Prediction and Analysis of Weaning Results of Ventilator-Dependent Patients with an Artificial Neuromolecular System

  • Authors:

  • Jong-Chen Chen;Shou-Wei Chien;Jinchyr Hsu

  • Affiliations:

  • Department of Information Management, National Yunlin University of Science and Technology, Touliu, Taiwan;Department of Information Management, National Yunlin University of Science and Technology, Touliu, Taiwan;Department of Internal Medicine, TaiChung Hospital, TaiChung, Taiwan

  • Venue:
  • ICCS '08 Proceedings of the 8th international conference on Computational Science, Part I
  • Year:
  • 2008

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Abstract

We have developed a vertical information processing model, motivated from physiological evidence, which integrates intra- and interneuronal information processing. Information processing at the intraneuronal levels is to create a repertoire of pattern processing neurons. Information processing at the interneuronal levels is to group appropriate pattern processing neurons to constitute an effective pattern processing system. The system was applied to a database of the weaning results of ventilator-dependent patients. Ventilator has been used to support the breathing need of patients, and weaning is the gradual process of removing it from ventilator-dependent patients. Experiments with the model show that the integrated system is able to learn to differentiate data in an autonomous manner, separating those patients who have successful weaning results from those who do not. Our parameter analysis shows that most of the parameters identified as significant by the system are the same as those by physicians, but some are not.