Neonatologia e fim de vida: análise das percepções dos profissionais de saúde

O cuidado no fim de vida em neonatologia é um assunto que desperta diversos conflitos éticos entre os profissionais, principalmente pela possibilidade de adiamento da morte devido aos novos aparatos vindos do desenvolvimento da ciência, mesmo quando a cura não é mais possível. Este estudo analisou de maneira qualitativa a percepção dos profissionais de saúde de uma unidade de terapia intensiva neonatal da rede federal do Rio de Janeiro. Nesta pesquisa foram realizadas vinte entrevistas com fisioterapeutas, médicos, enfermeiros, técnicos de enfermagem, psicólogo e nutricionistas, todas do sexo feminino. Elementos como a percepção em relação a: qual conduta é realizada em pacientes em fim de vida, quais elas acreditam serem as mais adequadas, quais os sentimentos frente a um recémnascido terminal, quem elas percebem que decide nessas situações e quem elas creem que deveria participar do processo de decisão, assim como se elas gostariam de participar caso fossem mães de um bebê terminal, foram colhidos e divididos em categorias para serem discutidos. Como conclusão, nota-se que as profissionais relataram que condutas que levam a distanásia são frequentes no setor, apesar de muitas acreditarem que a melhor terapia seja a de cuidados paliativos. Sentimentos de tristeza, impotência e angústia são comuns entre elas ao lidar com a terminalidade e obstinação terapêutica. A falta de comunicação destaca-se como fator importante na visão das entrevistadas para a pequena contribuição de toda a equipe multidisciplinar e dos pais no processo decisório em situações de fim de vida.

Image-Based Delineation and Classification of Built Heritage Masonry

Fundacion Zain is developing new built heritage assessment protocols. The goal is to objectivize and standardize the analysis and decision process that leads to determining the degree of protection of built heritage in the Basque Country. The ultimate step in this objectivization and standardization effort will be the development of an information and communication technology (ICT) tool for the assessment of built heritage. This paper presents the ground work carried out to make this tool possible: the automatic, image-based delineation of stone masonry. This is a necessary first step in the development of the tool, as the built heritage that will be assessed consists of stone masonry construction, and many of the features analyzed can be characterized according to the geometry and arrangement of the stones. Much of the assessment is carried out through visual inspection. Thus, this process will be automated by applying image processing on digital images of the elements under inspection. The principal contribution of this paper is the automatic delineation the framework proposed. The other contribution is the performance evaluation of this delineation as the input to a classifier for a geometrically characterized feature of a built heritage object. The element chosen to perform this evaluation is the stone arrangement of masonry walls. The validity of the proposed framework is assessed on real images of masonry walls.

Gestão de relacionamento com os clientes em um ambiente automatizado: estudo de caso em um escritório de contabilidade

O entendimento das necessidades dos clientes tornou-se mandatório para sobreviver em um mercado globalizado e altamente competitivo. Por isso, o conceito de gestão de relacionamento com os clientes é fundamental para as empresas. Atualmente, as organizações buscam recursos para atrair, reter e cultivar os clientes. Neste sentido, os escritórios de contabilidade estão investindo no aperfeiçoamento dos métodos de interação com os clientes. Uma maneira diferenciada é a utilização de soluções tecnológicas. Assim, o presente estudo teve por objetivo analisar as estratégias utilizadas por um escritório de contabilidade automatizado para gerir o relacionamento com os seus clientes. Além disso, o objetivo específico foi sugerir estratégias que possam ser aplicadas em escritórios de contabilidade. O estudo foi classificado como uma pesquisa aplicada e exploratória. Para a coleta de dados foi realizado um estudo de caso por meio de uma entrevista semi-estruturada com um empresário de um escritório contábil. A pesquisa constatou que o uso de ferramentas tecnológicas proporciona facilidade de acesso ao escritório, rapidez no negócio e no processo decisório dos clientes. As sugestões de estratégias elencadas no estudo permitem aprimorar os canais de interação dos escritórios de contabilidade, incentivar o uso das soluções tecnológicas e facilitar as tomadas de decisões empresarias por meio das demonstrações financeiras geradas.

Valores e atitudes de médicos e pacientes sobre o processo de decisão clínica: o caso da terapêutica hormonal no climatério

Situado no contexto da qualidade em saúde, este estudo versa sobre a decisão clínica e autonomia do paciente. Parte-se da premissa que, demais da competência técnica profissional e utilização de tecnologia adequada, o respeito aos direitos dos pacientes é atributo essencial à boa qualidade do atendimento médico. Tomando como exemplo a abordagem terapêutica do climatério, foi feita análise qualitativa do processo de decisão clínica, com base nas informações obtidas através de entrevistas semi-estruturadas com médico ginecologistas e com pacientes em fase de climatério. O propósito foi buscar apreender os valores dos médicos e dos pacientes e tentar compreender a lógica de seus comportamentos e atitudes, no que se refere especificamente aos papéis desempenhados por eles nesse processo. Com base nos resultados da análise, discute-se a complexidade da aplicação do princípio da autonomia na prática clínica e apresenta-se uma reflexão sobre a acreditação, como estratégia possível de contribuição a esse processo e à melhoria da qualidade do atendimento médico, por sua grande identificação como os aspectos relativos aos direitos dos pacientes, aos processos de educação permanente e à melhoria contínua da qualidade.

