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Notes on Heartbreak

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Annie Lord

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“To understand what effects roadblocks to communication have on children or what they do to the parent-child relationship, parents must first be shown that their verbal responses usually carry more than one meaning or one message. For example, to say to a child who has just complained that her friend doesn’t like her or doesn’t play with her anymore, “I would suggest you try to treat Emma better and then maybe she will want to play with you” conveys much more to a child than simply the “content” of your suggestion. The child may “hear” any or all of these hidden messages: -“You don’t accept my feeling the way I do, so you want me to change.” -“You don’t trust me to work out this problem myself.” -“You think it’s my fault, then.” -“You think I’m not as smart as you.” -“You think I’m doing something bad or wrong.”

ParentingCommunication SkillsParenting Tips
Author:Dr. Thomas Gordon
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“It’s true that AI can mimic the human brain, but it can also outperform us mere humans by discovering complex patterns that no human being could ever process and identify.”

DoctorsMedicineDataTherapyAiHealthcarePhysiciansDiagnosisClinicPatient Care
Book:AI Doctor: The Rise of Artificial Intelligence in Healthcare - A Guide for Users, Buyers, Builders, and Investors
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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.”

AnalysisStatisticsMedical CarePatient CareData AnalyticsEhrClinical Decision SupportDiagnostic Reasoning
Book:Reinventing Clinical Decision Support: Data Analytics, Artificial Intelligence, and Diagnostic Reasoning
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