Information Technology in Brain Intensive Therapy

In order to control a process, especially in a computer and instrumented assisted way, as in Brain Intensive Therapy (IT), a model of its behavior is needed. In order to do that, one must first be able to select the best set of a few (thus understandable and manageable) truly relevant variables. In manufactured systems, also used in Brain IT, physics is in fact often quite known, with a manageable number of degrees of freedom; for instance, in robot kinematics, natural state variables may be (angular) positions and velocities. Some natural systems are also easily characterized by state variables with physical meaning: for instance reservoirs (e.g., lakes), but also body organs with respect to soluble substances may be characterized by volumes, concentrations, fluxes, and gradients among compartments (Liberati & Turkheimer, 1999). In other cases, the “natural” variables of the systems are not the best ones for identifying and control a process. A transformation of some of them may be needed; for instance, in the autonomous nervous control of the hearth system, the differential interbeat measure is the one that almost linearly interacts with baroreflex in the feedback control loop (Baselli et al., 1986). In many cases, mainly for natural, especially neural processes, it is not easy to formulate a model based on variables whose physical meaning is a priori known; for instance, the electroencephalogram (EEG) is a very far field shielded measure of the interacting activity of billions of neurons. Many actors are thus playing, each one with many meaningful state variables, also depending on the investigated level, but with high reciprocal correlation. It would not be efficient to model every single variable for global monitoring such that it is not useful to take into account the kinetics of every single molecule of a gas when only global effects of pressure are of interest. A quest of some higher-level variables, even without a direct physical meaning, is thus natural in order to easily manage the complexity of the problem. Once such salient variables are found, the problem often arises to correlate in logical and/or mathematical sense their dynamics in order to properly model, forecast, and control the patient features. Such interrelated problems will be addressed briefly in the present contribution, where Intensive Therapy is chosen as a paradigmatic application because of its critical conditions, while the approaches described whose rationale is better analyzed in the referred bibliography are of a quite general use in health information systems.