An outbreak of Rocky Mountain Spotted Fever associated with a novel tick vector, Rhipicephalus sanguineus, in Arizona, 2004: preliminary report.

This study describes preliminary results of an investigation of RMSF in Arizona associated with the brown dog tick, Rhipicephalus sanguineus. High numbers of dogs and heavy infestations of ticks created a situation leading to human disease.

[Prediction of postoperative nausea and vomiting using an artificial neural network]. / Vorhersage von Ubelkeit und Erbrechen in der postoperativen Phase durch ein künstliches neuronales Netz.

OBJECTIVE: Postoperative nausea and vomiting (PONV) are still frequent side-effects after general anaesthesia. These unpleasant symptoms for the patients can be sufficiently reduced using a multimodal antiemetic approach. However, these efforts should be restricted to risk patients for PONV. Thus, predictive models are required to identify these patients before surgery. So far all risk scores to predict PONV are based on results of logistic regression analysis. Artificial neural networks (ANN) can also be used for prediction since they can take into account complex and non-linear relationships between predictive variables and the dependent item. This study presents the development of an ANN to predict PONV and compares its performance with two established simplified risk scores (Apfel's and Koivuranta's scores). METHODS: The development of the ANN was based on data from 1,764 patients undergoing elective surgical procedures under balanced anaesthesia. The ANN was trained with 1,364 datasets and a further 400 were used for supervising the learning process. One of the 49 ANNs showing the best predictive performance was compared with the established risk scores with respect to practicability, discrimination (by means of the area under a receiver operating characteristics curve) and calibration properties (by means of a weighted linear regression between the predicted and the actual incidences of PONV). RESULTS: The ANN tested showed a statistically significant ( p<0.0001) and clinically relevant higher discriminating power (0.74; 95% confidence interval: 0.70-0.78) than the Apfel score (0.66; 95% CI: 0.61-0.71) or Koivuranta's score (0.69; 95% CI: 0.65-0.74). Furthermore, the agreement between the actual incidences of PONV and those predicted by the ANN was also better and near to an ideal fit, represented by the equation y=1.0x+0. The equations for the calibration curves were: KNN y=1.11x+0, Apfel y=0.71x+1, Koivuranta 0.86x-5. CONCLUSION: The improved predictive accuracy achieved by the ANN is clinically relevant. However, the disadvantages of this system prevail because a computer is required for risk calculation. Thus, we still recommend the use of one of the simplified risk scores for clinical practice.

[Artificial neural networks. Theory and applications in anesthesia, intensive care and emergency medicine]. / Künstliche neuronale Netze. Theorie und Anwendungen in der Anästhesie, Intensiv- und Notfallmedizin.

Artificial neural networks (ANN) are constructed to simulate processes of the central nervous system of higher creatures. An ANN consists of a set of processing units (nodes) which simulate neurons and are interconnected via a set of "weights" (analogous to synaptic connections in the nervous system) in a way which allows signals to travel through the network in parallel. The nodes (neurons) are simple computing elements. They accumulate input from other neurons by means of a weighted sum. If a certain threshold is reached the neuron sends information to all other connected neurons otherwise it remains quiescent. One major difference compared with traditional statistical or rule-based systems is the learning aptitude of an ANN. At the very beginning of a training process an ANN contains no explicit information. Then a large number of cases with a known outcome are presented to the system and the weights of the inter-neuronal connections are changed by a training algorithm designed to minimise the total error of the system. A trained network has extracted rules that are represented by the matrix of the weights between the neurons. This feature is called generalisation and allows the ANN to predict cases that have never been presented to the system before. Artificial neural networks have shown to be useful predicting various events. Especially complex, non-linear, and time depending relationships can be modelled and forecasted. Furthermore an ANN can be used when the influencing variables on a certain event are not exactly known as it is the case in financial or weather forecasts. This article aims to give a short overview on the function of ANN and their previous use and possible future applications in anaesthesia, intensive care, and emergency medicine.