Most of the points are naive and confuse predictive power with interpretability (the latter is harder). For example: > Did you know that a blood oxygen saturat

Most of the points are naive and confuse predictive power with interpretability (the latter is harder). For example: > Did you know that a blood oxygen saturation of 0% is highly correlated with healthy outcomes? No, I didn’t get the percentage backwards. A 0% reading is what you get when the nurse looks at you and decides you’re too obviously healthy to bother with putting the pulse oximeter on your finger. The empty field value gets saved as a 0, of course. Well, statistical methods (no matter if classical, Bayesian, Deep Learning, or anything, as long as it goes beyond linear methods) will perfectly capture the special case of 0% and predict accordingly. These methods are free of our biases and will take consistent approaches (e.g. empty columns, columns that de facto mean something else, common typos, etc). Sure, interpretability is problematic, and we often need to consider the knowledge of physicians and medical institutions rather than be based on raw data.