Source: Masters Abstracts International, Volume: 47-03, page: 1686. Advisers: Jack J. Lee; Yunxin Fu. Includes bibliographical references.

Yes / Many studies have examined the extent to which individuals’ probability judgments depart from Bayes’ theorem when revising probability estimates in the light of new information. Generally, these studies have not considered the implications of such departures for decisions involving risk. We identify when such departures will occur in two common types of decisions. We then report on two experiments where people were asked to revise their own prior probabilities of a forthcoming economic recession in the light of new information. When the reliability of the new information was independent of the state of nature, people tended to overreact to it if their prior probability was low and underreact if it was high. When it was not independent, they tended to display conservatism. We identify the circumstances where discrepancies in decisions arising from a failure to use Bayes’ theorem were most likely to occur in the decision context we examined. We found that these discrepancies were relatively rare and, typically, were not serious.

Thesis (Ph. D.)--University of Washington, 2002. / Vita. Includes bibliographical references (leaves 102-108).

Disease maps are geographical maps that display local estimates of disease risk. When the disease is rare, crude risk estimates can be highly variable, leading to extreme estimates in areas with low population density. Bayesian hierarchical models are commonly used to stabilize the disease map, making them more easily interpretable. By exploiting assumptions about the correlation structure in space and time, the statistical model stabilizes the map by shrinking unstable, extreme risk estimates to the risks in surrounding areas (local spatial smoothing) or to the risks at contiguous time points (temporal smoothing). Extreme estimates that are based on smaller populations are subject to a greater degree of shrinkage, particularly when the risks in adjacent areas or at contiguous time points do not support the extreme value and are more stable themselves. / A common goal in disease mapping studies is to identify areas of elevated risk. The objective of this thesis is to compare the accuracy of several fully Bayesian hierarchical models in discriminating between high-risk and background-risk areas. These models differ according to the various spatial, temporal and space-time interaction terms that are included in the model, which can greatly affect the smoothing of the risk estimates. This was accomplished with simulations based on the cervical cancer rate of Kentucky and at-risk person-years of the state of Kentucky's 120 counties from 1995 to 2002. High-risk areas were 'planted' in the generated maps that otherwise had background relative risks of one. The various disease mapping models were applied and their accuracy in correctly identifying high- and background-risk areas was compared by means of Receiver Operating Characteristic curve methodology. Using data on Multiple Sclerosis (MS) on the island of Sardinia, Italy we apply the more successful models to identify areas of elevated MS risk.

Thesis (Ph.D.)--University of Minnesota, 1999. / Includes bibliographical references (leaves 179-187). Also available on the World Wide Web as a PDF file.

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Immunhistochemische Zusatzuntersuchungen an Karzinommetastasen mit unbekanntem Primärtumor sind kostengünstig und erlauben insbesondere bei Adenokarzinomen oft eine spezifische Identifizierung des primären Tumorsitzes. Die Auswahl an kommerziell verfügbaren Antikörpern gegen Markerproteine mit gut dokumentierter hoher bis sehr hoher Spezifität für bestimmte Primärtumoren ist jedoch begrenzt. Dazu gehören der Thyreoidale Transkriptionsfaktor-1, Uroplakin III, GCDFP-15, Östrogen- und Progesteronrezeptoren, (-Fetoprotein, der A103-Antikörper gegen MART-1, die Cytokeratine 7 und 20, Basalzell-Cytokeratine, das carcinoembryonale Antigen, CA-125, EMA, Vimentin, HepPar-1, PSA, Thyreoglobulin und das S100-Protein. Die meisten dieser Marker sind jedoch nicht absolut spezifisch, die mit ihnen erzielten Färbeergebnisse müssen daher im Kontext des klinischen und konventionell-histomorphologischen Gesamtbefundes bewertet werden. Je genauer im Rahmen dieses Gesamtbefundes das Spektrum der infrage kommenden Karzinome und ihre relativen a priori Wahrscheinlichkeiten abgeschätzt werden, um so genauer lassen sich auch auf der Grundlage des Bayes-Theorems aus den Färbeergebnisse der Marker diagnostisch relevante Aussagen (prädiktive Werte) gewinnen. / Immunohistochemical studies on metastatic carcinomas of unknown primary site are cost-effective and often allow a specific identification of the tumor origin, especially if the metastases are adenocarcinomas by light microscopy. Commercially available site-specific markers include prostate-specific antigen, thyreoglobulin, thyreoid transcription factor-1, uroplakin III, GCDFP-15, estrogen- and progesterone rezeptors, (-Fetoprotein, the A103 monoclonal antibody against MART-1, cytokeratins 7 and 20, cytokeratins of basal cell type, p63, carcinoembryonic antigen, CA-125, EMA, vimentin, HepPar-1, and S100 protein. However, immunostainings with most of these markers do not show an absolute specificity for a certain primary site. For this reason, histopathologists interpretating staining results with these markers should take into consideration the available clinical data and the histological features of the metastatic carcinoma. These data are necessary to estimate the relative a priori probabilities of possible carcinomas. Based on Bayes` theorem, the a priori probabilities can then be used to calculate the diagnostically relevant predictive values for immunostaining results with the chosen markers.

