Jan. 25, 2018

We’ve switched things up for this episode, with Dr. Bryan Walsh asking the questions and me on the other side of the microphone.  We’re talking about our new Blood Chemistry Calculator – the product of lab data from tens of thousands of people and a machine learning algorithm called XGBoost.  The calculator analyzes a simple, inexpensive set of blood markers for patterns and immediately forecasts the probability that you’ve got any of a long list of deficiencies, overloads, and even infections - without directly testing for any of them.

Bryan and I discuss all the details, including the science behind the calculator, how you can use this tool to track progress over time, and how the calculator is a game-changer for practitioners.  If you’re ready to dive in and see what it can do for you, check out the calculator now.

Here’s the outline of this interview with Dr Bryan Walsh:

[00:02:52] Chris's blood chemistry journey.

[00:04:11] Podcast with Dr. Bryan Walsh: Risk Assessment in the Genomic Era: Are We Missing the Low-Hanging Fruit?

[00:04:36] How Tommy looks at blood chemistry.

[00:06:18] Study: Hu, Frank B., Ambika Satija, and JoAnn E. Manson. “Curbing the diabetes pandemic: the need for global policy solutions.” Jama 313.23 (2015): 2319-2320.

[00:07:32] Decision tables, Functional Blood Chemistry seminar, Denver, March 2017.

[00:10:27] Machine Learning.

[00:11:10] Dogs vs Cats, Deep Convolutional Neural Network.

[00:15:05] Pima Indians dataset. Note there are just 768 instances in this dataset and not thousands (as I said in the audio). This is important because that’s still enough to build a reasonably accurate model using XGBoost.

[00:18:02] Elite Performance Program.

[00:18:55] GlycoMark.

[00:19:04] Podcast: Why You Should Skip Oxaloacetate Supplementation, Fueling for Your Activity and More!

[00:19:25] Ceruloplasmin, adiponectin.

[00:21:10] Required markers.

[00:21:56] Podcast: Health Outcome-Based Optimal Reference Ranges for Cholesterol, with Tommy Wood, M.D.

[00:22:05] RDW Study: Horne BD, May HT, Muhlestein JB, Ronnow BS, Lappé DL, Renlund DG, et al. Exceptional mortality prediction by risk scores from common laboratory tests. Am J Med. 2009;122: 550–558. Additional references: 1, 2.

[00:22:44] Out of pocket costs.

[00:23:07] The Blood Chemistry Calculator.

[00:23:25] Calculator forecast specifications.

[00:26:48] Binary classification vs logistic regression.

[00:28:44] Clinical decision-making in difficult patients.

[00:30:18] The clinical crystal ball.

[00:30:42] Who's it for?

[00:31:58] Fitness professionals.

[00:32:21] Monthly membership.

[00:35:12] The licensed clinician.

[00:36:34] Quicksilver tri-test.

[00:39:51] 7-minute analysis.

[00:41:10] Evidence-based reference ranges.

[00:41:34] bloodcalculator.com.

[00:42:41] Podcast: National Cyclocross Champion Jeremy Powers on Racing, Training, and the Ketogenic Diet.

[00:43:45] It's a good time to be a software engineer.

[00:44:15] XGBoost Study: Chen, Tianqi, and Carlos Guestrin. “Xgboost: A scalable tree boosting system.” Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. ACM, 2016.

[00:44:39] Fatty Liver Index. Study: Bedogni, Giorgio, et al. "The Fatty Liver Index: a simple and accurate predictor of hepatic steatosis in the general population." BMC gastroenterology 6.1 (2006): 33.

[00:45:23] Atherogenic Index of Plasma (AIP).

[00:45:42] Study: Horne BD, May HT, Muhlestein JB, Ronnow BS, Lappé DL, Renlund DG, et al. Exceptional mortality prediction by risk scores from common laboratory tests. Am J Med. 2009;122: 550–558.

[00:49:30] Sensitivity and specificity.

[00:50:31] Sparse data handling.

[00:52:52] Growth mindset.

[00:55:16] Specializing in Not Specializing TED Talk.

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