Paleo Baby

It’s been about three years since NBT began using supervised machine learning to predict the results of more expensive or unattainable biomedical tests. With our bloodsmart.ai software, we can forecast infections and inflammation, xenobiotic and heavy metal toxicity, and metabolic health indicators like fatty liver and elevated insulin - all without directly testing these markers. As a result, we’ve dramatically shifted our clinical work away from direct testing, instead focusing on basic blood chemistry and supervised machine learning to guide decision making. It's one of the things I'm proudest of building. Sometimes I get asked how bloodsmart.ai compares to other blood chemistry programs. I used the other programs for years before coding my own, and rather than ML, they use what I call “hand-rolled algorithms.” For example, if alkaline phosphatase is low, then it must be a zinc deficiency. Unfortunately, biology is way more complicated than that, and supplementing with zinc with just one indicator never he