[show abstract][hide abstract] ABSTRACT: Discrete Conditional Phase-type (DC-Ph) models consist of a process component (survival distribution) preceded by a set of related conditional discrete variables. This paper introduces a DC-Ph model where the conditional component is a classification tree. The approach is utilised for modelling health service capacities by better predicting service times, as captured by Coxian Phase-type distributions, interfaced with results from a classification tree algorithm. To illustrate the approach, a case-study within the healthcare delivery domain is given, namely that of maternity services. The classification analysis is shown to give good predictors for complications during childbirth. Based on the classification tree predictions, the duration of childbirth on the labour ward is then modelled as either a two or three-phase Coxian distribution. The resulting DC-Ph model is used to calculate the number of patients and associated bed occupancies, patient turnover, and to model the consequences of changes to risk status.
[show abstract][hide abstract] ABSTRACT: The ability to model and predict the progression of disease in a patient can have wide ranging benefits, including the ability to successfully manage bed allocation in hospitals or the increase understanding of the evolution of the disease. This paper describes a new method of modelling the progression of a disease through different stages called a Coxian hidden Markov model. This model can be used to increase understanding of the characteristics of the different stages of the disease and to predict patient survival time given repeated measurements of dynamically changing clinical variables. This knowledge could then be used to provide better patient management
Computer-Based Medical Systems, 2006. CBMS 2006. 19th IEEE International Symposium on; 02/2006
Adele Marshall added a full-text to this conference proceeding and 1 other.