Computational Intelligence Processing in Medical Diagnosis

Computational intelligence techniques are gaining momentum in the medical prognosis and diagnosis. This volume presents advanced applications of machine intelligence in medicine and bio-medical engineering. Applied methods include knowledge bases, expert systems, neural networks, neuro-fuzzy systems...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Schmitt, Manfred (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Teodorescu, Horia-Nicolai (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Jain, Ashlesha (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Jain, Ajita (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt), Jain, Sandhya (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Heidelberg : Physica-Verlag HD : Imprint: Physica, 2002.
Έκδοση:1st ed. 2002.
Σειρά:Studies in Fuzziness and Soft Computing, 96
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • Introduction
  • Computational intelligence techniques in medical decision making: the data mining perspective
  • Internet-based decision support for evidence-based medicine
  • Integrating kernel methods into a knowledge-based approach to evidence-based medicine
  • Case-based reasoning prognosis for temporal courses
  • Pattern recognition in intensive care online monitoring
  • Artificial neural network models for timely assessment of trauma complication risk
  • Artificial neural networks in medical diagnosis
  • The application of neural networks in the classification of the electrocardiogram
  • Neural network predictions of significant coronary artery stenosis in women
  • A modular neural network system for the analysis of nuclei in histopathological sections
  • Septic shock diagnosis by neural networks and rule based systems
  • Monitoring depth of anesthesia
  • Combining evolutionary and fuzzy techniques in medical diagnosis
  • Genetic algorithms for feature selection in computer-aided diagnosis.