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Trust and its antecedents have been demonstrated as a barrier to the successful adoption of numerous fields of technology, most notably e-commerce, and may be a key factor in the lack of adoption or adaptation in the field of telemedicine. In the medical arena, trust is often formed through the relationships cultivated over time via clinician and patient. Trust and interpersonal relationships may also play a significant role in the adoption of telemedicine. The idea of telemedicine has been explored for nearly 30 years in one form or another. Yet, despite grandiose promises of how it will someday significantly improve the healthcare system, the field continues to lag behind other areas of technology by 10 to 15 years. The reasons for the lack of adoption may be many given the barriers that have been observed by other researchers with regards to trust and trustworthiness. This study examined the role of trust from various aspects within telemedicine, with particular emphasis on the role that trust plays in the adoption and adaptation of a telemedicine system. Simulators examined the role of trust in the treatment and management of diabetes mellitus (common illness) in order to assess the impact and role of trust components. Surveys of the subjects were conducted to capture the trust dynamics, as well as the development of a framework for successful implementation of telemedicine using trust and trustworthiness as a foundation. Results indicated that certain attributes do influence the level of trust in the system. The framework developed demonstrated that medical content, disease state management, perceived patient outcomes, and design all had significant impact on trust of the system.

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This dissertation explores motivation in decision-making and action in science and technology, through the lens of a case study: scientific data sharing in cancer research. The research begins with the premise that motivation and emotion are key elements of what it means to be human, and consequently, are important variables in how individuals make decisions and take action. At the same time, institutional controls and social messaging send a variety of signals intended to motivate specific actions and behaviors. Understanding the interplay between personal motives and social influences may point to strategies that better align individual and social perceptions and discourse. To explore these dynamics, this research centers on a large-scale cancer research program led by the National Institutes of Health's National Cancer Institute. The goal of the program is to encourage interoperability and data sharing between diverse and highly autonomous cancer centers across the U.S. Housed in an organization focused on biomedical informatics, the program has a technologically-focused mission; the goal is to facilitate institutional data sharing to connect the cancer research enterprise. This focus contrasts with the more relationship-based point-to-point data sharing currently reported by researchers as the norm. Researchers are motivated to share data with others under specific conditions: when there is a foundation of trust with the person or community being shared with; when the perceived reward of sharing is well-defined and of value to the person sharing; and when there is perceived to be a lower risk or cost than the benefit received. Without these conditions, there are often determined to be insufficient incentives and rewards for sharing. Data sharing is both a personal decision and a social level problem. Data is both subjective and personal; it is often an extension of researcher's identity, and serves as a measure of his or her value and capability. In the search for standards and interoperable data sets, institutional and technologically-mediated forms of data sharing are perceived to ignore the subjective and local knowledge embodied in the data being shared. To explore these dimensions, this study considers the technology, economics, legal elements, and personal sides of data sharing, and applies two conceptual frameworks to evaluate alternatives for action. / Ph. D.