This dissertation demonstrates, in a non-semantic information-theoretic framework, how the principles of 'maximisation of relevant information' and 'information parsimony' can guide the adaptation of an agent towards agent-agent interaction. Central to this thesis is the concept of digested information; I argue that an agent is intrinsically motivated to a.) process the relevant information in its environment and b.) display this information in its own actions. From the perspective of similar agents, who require similar information, this differentiates other agents from the rest of the environment, by virtue of the information they provide. This provides an informational incentive to observe other agents and integrate their information into one's own decision making process. This process is formalized in the framework of information theory, which allows for a quantitative treatment of the resulting effects, specifically how the digested information of an agent is influenced by several factors, such as the agent's performance and the integrated information of other agents. Two specific phenomena based on information maximisation arise in this thesis. One is flocking behaviour similar to boids that results when agents are searching for a location in a girdworld and integrated the information in other agent's actions via Bayes' Theorem. The other is an effect where integrating information from too many agents becomes detrimental to an agent's performance, for which several explanations are provided.

OBJETIVO: Comparar a eficácia e a tolerabilidade dos fármacos antiepiléticos (FAE) no tratamento em monoterapia de pacientes com epilepsia focal ou generalizada. MÉTODOS: Uma revisão sistemática foi realizada por meio da busca nas bases de dados eletrônicas Pubmed, Scopus, Web of Science e Cochrane Register of Controlled Trials. Foram incluídos os ensaios clínicos controlados com pacientes com epilepsia, em tratamento com FAE, via oral, em monoterapia, e que avaliaram o número de pacientes que atingiram a remissão das crises epilépticas, que interromperam o tratamento devido à ineficácia terapêutica ou à ocorrência de reações adversas (RAM) intoleráveis. Meta-análises de comparação de múltiplos tratamentos foram realizadas por meio do modelo bayesiano de efeitos randômicos que permitiu o cálculo do Odds Ratio meta-analítico para os FAE estudados. Também foi realizado um ranqueamento da probabilidade de cada FAE ser a melhor opção em eficácia e tolerabilidade. RESULTADOS E CONCLUSÕES: A busca identificou 18874 publicações, no entanto apenas 71 estudos foram selecionados, compreendendo 17555 pacientes com epilepsia. Vinte e nove estudos apresentaram os desfechos de eficácia no tratamento de crises focais, 19 em crises generalizadas e 58 apresentaram dados de tolerabilidade. Nesses estudos, 15 FAE foram avaliados. No tratamento das crises focais, os FAE de nova geração levetiracetam (LEV), lamotrigina (LTG), oxcarbazepina, sultiame e topiramato (TPM) demonstraram possuir eficácia equivalente à carbamazepina (CBZ), clobazam e valproato (VPA). No entanto, a CBZ apresentou o pior perfil de tolerabilidade devido à grande probabilidade do paciente abandonar o tratamento devido à RAM intoleráveis. Quanto ao tratamento das crises generalizadas, a LTG, LEV e TPM são tão eficazes quanto o VPA para o tratamento de crises generalizadas tônico-clônicas, tônicas e clônicas. O VPA e a etosuximida constituem as melhores opções para o tratamento de crises de ausências, enquanto que a LTG mostrou-se menos eficaz. Para o tratamento de crises mioclônicas e espasmos infantis mais ensaios clínicos randomizados são necessários para fornecer boas evidências que possam guiar a decisão clínica dos profissionais de saúde. Dentre os FAE com perfil de eficácia adequado, a LTG destacou-se pela menor probabilidade de manifestar RAM intoleráveis. / OBJECTIVE: To compare the efficacy and tolerability of the antiepileptic drugs (AED) in monotherapy of patients with focal or generalized epilepsy. METHODS: A systematic review was in the Medline/Pubmed, Scopus, Web of Science and Cochrane Register of Controlled Trials databases. We included randomized clinical trials of patients with epilepsy treated with oral monotherapy AED, which evaluated number of patients becoming seizure free at the maintenance treatment period; number of patients which withdrawals from the study because of therapeutic inefficacy and number of patients which withdrawals from the study because of intolerable adverse reaction. Network meta-analyses were performed using Bayesian random effects model. We also carried out a ranking of the probability of each AED be the best option in the efficacy and tolerability outcomes. Sensitivity analyses were conducted in order to check the robustness of the results. RESULTS AND CONCLUSIONS: The research identified 18,874 publications, but only 71 studies were selected, comprising 17555 patients with epilepsy.Twenty-nine trials showed the efficacy outcomes in the treatment of focal seizures, 19 in generalized seizures and 58 showed tolerability data. In the treatment of focal seizures, levetiracetam (LEV), lamotrigine (LTG), oxcarbazepine, sultiame and topiramate (TPM) have demonstrated equivalent efficacy to carbamazepine (CBZ), clobazam and valproate (VPA). LTG, LEV and TPM are as effective as the VPA for the treatment of generalized tonic-clonic, tonic and clonic seizures. VPA and ethosuximide are the best options for the treatment of absence seizures, whereas LTG was less effective. For the treatment of myoclonic seizures and infantile spasms, more randomized clinical trials are needed to provide good evidence to guide the clinical decision of health professionals. Among the AED with adequate efficacy profile, LTG stands out as the AED with the best tolerability profile, suggesting it may be the best option for the treatment of patients with epilepsy.

