We consider the use of intelligent systems to address the long-standing medical problem of diagnostic differentiation between harmful (secondary) and benign (primary) headache conditions. In secondary headaches, the condition is caused by an underlying pathology, in contrast to primary headaches where the production of pain represents the sole constituent of the disorder. Conventional diagnostic paradigms carry an unacceptable risk of misdiagnosis, leaving patients open to potentially catastrophic consequences. Intelligent systems approaches, grounded in artificial intelligence, are adopted in this study as a potential means to unite contributions from multiple settings, including medicine, the life sciences, pervasive computation, sensor technologies, and autonomous intelligent agency, in the fight against headache uncertainty. In this paper, we therefore present the first steps in our research towards a data intensive, unified approach to headache dichotomisation. We begin by presenting a background to headache and its classification, followed by analysis of the space of confounding symptoms, in addition to the problem of primary and secondary condition discrimination. Finally, we proceed to report results of a preliminary case study, in which the epileptic seizure is considered as a manifestation of a headache generating neuropathology. It was found that our classification approach, based on supervised machine learning, represents a promising direction, with a best area under curve test outcome of 0.915. We conclude that intelligent systems, in conjunction with biosignals, could be suitable for classification of a more general set of pathologies, while facilitating the medicalisation of arbitrary settings.
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