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1.
J Med Syst ; 25(4): 269-76, 2001 Aug.
Article in English | MEDLINE | ID: mdl-11463203

ABSTRACT

Digitized data from CTG (cardiotocography) measurements (fetal heart rate and uterine contractions) have been used for categorization of typical heart rate patterns before and during delivery. Short time series of CTG data, about 7 min duration, have been used in the categorization process. In the first part of the study, selected CTG data corresponding to 10 typical cases were used for purely auto associative unsupervised training of a Self-Organizing Map Neural Network (SOM). The network may then be used for objective categorization of CTG patterns through the map coordinates produced by the network. The SOM coordinates were then compared. In the second part of the study, a hybrid neural network consisting of a SOM network and a Back-Propagation network (BP) was trained with data corresponding to a number of basic heart rate patterns as described by eight manually selected indices. Test data (different than the training data) were then used to check the performance of the network. The present study shows that the categorization process, in which neural networks were used, can be reliable and agree well with the manual categorization. Since the categorization by neural networks is very fast and does not involve human efforts, it may be useful in patient monitoring.


Subject(s)
Cardiotocography/methods , Heart Rate, Fetal/physiology , Neural Networks, Computer , Data Display , Female , Humans , Image Interpretation, Computer-Assisted , Labor, Obstetric/physiology , Pregnancy , Uterine Contraction/physiology
2.
Comput Biomed Res ; 32(2): 132-44, 1999 Apr.
Article in English | MEDLINE | ID: mdl-10337495

ABSTRACT

One of the problems in the management of the diabetic patient is to balance the dose of insulin without exactly knowing how the patient's blood glucose concentration will respond. Being able to predict the blood glucose level would simplify the management. This paper describes an attempt to predict blood glucose levels using a hybrid AI technique combining the principal component method and neural networks. With this approach, no complicated models or algorithms need be considered. The results obtained from this fairly simple model show a correlation coefficient of 0.76 between the observed and the predicted values during the first 15 days of prediction. By using this technique, all the factors affecting this patient's blood glucose level are considered, since they are integrated in the data collected during this time period. It must be emphasized that the present method results in an individual model, valid for that particular patient under a limited period of time. However, the method itself has general validity, since the blood glucose variations over time have similar properties in any diabetic patient.


Subject(s)
Artificial Intelligence , Blood Glucose/analysis , Diabetes Mellitus/blood , Algorithms , Blood Glucose/metabolism , Computer Simulation , Diabetes Mellitus/drug therapy , Forecasting , Humans , Insulin/administration & dosage , Insulin/therapeutic use , Neural Networks, Computer , Nonlinear Dynamics , Reproducibility of Results
3.
Reg Anesth Pain Med ; 24(3): 225-30, 1999.
Article in English | MEDLINE | ID: mdl-10338172

ABSTRACT

BACKGROUND AND OBJECTIVES: Morphine and nonsteroidal antiinflammatory drugs (NSAID) have been found to be effective in relieving postoperative pain. The goal of this study was to determine whether ketorolac alone or in combination with morphine provides superior pain relief following arthroscopy performed with local anesthesia (LA). METHODS: This was a randomized, double-blind, prospective, study in 100 healthy patients from 15 to 60 years of age. Knee arthroscopy was performed with LA using 40 mL prilocaine (5 mg/mL) with adrenaline (4 microg/mL). At the end of the operation, a catheter was inserted intra-articularly, and one of the following solutions diluted to a total volume of 40 mL was injected: group P (40 mL normal saline), group M (3 mg morphine), group K30 (30 mg ketorolac), group K60 (60 mg ketorolac), and group KM (3 mg morphine + 30 mg ketorolac). Visual analog scale (VAS) pain scores (0-100 mm) were measured preoperative and at 30, 60, 90, 120 minutes postoperative and thereafter 4, 8, 24, and 48 hours at rest and on movement of the knee. The total number of distalgesic tablets (325 mg paracetamol + 32.5 mg dextropropoxyphene) consumed during the 48 hours postoperative was recorded. RESULTS: Significant differences in VAS pain scores were seen between group P and group KM at 4, 8, and 24 hours (P < .05) and between group M and group KM at 4, 8, 24, and 48 hours (P < .01) after the operation at rest. During mobilization of the knee, a significant difference in VAS pain score was found between group P and group KM at 8, 24, and 48 hours (P < .05) and between group P and group K60 at 24 and 48 hours (P < .05). The total consumption of distalgesic tablets did not differ among the groups. CONCLUSIONS: The combination of 3 mg morphine plus 30 mg ketorolac provided significantly better analgesia than either placebo alone or morphine alone. This result could be a synergistic effect.


Subject(s)
Analgesics, Non-Narcotic/administration & dosage , Analgesics, Opioid/administration & dosage , Anesthesia, Local/methods , Knee Joint/surgery , Morphine/administration & dosage , Pain, Postoperative/drug therapy , Tolmetin/analogs & derivatives , Adolescent , Adult , Arthroscopy/adverse effects , Double-Blind Method , Drug Therapy, Combination , Female , Humans , Injections, Intra-Articular , Ketorolac , Male , Middle Aged , Pain Measurement/drug effects , Prospective Studies , Tolmetin/administration & dosage
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