Introdução: Os medicamentos são importantes elementos da maioria dos esquemas terapêuticos cobertos pelo Sistema Único de Saúde (SUS), representando significativa parcela do orçamento no país. O Complexo de Saúde vinculado ao Hospital das Clínicas atende toda a região noroeste do Estado de São Paulo e de outras partes do estado e do país, como centro de referência em tratamentos de alta complexidade, sendo frequente a prescrição de medicamentos de alto custo (MAC). Estima-se que 75,4% do orçamento geral para compra de medicamentos do complexo HCRP-FMRP-USP, são dedicados à aquisição de medicação não padronizada (medicamento especial) num total de aproximadamente R$ 46.313.170,08 (2015). Sendo assim, ferramentas para controle não só da prescrição, como também da aquisição e seu uso são fundamentais para otimizar a gestão do Hospital, evoluindo de um caráter reativo a um proativo, no qual a tomada de decisões tenha como base um histórico e indicadores de casos apresentados no complexo. Objetivo: Desenvolver uma plataforma eletrônica baseada na rede mundial de computadores, que possibilite a gestão entendida como documentação, rastreabilidade e inter-relacionamento entre os componentes da cadeia de decisão de medicamentos considerados especiais no Hospital das Clínicas da Faculdade de Medicina de Ribeirão Preto da Universidade de São Paulo. Métodos: Compreendeu o desenvolvimento de um sistema que tem como características principais monitoramento, acompanhamento e controle da cadeia de decisão de medicamentos que são considerados especiais pela instituição. Este sistema também permite a tomada de decisões, o desenvolvimento de indicadores em tempo real para decisão administrativa e o controle que requer a cadeia de suprimento de medicamentos de alto custo em cada um dos seus componentes. Resultados: Maior e melhor comunicação entre as unidades de farmácia, o solicitante (médico), o Departamento de atenção à Saúde (DAS) e os locais do Complexo HC-FMRP-USP que compõem a cadeia de decisão do suprimento de medicamentos especiais (MAC); além disso, possibilitará organizar um histórico de dados que poderá ser transposto facilmente a indicadores para o plano assistencial à medida, que garanta a presença de um agente transformador. Conclusões: Uma plataforma eletrônica foi desenvolvida que permite armazenamento, gestão e o processamento de dados e informações respeito à cadeia de decisão do fornecimento de medicamentos não padronizados / Introduction: Medicines are important elements in health care, especially those covered by the Brazilian Unified Healthcare System - Sistema Único de Saúde (SUS), representing a significant portion of its budget. The health infrastructure linked to Hospital das Clínicas serves throughout the northwest region of State the São Paulo and other parts of the state and the country. It is, therefore, known as a reference center for highly complex treatments and, for this reason, frequently prescribes treatments with expensive drugs. Is estimated that 75.4% of the general budget of HCRP-FMRP-USP complex is dedicated to the acquisition of this type of medication, i.e., not standardized medication (special medication), that has a value of approximately USD $14.434.300 (2015). Therefore, tools for controlling not only the prescription, as well as the acquisition and use, becomes critical to optimize the management of the hospital, aiming to move from a reactive to proactive role, where decision-making is based on a history and on indicators of the cases presented in the complex. Objective: To develop an electronic platform based on the World Wide Web, which allows the management, documentation, traceability and interrelationship between the components of the considered decision chain of nonstandard medicines in the Clinics Hospital of Ribeirão Preto Medical School of the University of Sao Paulo. Methods: Include a software development that has, as main features, tracking, monitoring and control of decision chain of drugs, which are considered special by the institution. This software also allows making decisions, development of indicators in real-time and administrative decisions that require the regulatory control supply system of high cost of medicines in each of its components. Results: Further and improved communication between the pharmacy units, the applicant (physician), the Department of attention to health (DAS) and places from the HC-FMRP-USP complex that integrate the chain of decision of the special drug supply. Moreover, organize a data history, which easily can be implemented to indicators for the assistance plan as guaranteeing the presence of a transforming agent. Conclusions: Developed an electronic platform that enables storage, management and processing of data and information, considering the chain decision of nonstandard medicines supply.

Doctor of Philosophy(PhD) / ABSTRACT Despite the extensive quantitative and qualitative knowledge about therapeutic regimens and the molecular biology of HIV/AIDS, the eradication of HIV infection cannot be achieved with available antiretroviral regimens. HIV drug resistance remains the most challenging factor in the application of approved antiretroviral agents. Previous investigations and existing HIV/AIDS models and algorithms have not enabled the development of long-lasting and preventive drug agents. Therefore, the analysis of the dynamics of drug resistance and the development of sophisticated HIV/AIDS analytical algorithms and models are critical for the development of new, potent antiviral agents, and for the greater understanding of the evolutionary behaviours of HIV. This study presents novel computational methods for the analysis of drug-resistance dynamics, including: viral sequences, phenotypic resistance, immunological and virological responses and key clinical data, from HIV-infected patients at Royal Prince Alfred Hospital in Sydney. The lability of immunological and virological responses is analysed in the context of the evolution of antiretroviral drug-resistance mutations. A novel Bayesian algorithm is developed for the detection and classification of neutral and adaptive mutational patterns associated with HIV drug resistance. To simplify and provide insights into the multifactorial interactions between viral populations, immune-system cells, drug resistance and treatment parameters, a Bayesian graphical model of drug-resistance dynamics is developed; the model supports the exploration of the interdependent associations among these dynamics.

In this dissertation we study the feature selection and classification problems and apply our methods to real-world medical and biological data sets for disease diagnosis. Classification is an important problem in disease diagnosis to distinguish patients from normal population. DAMIP (discriminant analysis -- mixed integer program) was shown to be a good classification model, which can directly handle multigroup problems, enforce misclassification limits, and provide reserved judgement region. However, DAMIP is NP-hard and presents computational challenges. Feature selection is important in classification to improve the prediction performance, prevent over-fitting, or facilitate data understanding. However, this combinatorial problem becomes intractable when the number of features is large. In this dissertation, we propose a modified particle swarm optimization (PSO), a heuristic method, to solve the feature selection problem, and we study its parameter selection in our applications. We derive theories and exact algorithms to solve the two-group DAMIP in polynomial time. We also propose a heuristic algorithm to solve the multigroup DAMIP. Computational studies on simulated data and data from UCI machine learning repository show that the proposed algorithm performs very well. The polynomial solution time of the heuristic method allows us to solve DAMIP repeatedly within the feature selection procedure. We apply the PSO/DAMIP classification framework to several real-life medical and biological prediction problems. (1) Alzheimer's disease: We use data from several neuropsychological tests to discriminate subjects of Alzheimer's disease, subjects of mild cognitive impairment, and control groups. (2) Cardiovascular disease: We use traditional risk factors and novel oxidative stress biomarkers to predict subjects who are at high or low risk of cardiovascular disease, in which the risk is measured by the thickness of the carotid intima-media or/and the flow-mediated vasodilation. (3) Sulfur amino acid (SAA) intake: We use 1H NMR spectral data of human plasma to classify plasma samples obtained with low SAA intake or high SAA intake. This shows that our method helps for metabolomics study. (4) CpG islands for lung cancer: We identify a large number of sequence patterns (in the order of millions), search candidate patterns from DNA sequences in CpG islands, and look for patterns which can discriminate methylation-prone and methylation-resistant (or in addition, methylation-sporadic) sequences, which relate to early lung cancer prediction.

