How "Machine Learning" Can Predict Your Blood, Urine, Stool, Saliva & More!
https://bengreenfieldfitness.com/machinelearning
I recently took a test that uses machine learning to predict biochemical test results (like blood, urine and stool) - a test called the "Elite Performance Analysis (EPA) tool". Over the last three years, the folks at Nourish Balance Thrive (Dr. Tommy Wood and Chris Kelly, both former podcast guests) who designed this test have worked with over 1,000 athletes, averaging over 100 biochemical markers collected per athlete, including: -Blood biochemistry -Urine tests (DUTCH and organic acids) -Stool tests (PCR and culture) -Subjective quality of life questions (a Health Assessment Questionnaire, or HAQ), scored on an analog scale (1-5) As well as working to optimize the performance of athletes at every level, another goal of Nourish Balance Thrive is to give more people access to the type of work they do by increasing speed of access and reducing cost. Machine learning provides for a very good way to do this. By training an algorithm based on historical HAQ and biochemical test data, they can predict five common patterns of performance killers that they regularly see in their clients, including: 1. Blood sugar dysregulation (high/low fasting blood sugar and HbA1c, or high fasting insulin) 2. Low sex hormones (testosterone in men and oestrogen in women) 3. Suboptimal hemoglobin (“low oxygen deliverability”
See omnystudio.com/listener for privacy information.
Available Results
Generated results are saved to your library for reuse and search.
Choose Template
Pick the result you want. You can review provider and model before generating.
A concise first-pass summary for understanding the episode quickly.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
A comprehensive extraction focused on health practices, protocols, claims, and safety caveats.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.
Repeatable methods, frameworks, mental models, processes, and systems.
Scientific findings, mechanisms, studies, hypotheses, and the limits of the evidence discussed.
A concise first-pass summary for understanding the episode quickly.
A detailed readable summary organized by chapter or topic.
A navigable map of subjects, topic flow, and suggested chapters.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
Reusable atomic knowledge units extracted from the episode.
A comprehensive extraction focused on health practices, protocols, claims, and safety caveats.
A comprehensive extraction focused on opportunities, strategy, markets, and company building.
Explicit actions, next steps, habits, recommendations, and things to avoid.
A dedicated inventory of concrete resources named in the episode.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.
A concise first-pass summary for understanding the episode quickly.
A detailed readable summary organized by chapter or topic.
A navigable map of subjects, topic flow, and suggested chapters.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
Reusable atomic knowledge units extracted from the episode.
A comprehensive extraction focused on health practices, protocols, claims, and safety caveats.
A comprehensive extraction focused on opportunities, strategy, markets, and company building.
Scientific findings, mechanisms, studies, hypotheses, and the limits of the evidence discussed.
Technologies, AI models, technical methods, capabilities, limitations, and adoption implications.
Investment theses, assets, catalysts, valuation reasoning, time horizons, and risks.
Chronologies, actors, causes, consequences, turning points, and competing historical interpretations.
Policies, proposals, stakeholders, arguments, implementation constraints, and predicted effects.
Career paths, skills, hiring signals, workplace decisions, transitions, and limitations of the advice.
Behavioral mechanisms, biases, motivation, habits, emotions, interventions, and evidence limitations.
Economic mechanisms, incentives, indicators, market structure, forecasts, and uncertainty.
Leadership principles, team systems, organizational design, culture, feedback, and failure modes.
Audience, positioning, messaging, acquisition, retention, experiments, metrics, and failed approaches.
Teaching methods, learning strategies, practice, feedback, assessment, and effectiveness evidence.
Theses, premises, arguments, objections, values, thought experiments, and unresolved questions.
Communication patterns, conflict, boundaries, expectations, repair methods, and contextual limitations.
Books, papers, authors, courses, and other learning resources mentioned in the episode.
Repeatable methods, frameworks, mental models, processes, and systems.
Explicit actions, next steps, habits, recommendations, and things to avoid.
Memorable statements and important claims with attribution and source context.
A dedicated inventory of concrete resources named in the episode.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.