Using stratified medicine to understand, diagnose, and treat neuropathic pain

Neuropathic pain (NeuP) is defined as pain arising from a lesion or disease of the somatosensory nervous system. NeuP is common, affecting approximately 6-8% of the general population and currently treatment is inadequate due to both poor drug efficacy and tolerability. Many different types of injury can cause neuropathic pain including genetic (e.g. SCN9A gain of function variants), metabolic (e.g. diabetic polyneuropathy), infective (e.g. HIV associated neuropathy, hepatitis), traumatic and toxic (e.g. chemotherapy induced neuropathy) causes. Such injurious events can impact on anatomically distinct regions of the somatosensory nervous system ranging from the terminals of nociceptive afferents (in small fiber neuropathy) to the thalamus (in post-stroke pain). Classification of neuropathic pain using etiology and location remains an important aspect of routine clinical practice; however, pain medicine is coming to the realization that we need more precision in this classification. The hope is that improved classification will lead to better understanding of risk, prognosis and optimal treatment of NeuP

Architecture for Adaptive Intelligent Systems

We identify a class of niches to be occupied by 'adaptive intelligent systems (AISs)'. In contrast with niches occupied by typical AI agents, AIS niches present situations that vary dynamically along several key dimensions: different combinations of required tasks, different configurations of available resources, contextual conditions ranging from benign to stressful, and different performance criteria. We present a small class hierarchy of AIS niches that exhibit these dimensions of variability and describe a particular AIS niche, ICU (intensive care unit) patient monitoring, which we use for illustration throughout the paper. We have designed and implemented an agent architecture that supports all of different kinds of adaptation by exploiting a single underlying theoretical concept: An agent dynamically constructs explicit control plans to guide its choices among situation-triggered behaviors. We illustrate the architecture and its support for adaptation with examples from Guardian, an experimental agent for ICU monitoring