Doctor of Philosophy(PhD) / ABSTRACT Despite the extensive quantitative and qualitative knowledge about therapeutic regimens and the molecular biology of HIV/AIDS, the eradication of HIV infection cannot be achieved with available antiretroviral regimens. HIV drug resistance remains the most challenging factor in the application of approved antiretroviral agents. Previous investigations and existing HIV/AIDS models and algorithms have not enabled the development of long-lasting and preventive drug agents. Therefore, the analysis of the dynamics of drug resistance and the development of sophisticated HIV/AIDS analytical algorithms and models are critical for the development of new, potent antiviral agents, and for the greater understanding of the evolutionary behaviours of HIV. This study presents novel computational methods for the analysis of drug-resistance dynamics, including: viral sequences, phenotypic resistance, immunological and virological responses and key clinical data, from HIV-infected patients at Royal Prince Alfred Hospital in Sydney. The lability of immunological and virological responses is analysed in the context of the evolution of antiretroviral drug-resistance mutations. A novel Bayesian algorithm is developed for the detection and classification of neutral and adaptive mutational patterns associated with HIV drug resistance. To simplify and provide insights into the multifactorial interactions between viral populations, immune-system cells, drug resistance and treatment parameters, a Bayesian graphical model of drug-resistance dynamics is developed; the model supports the exploration of the interdependent associations among these dynamics.

abstract: Proliferation of social media websites and discussion forums in the last decade has resulted in social media mining emerging as an effective mechanism to extract consumer patterns. Most research on social media and pharmacovigilance have concentrated on Adverse Drug Reaction (ADR) identification. Such methods employ a step of drug search followed by classification of the associated text as consisting an ADR or not. Although this method works efficiently for ADR classifications, if ADR evidence is present in users posts over time, drug mentions fail to capture such ADRs. It also fails to record additional user information which may provide an opportunity to perform an in-depth analysis for lifestyle habits and possible reasons for any medical problems. Pre-market clinical trials for drugs generally do not include pregnant women, and so their effects on pregnancy outcomes are not discovered early. This thesis presents a thorough, alternative strategy for assessing the safety profiles of drugs during pregnancy by utilizing user timelines from social media. I explore the use of a variety of state-of-the-art social media mining techniques, including rule-based and machine learning techniques, to identify pregnant women, monitor their drug usage patterns, categorize their birth outcomes, and attempt to discover associations between drugs and bad birth outcomes. The technique used models user timelines as longitudinal patient networks, which provide us with a variety of key information about pregnancy, drug usage, and post- birth reactions. I evaluate the distinct parts of the pipeline separately, validating the usefulness of each step. The approach to use user timelines in this fashion has produced very encouraging results, and can be employed for a range of other important tasks where users/patients are required to be followed over time to derive population-based measures. / Dissertation/Thesis / Masters Thesis Computer Science 2016

abstract: Text mining of biomedical literature and clinical notes is a very active field of research in biomedical science. Semantic analysis is one of the core modules for different Natural Language Processing (NLP) solutions. Methods for calculating semantic relatedness of two concepts can be very useful in solutions solving different problems such as relationship extraction, ontology creation and question / answering [1–6]. Several techniques exist in calculating semantic relatedness of two concepts. These techniques utilize different knowledge sources and corpora. So far, researchers attempted to find the best hybrid method for each domain by combining semantic relatedness techniques and data sources manually. In this work, attempts were made to eliminate the needs for manually combining semantic relatedness methods targeting any new contexts or resources through proposing an automated method, which attempted to find the best combination of semantic relatedness techniques and resources to achieve the best semantic relatedness score in every context. This may help the research community find the best hybrid method for each context considering the available algorithms and resources. / Dissertation/Thesis / Doctoral Dissertation Biomedical Informatics 2016