Parameter learning for POMDP spoken dialogue models

The partially observable Markov decision process (POMDP) provides a popular framework for modelling spoken dialogue. This paper describes how the expectation propagation algorithm (EP) can be used to learn the parameters of the POMDP user model. Various special probability factors applicable to this task are presented, which allow the parameters be to learned when the structure of the dialogue is complex. No annotations, neither the true dialogue state nor the true semantics of user utterances, are required. Parameters optimised using the proposed techniques are shown to improve the performance of both offline transcription experiments as well as simulated dialogue management performance. ©2010 IEEE.

Effective handling of dialogue state in the hidden information state POMDP-based dialogue manager

Effective dialogue management is critically dependent on the information that is encoded in the dialogue state. In order to deploy reinforcement learning for policy optimization, dialogue must be modeled as a Markov Decision Process. This requires that the dialogue statemust encode all relevent information obtained during the dialogue prior to that state. This can be achieved by combining the user goal, the dialogue history, and the last user action to form the dialogue state. In addition, to gain robustness to input errors, dialogue must be modeled as a Partially Observable Markov Decision Process (POMDP) and hence, a distribution over all possible states must be maintained at every dialogue turn. This poses a potential computational limitation since there can be a very large number of dialogue states. The Hidden Information State model provides a principled way of ensuring tractability in a POMDP-based dialogue model. The key feature of this model is the grouping of user goals into partitions that are dynamically built during the dialogue. In this article, we extend this model further to incorporate the notion of complements. This allows for a more complex user goal to be represented, and it enables an effective pruning technique to be implemented that preserves the overall system performance within a limited computational resource more effectively than existing approaches. © 2011 ACM.

Reinforcement learning for parameter estimation in statistical spoken dialogue systems

Reinforcement techniques have been successfully used to maximise the expected cumulative reward of statistical dialogue systems. Typically, reinforcement learning is used to estimate the parameters of a dialogue policy which selects the system's responses based on the inferred dialogue state. However, the inference of the dialogue state itself depends on a dialogue model which describes the expected behaviour of a user when interacting with the system. Ideally the parameters of this dialogue model should be also optimised to maximise the expected cumulative reward. This article presents two novel reinforcement algorithms for learning the parameters of a dialogue model. First, the Natural Belief Critic algorithm is designed to optimise the model parameters while the policy is kept fixed. This algorithm is suitable, for example, in systems using a handcrafted policy, perhaps prescribed by other design considerations. Second, the Natural Actor and Belief Critic algorithm jointly optimises both the model and the policy parameters. The algorithms are evaluated on a statistical dialogue system modelled as a Partially Observable Markov Decision Process in a tourist information domain. The evaluation is performed with a user simulator and with real users. The experiments indicate that model parameters estimated to maximise the expected reward function provide improved performance compared to the baseline handcrafted parameters. © 2011 Elsevier Ltd. All rights reserved.

Gaussian processes for fast policy optimisation of POMDP-based dialogue managers

Modelling dialogue as a Partially Observable Markov Decision Process (POMDP) enables a dialogue policy robust to speech understanding errors to be learnt. However, a major challenge in POMDP policy learning is to maintain tractability, so the use of approximation is inevitable. We propose applying Gaussian Processes in Reinforcement learning of optimal POMDP dialogue policies, in order (1) to make the learning process faster and (2) to obtain an estimate of the uncertainty of the approximation. We first demonstrate the idea on a simple voice mail dialogue task and then apply this method to a real-world tourist information dialogue task. © 2010 Association for Computational Linguistics.

Phrase-based statistical language generation using graphical models and active learning

Most previous work on trainable language generation has focused on two paradigms: (a) using a statistical model to rank a set of generated utterances, or (b) using statistics to inform the generation decision process. Both approaches rely on the existence of a handcrafted generator, which limits their scalability to new domains. This paper presents BAGEL, a statistical language generator which uses dynamic Bayesian networks to learn from semantically-aligned data produced by 42 untrained annotators. A human evaluation shows that BAGEL can generate natural and informative utterances from unseen inputs in the information presentation domain. Additionally, generation performance on sparse datasets is improved significantly by using certainty-based active learning, yielding ratings close to the human gold standard with a fraction of the data. © 2010 Association for Computational Linguistics.

