Manuscript Topics

The delivery of real-time data provides the opportunity to build knowledge, but data accumulates in a speed unmatchable by the human capacity of data processing. Thus, approximation of unknown functions from sampled data is an important activity in modern modeling and systems theory. It is important to develop models from data, which have sufficient generalization power and can describe the underlying process with accuracy, despite the nonlinearity and the complexity of these processes.

Health care is one of the areas where data has been growing exponentially. Improving quality, safety or clinical effectiveness as well as reducing costs are nowadays the main concerns for health care decision-makers. These are challenging problems, and the structure or design of a system may influence the outcome, and is likely to be more significant in high-acuity, complex environments such as intensive care units (ICUs). Patients here are among the sickest patients in the hospital, and decisions that are made can literally mean the difference between life and death. Ultimately, the development of personalized models, that can adapt to the specificities of the individual patient can deliver the necessary quality of care.

In clinical decision support systems, it is crucial to interpret the developed models by determining which attributes are chosen by the artificial intelligence techniques and what is their clinical significance; propose alternative procedures or develop criteria for classifying patients into patient sub-group; and designing a post-implementation assessment of how well the system meets the goals.

Computational intelligence can have an important role in health care as it can provide a transparent description of the system that reflects the nonlinearity of the system. Rule-based models, as e.g. fuzzy models, allow for a linguistic description of the knowledge captured in the model. It can also help the identification of important factors or features that identify specific groups of patients within a specific clinical setting. The design of specific decision models can support clinicians' decisions in terms of identifying the most suitable therapy for a specific patient, in order to achieve more favorable clinical outcomes and preventing poor outcomes due to practice variation.

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