Abstract
In order to properly care for critically ill patients in the intensive care unit (ICU), clinicians must be aware of hemodynamic patterns. In a typical ICU a variety of physiologic measurements are made continuously and intermittently in an attempt to provide clinicians with the most accurate and precise data needed for recognizing such patterns. However, the data are disjointed, yielding little information beyond that provided by instantaneous high/low limit checking. While instantaneous limit checking is useful for determining immediate dangers, it does not provide much information about temporal patterns. As a result, the clinician is left to manually sift through an excess of data in the interest of generating information. In this study, an arrangement of self-organizing artificial neural networks (ANNS) is proposed to automate the discovery, recognition, and prediction of such hemodynarmic patterns in real-time and ultimately lessen the burden on clinicians. ANNs are well suited for pattern recognition and prediction in a data-rich environments because they are very trainable and have a tendency to discover their own internal representations of knowledge, thus reducing the need for a priori knowledge in symbolic form. Results from actual clinical data are presented.
Spencer, Ronald Glen (1994). Self-organizing discovery, recognition, and prediction of hemodynamic patterns in the intensive care unit. Master's thesis, Texas A&M University. Available electronically from
https : / /hdl .handle .net /1969 .1 /ETD -TAMU -1994 -THESIS -S745.