A Neural Network Approach to the Prediction and Confidence Assignation of Nonlinear Time Series Classifications

This thesis uses multiple layer perceptrons (MLP) neural networks and Kohonen clustering networks to predict and assign confidence to nonlinear time series classifications. The nonlinear time series used for analysis is the Standard and Poor\u27s 100 (S&P 100) index. The target prediction is classification of the daily index change. Financial indicators were evaluated to determine the most useful combination of features for input into the networks. After evaluation it was determined that net changes in the index over time and three short-term indicators result in better accuracy. A back-propagation trained MLP neural network was then trained with these features to get a daily classification prediction of up or down. Next, a Kohonen clustering network was trained to develop 30 different clusters. The predictions from the MLP network were labeled as correct or incorrect within each classification and counted in each category to determine a confidence for a given cluster. Test data was then run through both networks and predictions were assigned a confidence based on which cluster they belonged to. The results of these tests show that this method can improve the accuracy of predictions from 51% to 73%. Within a cluster accuracy is near 100% for some classifications

Outbreak of Pneumonia in the Setting of Fatal Pneumococcal Meningitis among US Army Trainees: Potential Role of Chlamydia pneumoniae Infection

<p>Abstract</p> <p>Background</p> <p>Compared to the civilian population, military trainees are often at increased risk for respiratory infections. We investigated an outbreak of radiologically-confirmed pneumonia that was recognized after 2 fatal cases of serotype 7F pneumococcal meningitis were reported in a 303-person military trainee company (Alpha Company).</p> <p>Methods</p> <p>We reviewed surveillance data on pneumonia and febrile respiratory illness at the training facility; conducted chart reviews for cases of radiologically-confirmed pneumonia; and administered surveys and collected nasopharyngeal swabs from trainees in the outbreak battalion (Alpha and Hotel Companies), associated training staff, and trainees newly joining the battalion.</p> <p>Results</p> <p>Among Alpha and Hotel Company trainees, the average weekly attack rates of radiologically-confirmed pneumonia were 1.4% and 1.2% (most other companies at FLW: 0-0.4%). The pneumococcal carriage rate among all Alpha Company trainees was 15% with a predominance of serotypes 7F and 3. <it>Chlamydia pneumoniae </it>was identified from 31% of specimens collected from Alpha Company trainees with respiratory symptoms.</p> <p>Conclusion</p> <p>Although the etiology of the outbreak remains unclear, the identification of both <it>S. pneumoniae </it>and <it>C. pneumoniae </it>among trainees suggests that both pathogens may have contributed either independently or as cofactors to the observed increased incidence of pneumonia in the outbreak battalion and should be considered as possible etiologies in outbreaks of pneumonia in the military population.</p