The Helicobacter pylori AI-Clinician: Harnessing Artificial Intelligence to Personalize H. pylori Treatment Recommendations
Journal:
arXiv
Published Date:
Dec 7, 2024
Abstract
Helicobacter pylori (H. pylori) is the most common carcinogenic pathogen
worldwide. Infecting roughly 1 in 2 individuals globally, it is the leading
cause of peptic ulcer disease, chronic gastritis, and gastric cancer. To
investigate whether personalized treatments would be optimal for patients
suffering from infection, we developed the H. pylori AI-clinician
recommendation system. This system was trained on data from tens of thousands
of H. pylori-infected patients from Hp-EuReg, orders of magnitude greater than
those experienced by a single real-world clinician. We first used a simulated
dataset and demonstrated the ability of our AI Clinician method to identify
patient subgroups that would benefit from differential optimal treatments.
Next, we trained the AI Clinician on Hp-EuReg, demonstrating the AI Clinician
reproduces known quality estimates of treatments, for example bismuth and
quadruple therapies out-performing triple, with longer durations and higher
dose proton pump inhibitor (PPI) showing higher quality estimation on average.
Next we demonstrated that treatment was optimized by recommended personalized
therapies in patient subsets, where 65% of patients were recommended a bismuth
therapy of either metronidazole, tetracycline, and bismuth salts with PPI, or
bismuth quadruple therapy with clarithromycin, amoxicillin, and bismuth salts
with PPI, and 15% of patients recommended a quadruple non-bismuth therapy of
clarithromycin, amoxicillin, and metronidazole with PPI. Finally, we determined
trends in patient variables driving the personalized recommendations using
random forest modelling. With around half of the world likely to experience H.
pylori infection at some point in their lives, the identification of
personalized optimal treatments will be crucial in both gastric cancer
prevention and quality of life improvements for countless individuals
worldwide.
