Medical data is very often badly structured, incomplete and
inconsistent. This limits our ability to generate useful models for
prediction and decision support if we rely purely on machine learning
techniques. That means we need to exploit expert knowledge at various
model development stages. This problem - which is common in many
application domains - is tackled in a paper** published in the latest
issue of
Artificial Intelligence in Medicine.
The
paper describes a rigorous and repeatable method for building
effective Bayesian Network (BN) models from complex data - much of which
comes in unstructured and incomplete responses by patients from
questionnaires and interviews. Such data inevitably contains repetitive,
redundant and contradictory responses; without expert knowledge
learning a BN model from the data alone is especially problematic where
we are interested in simulating causal interventions for risk
management. The novelty of this work is that it provides a rigorous
consolidated and generalised framework that addresses the whole
life-cycle of BN model development. The method is validated using data
from forensic psychiatry. The resulting BN models demonstrate
competitive to superior predictive performance against the data-driven
state-of-the-art models. More importantly, the resulting BN models go
beyond improving predictive accuracy and into usefulness for risk
management through intervention, and enhanced decision support in terms
of answering complex clinical questions that are based on unobserved
evidence.
The method is applicable to any application
domain involving large-scale decision analysis based on such complex and
unstructured information. It challenges decision scientists to reason
about building models based on what information is really required for
inference, rather than based on what data is available. Hence, it forces
decision scientists to use available data in a much smarter way.
**The full reference for the paper is:
Constantinou,
A. C., Fenton, N., Marsh, W., & Radlinski, L. (2016). "From complex
questionnaire and interviewing data to intelligent Bayesian Network
models for medical decision support".Artificial Intelligence in Medicine, Vol 67 pages 75-93. DOI http://dx.doi.org/10.1016/j.artmed.2016.01.002
For those who do not have access to the journal a pre-publication draft can be downloaded:
http://constantinou.info/downloads/papers/complexBN.pdf
