a new approach to Healthcare is now possible due the availability of:
- more Data (Big Data, accessibility, sharing, privacy???)
- Data Burden: how extract the relevant data? (causal models can supply hints?)
- more sound Models
- A top down model: from the disease to the optimal care
- A bottom up model: from biochemical pathways to symptoms and symptoms collections (diseases)
Modelling Metabolism to improve Health Management
GP Publications
Definitions
- Health (ability to cope with everyday environmental stress)
- energy production (whole body)
- Local conditions affecting energy production (organ specific)
Modelling by Agents
Structural Causal Models - SCM
Vensim Metabolic Pathways
Tutorial on How To Develop Stock-and-Flow Diagrams Using Vensim - System Dynamics Simulation Using Stock-and-Flow Diagrams
Vensim PLE Tutorial
Vensim Help
Generalized Structural Causal Models - GSCM
Generalized Structural Causal Models, 2018
- In an SCM, each endogenous variable is associated with
a structural equation that describes its causal dependence
on other variables in the system, which induces a set of
probability distributions over the space of endogenous
variables. We generalize the notion of a structural equation
to the concept of a causal constraint, which is a
functional relation between variables that is invariant under
a specified set of interventions. A generalized structural
causal model is then a set of causal constraints in
combination with a probability distribution on the exogenous
variables.
What is new in causal inference - Judea Pearl
The seven tools of causal inference with reflections on machine learning, 2018
- In this technical report Judea Pearl reflects on some of the limitations of machine learning systems that are based solely on statistical interpretation of data. To understand why? and to answer what if? questions, we need some kind of a causal model.
Examples
Metabolism_of_stromal_and_immune_cells_in_health_and_disease_2014
David Hammerstain
Google: Charlotte Kellogg Public Health
Ron S. Kenett, Israel : Applications of Bayesian Networks to Operational Risks, Healthcare, Biotechnology and Customer Surveys
