Can artificial intelligence help predict the risk of pancreatic cancer using disease trajectories? NatureMedicine UCPH_Research harvardmed AI ai machinelearning ML artificialintelligence ainews airesearch cancer research cancerresearch
By Neha MathurMay 10 2023Reviewed by Lily Ramsey, LLM In a recent article published in Nature Medicine, researchers applied artificial intelligence methods to real-world longitudinal clinical data to design surveillance programs for the early detection of patients at elevated risk of one of the most aggressive diseases, pancreatic cancer.
While age is a recognized risk factor for pancreatic cancer, age-based population-wide screening is impractical due to the high cost of clinical testing, which also yields false-positive results. The former comprised data for 8.6 million patients captured between 1977 and 2018, corresponding to 24,000 pancreatic cancer cases, whereas the latter had clinical data of three million patients with 3,900 pancreatic cancer cases.
Further, the researchers evaluated the prediction performance of the different models trained in the DNPR using the area under the receiver operating characteristic and relative risk curves. In addition, they reported ML-derived RR scores of patients with cancer in the high-risk group. The performance of the bag-of-words model and the MLP model for predicting cancer occurrence within 36 months in terms of AUROC were 0.807 and 0.845, respectively. However, compared to Transformer, the RRs for bag-of-words and MLP were much lower .
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