UNSTRUCTURED
Pain is a subjective phenomenon caused/perceived centrally and modified by physical, physiological, or social influences. Currently, the most commonly used approaches for pain measurement rely on self-reporting of pain level on a discrete rating scale. This provides a subjective and only semi-quantitative indicator of pain.
This paper presents an approach that combines self-reported pain with pain-related biomarkers to be obtained from biosensors (in development) and possibly other sources of evidence to provide more dependable estimates of experienced pain, a clinical decision support system. We illustrate the approach using a Bayes network, but also describe other artificial intelligence (AI) methods that provide other ways to combine evidence. We also propose an optimization approach for tuning the AI method parameters (opaque to clinicians) so as to best approximate the kinds of outputs most useful to medical practitioners.
We present some data from a sample of 379 patients that illustrate several evidence patterns we may expect in real healthcare situations. The majority (79.7%) of our patients show consistent evidence suggesting this biomarker approach may be reasonable. We also found five patterns of inconsistent evidence. These suggest a direction for further exploration. Finally, we sketch out an approach for collecting medical experts’ guidance as to the way the combined evidence might be presented so as to provide the most useful guidance (also needed for any optimization approach). We recognize that one possible outcome may be that all this approach may be able to provide is a quantified measure of the extent to which the evidence is consistent or not, leaving the final decision to the clinicians (where it must reside). Pointers to additional sources of evidence might also be possible in some situations.