Users of tiotropium and single-ingredient LABA had similar risk of total mortality and cardiovascular endpoints. The decreased risk of asthma exacerbations with tiotropium may be due to residual confounding by indication. Confidence limits for most events include reduced risks for tiotropium and also small increases in risk. Nevertheless, the point estimates suggest that tiotropium was associated with a lower risk of each cardiac event except myocardial infarction. However, the small number of cases means that further studies are needed to confirm these results.

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Sequence classification has a wide range of real-world applications in different domains, such as genome classification in health and anomaly detection in business. However, the lack of explicit features in sequence data makes it difficult for machine learning models. While Neural Network (NN) models address this with learning features automatically, they are limited to capturing adjacent structural connections and ignore global, higher-order information between the sequences. To address these challenges in the sequence classification problems, we propose a novel Hypergraph Attention Network model, namely Seq-HyGAN. To capture the complex structural similarity between sequence data, we first create a hypergraph where the sequences are depicted as hyperedges and subsequences extracted from sequences are depicted as nodes. Additionally, we introduce an attention-based Hypergraph Neural Network model that utilizes a two-level attention mechanism. This model generates a sequence representation as a hyperedge while simultaneously learning the crucial subsequences for each sequence. We conduct extensive experiments on four data sets to assess and compare our model with several state-of-the-art methods. Experimental results demonstrate that our proposed Seq-HyGAN model can effectively classify sequence data and significantly outperform the baselines. We also conduct case studies to investigate the contribution of each module in Seq-HyGAN.

Diagnosing Lyme disease has been problematic since its first recognition in 1975. An assortment of problems, including clinical symptoms that mimic several other diseases, and lack of an accurate laboratory test, have hindered diagnosis. Overdiagnosis and misdiagnosis may result. This thesis seeks to improve the accuracy of diagnosing Lyme disease by creating an expert system. The type of expert system developed in this thesis will be a probabilistic Bayesian belief network. The network consists of nodes which represent diagnostic variables and links between nodes which represent the probabilistic influence one node has on another. Much is known about Lyme disease, its transmission, and the diagnostic symptoms that are associated with the disease. This information about variables is incorporated into the network through a literature search. Initial estimates of these variables were determined to initialize the system with a priori values. To test the system, data were collected on a number of patients who presented symptoms consistent with Lyme disease. The system's classification will be compared to the patients classification based on serological results and methods for improving the system' s accuracy are discussed.

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