Self-organizing discovery, recognition, and prediction of hemodynamic patterns in the intensive care unit /
(ANNS) is proposed to automate the discovery, recognition,
| Main Author: | |
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| Format: | Thesis eBook |
| Language: | English |
| Published: |
[Place of publication not identified] :
[publisher not identified] ;
1994.
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| Subjects: | |
| Online Access: | Link to OAKTrust copy |
| Summary: | (ANNS) is proposed to automate the discovery, recognition, and prediction of such hemodynarmic patterns in real-time and arrangement of self-organizing artificial neural networks clinician is left to manually sift through an excess of data environments because they are very trainable and have a for determining immediate dangers, it does not provide much hemodynamic patterns. In a typical ICU a variety of In order to properly care for critically ill patients in the in the interest of generating information. In this study, an information about temporal patterns. As a result, the information beyond that provided by instantaneous high/low intensive care unit (ICU), clinicians must be aware of intermittently in an attempt to provide clinicians with the knowledge, thus reducing the need for a priori knowledge in limit checking. While instantaneous limit checking is useful most accurate and precise data needed for recognizing such patterns. However, the data are disjointed, yielding little physiologic measurements are made continuously and presented. suited for pattern recognition and prediction in a data-rich symbolic form. Results from actual clinical data are tendency to discover their own internal representations of ultimately lessen the burden on clinicians. ANNs are well |
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| Item Description: | "Major subject: Bioengineering". Vita. |
| Physical Description: | ix, 77 leaves : illustrations ; 28 cm. Also available online. |
| Bibliography: | Includes bibliographical references. |