Pancreatic cancer risk prediction using deep sequential modeling of longitudinal diagnostic and medication records
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Background
Pancreatic ductal adenocarcinoma (PDAC) is a rare, aggressive cancer often diagnosed late with low survival rates, due to the lack of population-wide screening programs and the high cost of currently available early detection methods.
Methods
To facilitate earlier treatment, we developed an AI-based tool that predicts the risk of pancreatic cancer diagnosis within 6, 12 and 36 months of assessment, using time sequences of diagnostic and medication events from real-world electronic health records (EHRs). Trained on a large US Veterans Affairs dataset with 19,000 PDAC cases and millions of controls, the tool employs a Transformer-based model that can capture and benefit from information synergy between diagnoses and medications.
Findings
Risk prediction is improved when incorporating medication data alongside diagnostic codes. For N patients predicted to be at highest risk out of 1 million, risk of cancer within 3 years is substantially higher than using a reference estimate based on age and gender alone (standard incidence ratio SIR=115 to 70 for N=1000 to 5000). Detection of the most predictive features generates clinical hypotheses such as X and Y. We quantify prediction bias between different socioeconomic subpopulations.
Interpretation
The risk prediction tool is intended to be the first step in a three-step clinical program: identification of high-risk individuals using AI tools, followed by a stratified surveillance program for early detection and intervention, aiming to benefit patients and lower health-care costs.
Funding
US CDMRP Pancreatic Cancer Risk Using Artificial Intelligence.
