Personalized Medicine: Ezekiel

Description

This lecture introduces the concept of personalized medicine, emphasizing the need for tailored treatments based on individual patient characteristics. The instructor presents Ezekiel, an automated software designed to assist doctors in finding personalized dosage adjustments using Bayesian mathematical models. The software aims to improve treatment efficiency and is interdisciplinary, involving collaboration with clinical pharmacologists. The lecture covers the development process, testing phases, and future plans for Ezekiel, including attending relevant events and refining algorithms for data aggregation. Additionally, the Q&A session addresses funding strategies, the use of Bayesian approach, and the software's branding. The presentation concludes with a focus on the importance of patient-specific care and the potential impact of Ezekiel on healthcare.

Official source

https://mediaspace.epfl.ch/media/0_906xzgu3

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Related concepts (4)

Bayesian statistics

Bayesian statistics (ˈbeɪziən or ˈbeɪʒən ) is a theory in the field of statistics based on the Bayesian interpretation of probability where probability expresses a degree of belief in an event. The degree of belief may be based on prior knowledge about the event, such as the results of previous experiments, or on personal beliefs about the event. This differs from a number of other interpretations of probability, such as the frequentist interpretation that views probability as the limit of the relative frequency of an event after many trials.

Bayesian probability

Bayesian probability (ˈbeɪziən or ˈbeɪʒən ) is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation representing a state of knowledge or as quantification of a personal belief. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses; that is, with propositions whose truth or falsity is unknown.

Bayesian network

A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). It is one of several forms of causal notation. Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms.

Bayesian epistemology

Bayesian epistemology is a formal approach to various topics in epistemology that has its roots in Thomas Bayes' work in the field of probability theory. One advantage of its formal method in contrast to traditional epistemology is that its concepts and theorems can be defined with a high degree of precision. It is based on the idea that beliefs can be interpreted as subjective probabilities. As such, they are subject to the laws of probability theory, which act as the norms of rationality.