Saturday, 19 March 2016

Turning poorly structured data into intelligent Bayesian Network models for medical decision support



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 

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