Rx

Our WebApp offers six improvements on what is available in the literature and practice: sampling distribution, absolute risks, distinct hazards by phenotype, confidence intervals for individualized risk, explicitly reveals the footprint of missingness by parameter, and personalizes risk on a kaplan-meier curve for the user

                1. f(t)
                      \
           2. S(t) -> 4. y:h'(f)=0;t(X'X).X'Y -> 5. b -> 6. SV'
                      /
                      3. h(t) 

• f(t) Mozart

• S(t) Bach

• h(t) Handel

• t(X’X).X’Y Ludwig

• b Genres

• SV’ Chopin

Specialist

• With any individualized prediction in science, a patient can’t say “that resonates” with me. The numbers are abstract and they’ve never experienced the outcome

• But with art or tragedy the eternal recurrence of the same is encoded in the latent-space or collective unconscious and so it can resonate

• Our way around this is by handing the end-user an app in which they might update their risk-profile and see if it changes the risk prediction

• If there’s no change, then we could say the app is “mirroring” humanity so abominably; the backend model has no beta-coefficients for what has been tested

• The structure of this discourse is : \(\mu\): base-case, \(\sigma\) varcov-matrix, \(\%\) predictive-accuracy

\(\mu\) Base-case

• \(f(t)\) l’homme moyen. But with five pdf’s, reflecting modalities

  • Vision

    • Static Images

    • Videos (with audio)

    • GIFs

  • Auditory

    • Unimodal

    • Bimodal e.g. audiovideo

  • Chemistry

    • Smell (e.g. volatile substances)

    • Blood, serum, urine, etc

  • Language

    • Text

    • Audio

    • Multilingual

  • Tactile

    • Palpation or echo

    • Etc

• \(S(t)\) somewhere ages and ages hence

  • Cumulative datasets on which all the pdfs are trained

• \(h(t)\) two roads diverged in a wood, and i—

  • Instantaneous prediction utilizing speed & a lot of data on the fly

\(\sigma\) Varcov-matrix

• var[\((X'X)^T \cdot X'Y\)] think: the variability of courses & items in italian cuisine & modes-qualities-relatives in chopin

  • ChatGPT Solving Complex Kidney Transplant Cases: A Comparative Study with Human Respondents

  • This paper only offers 6 clinical scenarios

  • GPT models have a real edge in being “large” and having been trained on the entire pdf including extreme tails

  • No clinician has the “experience” & ability to pay “attention” even to traits associated with tails & produce stable & accurate predictions

\(\%\) Predictive-accuracy

• \(\beta\) my palate craves very diverse things over time & i can only tell what i want on a given day

• \(SV'\) \(\ge 85\) only a very rich culture can throw me a bone to chew on that will keep me engaged

  • Clinicians can’t be interrogated in frequent iterations without accruing a bill or their irate responses

Generalist

Arte (Imitation) & Scienza (Prediction) = Tragedy (Representation). When the products of art, science, or tragedy resonate with us, they have been accurately decoded with all the parameter coefficients similar to our own

                     1. Observing
                                \
           2. Time = Compute -> 4. Collective Unconscious -> 5. Decoding -> 6. Imitation-Prediction-Representation
                                /
                                3. Encoding