating model performance in the real world, particularly in the GI site group, as GI patients experience the highest rates of UCEs. CDI will safely explore the integration and benefits of early warning systems for UCEs at Princess Margaret, with the goal of deploying these systems in clinical practice, across cancer sites.

Example of how AI-model contouring (middle row) compare to human-generated contouring (top row) and the difference between both (bottom row). The results illustrate how well the AI-model matched the contouring process in cancer treatment planning (Benjamin Haibe-Kain’s Lab, 2022)