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“Using this technique, Baum et al constructed a forest that contained 1,000 decision trees and looked at 84 co-variates that may have been influencing patients' response or lack of response to the intensive lifestyle modifications program. These variables included a family history of diabetes, muscle cramps in legs and feet, a history of emphysema, kidney disease, amputation, dry skin, loud snoring, marital status, social functioning, hemoglobin A1c, self-reported health, and numerous other characteristics that researchers rarely if ever consider when doing a subgroup analysis. The random forest analysis also allowed the investigators to look at how numerous variables *interact* in multiple combinations to impact clinical outcomes. The Look AHEAD subgroup analyses looked at only 3 possible variables and only one at a time. In the final analysis, Baum et al. discovered that intensive lifestyle modification averted cardiovascular events for two subgroups, patients with HbA1c 6.8% or higher (poorly managed diabetes) and patients with well-controlled diabetes (Hba1c < 6.8%) and good self-reported health. That finding applied to 85% of the entire patient population studied. On the other hand, the remaining 15% who had controlled diabetes but poor self-reported general health responded negatively to the lifestyle modification regimen. The negative and positive responders cancelled each other out in the initial statistical analysis, falsely concluding that lifestyle modification was useless. The Baum et al. re-analysis lends further support to the belief that a one-size-fits-all approach to medicine is inadequate to address all the individualistic responses that patients have to treatment.”

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Book:Reinventing Clinical Decision Support: Data Analytics, Artificial Intelligence, and Diagnostic Reasoning
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“The US National Institutes of Health states: "There is a lot of overlap between the terms 'precision medicine' and 'personalized medicine.' According to the National Research Council, 'personalized medicine' is an older term with a meaning similar to 'precision medicine.' However, there was concern that the word 'personalized' could be misinterpreted to imply that treatments and preventions are being developed uniquely for each individual; in precision medicine, the focus is on identifying which approaches will be effective for which patients based on genetic, environmental, and lifestyle factors.”

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Book:Reinventing Clinical Decision Support: Data Analytics, Artificial Intelligence, and Diagnostic Reasoning
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