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 on average the AI Clinician reproduces known quality estimates of treatment decision making, 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.

The COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has posed significant health challenges worldwide. While respiratory symptoms have been the primary focus, emerging evidence has highlighted the impact of COVID-19 on various organ systems, including the gastrointestinal (GI) tract. This study, based on data from 913 post-COVID-19 patients in Iraq collected during 2022 and 2023, investigates the prevalence and patterns of GI symptoms in individuals recovering from COVID-19 and leverages machine learning algorithms to identify predictive factors for these symptoms. The research findings reveal that a notable percentage of post-COVID-19 patients experience GI symptoms during their recovery phase. Diarrhea emerged as the most frequently reported symptom, followed by abdominal pain and nausea. Machine learning analysis uncovered significant predictive factors for GI symptoms, including age, gender, disease severity, comorbidities, and the duration of COVID-19 illness. These findings underscore the importance of monitoring and addressing GI symptoms in post-COVID-19 care, with machine learning offering valuable tools for early identification and personalized intervention. This study contributes to the understanding of the long-term consequences of COVID-19 on GI health and emphasizes the potential benefits of utilizing machine learning-driven analysis in predicting and managing these symptoms. Further research is warranted to delve into the mechanisms underlying GI symptoms in COVID-19 survivors and to develop targeted interventions for symptom management. Keywords: COVID-19, gastrointestinal symptoms, machine learning, predictive factors, post-COVID-19 care, long COVID.