The article is like four paragraphs.
I think the contribution is better identification of risk factors than were previously known?
The most important variables influencing KECAN included 4 known risk
factors(age, race, sex, BMI) and 9 novel (COPD, greater Hct,lower HDL,
greater LDL, lower serum CO2, lower Na, lower BUN, lower ALT, and
greater WBC).
The AI part seems like a buzzword-y add-on?
"We collected prescriptions, laboratory results, and International Classification of Diseases diagnoses 1 to 5 years prior to index. We randomly divided the cohort into training (50%), preliminary validation (25%), and testing (25%). In the preliminary validation set, simple random sampling imputation and extreme gradient boosting machine learning were most accurate. In the test set, we compared the final model, the Kettles Esophageal and Cardia Adenocarcinoma predictioN (K-ECAN) Tool, to HUNT, Kunzmann, and published guidelines."
I'm 100% not knowledgeable enough to parse that out, but I think maybe they ran XGBoost and did some hyperparameter tuning?
I think the contribution is better identification of risk factors than were previously known?
The AI part seems like a buzzword-y add-on?"We collected prescriptions, laboratory results, and International Classification of Diseases diagnoses 1 to 5 years prior to index. We randomly divided the cohort into training (50%), preliminary validation (25%), and testing (25%). In the preliminary validation set, simple random sampling imputation and extreme gradient boosting machine learning were most accurate. In the test set, we compared the final model, the Kettles Esophageal and Cardia Adenocarcinoma predictioN (K-ECAN) Tool, to HUNT, Kunzmann, and published guidelines."
I'm 100% not knowledgeable enough to parse that out, but I think maybe they ran XGBoost and did some hyperparameter tuning?