Policy optimisation of POMDP-based dialogue systems without state space compression

The partially observable Markov decision process (POMDP) has been proposed as a dialogue model that enables automatic improvement of the dialogue policy and robustness to speech understanding errors. It requires, however, a large number of dialogues to train the dialogue policy. Gaussian processes (GP) have recently been applied to POMDP dialogue management optimisation showing an ability to substantially increase the speed of learning. Here, we investigate this further using the Bayesian Update of Dialogue State dialogue manager. We show that it is possible to apply Gaussian processes directly to the belief state, removing the need for a parametric policy representation. In addition, the resulting policy learns significantly faster while maintaining operational performance. © 2012 IEEE.

On-line policy optimisation of Bayesian spoken dialogue systems via human interaction

A partially observable Markov decision process has been proposed as a dialogue model that enables robustness to speech recognition errors and automatic policy optimisation using reinforcement learning (RL). However, conventional RL algorithms require a very large number of dialogues, necessitating a user simulator. Recently, Gaussian processes have been shown to substantially speed up the optimisation, making it possible to learn directly from interaction with human users. However, early studies have been limited to very low dimensional spaces and the learning has exhibited convergence problems. Here we investigate learning from human interaction using the Bayesian Update of Dialogue State system. This dynamic Bayesian network based system has an optimisation space covering more than one hundred features, allowing a wide range of behaviours to be learned. Using an improved policy model and a more robust reward function, we show that stable learning can be achieved that significantly outperforms a simulator trained policy. © 2013 IEEE.

Gaussian processes for POMDP-based dialogue manager optimization

A partially observable Markov decision process (POMDP) has been proposed as a dialog model that enables automatic optimization of the dialog policy and provides robustness to speech understanding errors. Various approximations allow such a model to be used for building real-world dialog systems. However, they require a large number of dialogs to train the dialog policy and hence they typically rely on the availability of a user simulator. They also require significant designer effort to hand-craft the policy representation. We investigate the use of Gaussian processes (GPs) in policy modeling to overcome these problems. We show that GP policy optimization can be implemented for a real world POMDP dialog manager, and in particular: 1) we examine different formulations of a GP policy to minimize variability in the learning process; 2) we find that the use of GP increases the learning rate by an order of magnitude thereby allowing learning by direct interaction with human users; and 3) we demonstrate that designer effort can be substantially reduced by basing the policy directly on the full belief space thereby avoiding ad hoc feature space modeling. Overall, the GP approach represents an important step forward towards fully automatic dialog policy optimization in real world systems. © 2013 IEEE.

强化学习算法中启发式回报函数的设计及其收敛性分析

回报函数设计的好与坏对学习系统性能有着重要作用,按回报值在状态-动作空间中的分布情况,将回报函数的构建分为两种形式:密集函数和稀疏函数,分析了密集函数和稀疏函数的特点.提出启发式回报函数的基本设计思路,利用基于保守势函数差分形式的附加回报函数,给学习系统提供更多的启发式信息,并对算法的最优策略不变性和迭代收敛性进行了证明.启发式回报函数能够引导学习,加快学习进程,从而可以实现强化学习在实际大型复杂系统应用中的实时控制和调度.

Why do people non-demand reveal in hypothetical double referenda for public goods?

Hypothetical contingent valuation surveys used to elicit values for environmental and other public goods often employ variants of the referendum mechanism due to the cognitive simplicity and familiarity of respondents with this voting format. One variant, the double referendum mechanism, requires respondents to state twice how they would vote for a given policy proposal given their cost of the good. Data from these surveys often exhibit anomalies inconsistent with standard economic models of consumer preferences. There are a number of published explanations for these anomalies, mostly focusing on problems with the second vote. This article investigates which aspects of the hypothetical task affect the degree of nondemand revelation and takes an individual-based approach to identifying people most likely to non-demand reveal. A clear profile emerges from our model of a person who faces a negative surplus i.e. a net loss in the second vote and invokes non self-interested, non financial motivations during the decision process.

Dynamic pricing models for used products in remanufacturing with lost-sales and uncertain quality

In this paper, we investigate the remanufacturing problem of pricing single-class used products (cores) in the face of random price-dependent returns and random demand. Specifically, we propose a dynamic pricing policy for the cores and then model the problem as a continuous-time Markov decision process. Our models are designed to address three objectives: finite horizon total cost minimization, infinite horizon discounted cost, and average cost minimization. Besides proving optimal policy uniqueness and establishing monotonicity results for the infinite horizon problem, we also characterize the structures of the optimal policies, which can greatly simplify the computational procedure. Finally, we use computational examples to assess the impacts of specific parameters on optimal price and reveal the benefits of a dynamic pricing policy. © 2013 Elsevier B.V. All rights reserved.