Name
Abstract | ||
|---|---|---|
Much knowledge of human physiology is formalised as systems of differential equations. For example, standard models of pharma-cokinetics and pharmacodynamics use systems of differential equations to describe a drug's movement through the body and its effects. Here, we propose a method for automatically incorporating this existing knowledge into a Dynamic Bayesian Network (DBN) framework. A benefit of recasting a differential equation model as a DBN is that the DBN can be used to individualise the model parameters dynamically, based on real-time evidence. Our approach provides principled handling of data and model uncertainty, and facilitates integration of multiple strands of temporal evidence. We demonstrate our approach with an abstract example and evaluate it in a real-world medical problem, tracking the interaction of insulin and glucose in critically ill patients. We show that it is better able to reason with the data, which is sporadic and has measurement uncertainties. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-22218-4_20 | AIME '87 |
Keywords | Field | DocType |
existing knowledge,dynamic bayesian network,differential equation,clinical time series data,model parameters dynamically,differential equation model,mathematical model,real-time evidence,abstract example,temporal evidence,standard model,model uncertainty,dynamic bayesian networks | Differential equation,Time series data analysis,Data mining,Computer science,Human physiology,Artificial intelligence,Mathematical model,Machine learning,Dynamic Bayesian network | Conference |
Citations | PageRank | References |
5 | 0.51 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Catherine G. Enright | 1 | 11 | 2.01 |
| Michael G. Madden | 2 | 394 | 31.28 |
| Niall Madden | 3 | 29 | 7.41 |
| John G. Laffey | 4 | 6 | 0.87 |