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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?



It's deep in the 'just statistics' territory of ML/AI by the sounds of it, yeah. This is just a classification problem (cancer, not cancer) with a bunch of available patient data spanning different dimensions, four of them known risk factors, and regression analysis to find others (well, re-discovering/confirming those four too) that correlate.

I imagine at least the title here is a groan for the paper authors.


That is machine learning or more colloquially: AI. A pretty cool result regardless.




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