Lee Kit Ying. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 115-117). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Project Background --- p.1 / Chapter 1.2 --- Problem Specifications --- p.3 / Chapter 1.3 --- Contributions --- p.5 / Chapter 1.4 --- Thesis Organization --- p.6 / Chapter 2 --- Background --- p.8 / Chapter 2.1 --- Medical Data Mining --- p.8 / Chapter 2.1.1 --- General Information --- p.9 / Chapter 2.1.2 --- Related Research --- p.10 / Chapter 2.1.3 --- Characteristics and Difficulties Encountered --- p.11 / Chapter 2.2 --- DNA Sequence Analysis --- p.13 / Chapter 2.3 --- Hepatitis B Virus --- p.14 / Chapter 2.3.1 --- Virus Characteristics --- p.15 / Chapter 2.3.2 --- Important Findings on the Virus --- p.17 / Chapter 2.4 --- Bayesian Network and its Classifiers --- p.17 / Chapter 2.4.1 --- Formal Definition --- p.18 / Chapter 2.4.2 --- Existing Learning Algorithms --- p.19 / Chapter 2.4.3 --- Evolutionary Algorithms and Hybrid EP (HEP) --- p.22 / Chapter 2.4.4 --- Bayesian Network Classifiers --- p.25 / Chapter 2.4.5 --- Learning Algorithms for BN Classifiers --- p.32 / Chapter 3 --- Bayesian Network Classifier for Clinical Data --- p.35 / Chapter 3.1 --- Related Work --- p.36 / Chapter 3.2 --- Proposed BN-augmented Naive Bayes Classifier (BAN) --- p.38 / Chapter 3.2.1 --- Definition --- p.38 / Chapter 3.2.2 --- Learning Algorithm with HEP --- p.39 / Chapter 3.2.3 --- Modifications on HEP --- p.39 / Chapter 3.3 --- Proposed General Bayesian Network with Markov Blan- ket (GBN) --- p.40 / Chapter 3.3.1 --- Definition --- p.41 / Chapter 3.3.2 --- Learning Algorithm with HEP --- p.41 / Chapter 3.4 --- Findings on Bayesian Network Parameters Calculation --- p.43 / Chapter 3.4.1 --- Situation and Errors --- p.43 / Chapter 3.4.2 --- Proposed Solution --- p.46 / Chapter 3.5 --- Performance Analysis on Proposed BN Classifier Learn- ing Algorithms --- p.47 / Chapter 3.5.1 --- Experimental Methodology --- p.47 / Chapter 3.5.2 --- Benchmark Data --- p.48 / Chapter 3.5.3 --- Clinical Data --- p.50 / Chapter 3.5.4 --- Discussion --- p.55 / Chapter 3.6 --- Summary --- p.56 / Chapter 4 --- Classification in DNA Analysis --- p.57 / Chapter 4.1 --- Related Work --- p.58 / Chapter 4.2 --- Problem Definition --- p.59 / Chapter 4.3 --- Proposed Methodology Architecture --- p.60 / Chapter 4.3.1 --- Overall Design --- p.60 / Chapter 4.3.2 --- Important Components --- p.62 / Chapter 4.4 --- Clustering --- p.63 / Chapter 4.5 --- Feature Selection Algorithms --- p.65 / Chapter 4.5.1 --- Information Gain --- p.66 / Chapter 4.5.2 --- Other Approaches --- p.67 / Chapter 4.6 --- Classification Algorithms --- p.67 / Chapter 4.6.1 --- Naive Bayes Classifier --- p.68 / Chapter 4.6.2 --- Decision Tree --- p.68 / Chapter 4.6.3 --- Neural Networks --- p.68 / Chapter 4.6.4 --- Other Approaches --- p.69 / Chapter 4.7 --- Important Points on Evaluation --- p.69 / Chapter 4.7.1 --- Errors --- p.70 / Chapter 4.7.2 --- Independent Test --- p.70 / Chapter 4.8 --- Performance Analysis on Classification of DNA Data --- p.71 / Chapter 4.8.1 --- Experimental Methodology --- p.71 / Chapter 4.8.2 --- Using Naive-Bayes Classifier --- p.73 / Chapter 4.8.3 --- Using Decision Tree --- p.73 / Chapter 4.8.4 --- Using Neural Network --- p.74 / Chapter 4.8.5 --- Discussion --- p.76 / Chapter 4.9 --- Summary --- p.77 / Chapter 5 --- Adaptive HEP for Learning Bayesian Network Struc- ture --- p.78 / Chapter 5.1 --- Background --- p.79 / Chapter 5.1.1 --- Objective --- p.79 / Chapter 5.1.2 --- Related Work - AEGA --- p.79 / Chapter 5.2 --- Feasibility Study --- p.80 / Chapter 5.3 --- Proposed A-HEP Algorithm --- p.82 / Chapter 5.3.1 --- Structural Dissimilarity Comparison --- p.82 / Chapter 5.3.2 --- Dynamic Population Size --- p.83 / Chapter 5.4 --- Evaluation on Proposed Algorithm --- p.88 / Chapter 5.4.1 --- Experimental Methodology --- p.89 / Chapter 5.4.2 --- Comparison on Running Time --- p.93 / Chapter 5.4.3 --- Comparison on Fitness of Final Network --- p.94 / Chapter 5.4.4 --- Comparison on Similarity to the Original Network --- p.95 / Chapter 5.4.5 --- Parameter Study --- p.96 / Chapter 5.5 --- Applications on Medical Domain --- p.100 / Chapter 5.5.1 --- Discussion --- p.100 / Chapter 5.5.2 --- An Example --- p.101 / Chapter 5.6 --- Summary --- p.105 / Chapter 6 --- Conclusion --- p.107 / Chapter 6.1 --- Summary --- p.107 / Chapter 6.2 --- Future Work --- p.109 / Bibliography --- p.117