Mesmo com a evolução tecnológica em vários setores, a área de enfermagem tem tido investimentos escassos em pesquisa e desenvolvimento capazes de atender suas expectativas, principalmente no campo da inteligência artificial. As expectativas dos enfermeiros convergem à melhora de seus processos clínicos que resultará em uma maior aproximação de seus pacientes. Além disso, há dificuldade em reunir diagnósticos de enfermagem nos hospitais, onde diversos registros clínicos e procedimentos preenchidos manualmente e armazenados ainda em folhas de papel. Esta condição compromete a legibilidade dos documentos envolvidos nos processos hospitalares, e seu arquivamento torna o processo de levantamento de informações moroso, o que acaba por inviabilizar a pesquisa à qual poderia resultar em informações importantes para melhora do processo de tomada de decisões. O objetivo desta dissertação foi trazer o estado da arte em inteligência artificial focado em raciocínio baseado em casos e sua aplicação na sistematização da assistência de enfermagem. No sentido de validar o modelo levantado foi criado um protótipo para apresentar uma aplicação que pudesse auxiliar os enfermeiros em seus processos clínicos, armazenando suas experiências em uma base de casos para futuras pesquisas. O protótipo consistiu em digitalizar diagnósticos de enfermagem pediátrica, e inserção em uma base de casos, com o intuito de avaliar a eficácia do protótipo na manipulação destes casos, em uma estrutura propicia para recuperação, adaptação, indexação e comparação de casos. Esta dissertação apresenta como resultado uma ferramenta computacional para a área da saúde, empregando uma das técnicas de inteligência artificial, Raciocínio Baseados em Casos. Os resultados foram satisfatórios devido ao alto índice de aprovação nos quesitos confiabilidade, funcionalidade, usabilidade e eficiência conforme as normas ISO/ABNT de qualidade em software. / Even with the development of technology in many industries, the nursing sector has had low investment in research and development, mainly in the field of artificial intelligence. The expectations of nurses converge to improvement over his clinical procedures that will result in a closer relationship with their patients. Moreover, there is difficulty in finding nursing diagnoses in hospitals, while clinical records and procedures are completed manually and stored even on paper. This condition compromises the readability of documents involved in the admissions process, and archiving that also becomes time-consuming process of information gathering, which derail research that could result in important information for improved decision-making. The objective of this dissertation was to bring the state of the art regarding artificial intelligence focusing on Case-Based Reasoning and its application in the systematization of nursing care. In order to validate the model a prototype was set up to demonstrate an application that would assist nurses in their clinical files, storing their experiences in a case base for future research. The prototype was to scan diagnosis pediatric nursing, and insertion into a case base in order to evaluate the effectiveness of the prototype in handling those cases. It also provides a framework for recovery, adaptation, indexing, and comparison of cases. This dissertation presents results in a computational tool for health employing one of the techniques of artificial intelligence: Case-Based Reasoning. The results were satisfactory due to high rate in terms of structure reliability, functionality, usability and efficiency according to ISO / ABNT quality in software.

Made available in DSpace on 2016-04-29T14:23:07Z (GMT). No. of bitstreams: 1 Allex Motta Melo da Rocha.pdf: 4303012 bytes, checksum: 94163da5edc7a5c0777f5f42582edc52 (MD5) Previous issue date: 2012-05-24 / This current dissertation aims to use the techniques of Case-Based Reasoning (CBR) and Geographic Information System (GIS) together to identify and characterize the impact of a contagious injury in the city of Belém, Pará state, Brazil, caused by the bacterium Leptospira interrogans, known as leptospirosis. Environmental and health issues, particularly as the presence of rodent droppings, urine and feces, influencing an establishment of this disease. This research conducts a study of the use of CBR technique in order to formulate and develop a model to identify and assist in the diagnosis of the disease, using data collected from patients, such as epidemiological history and clinical, in the area, in the years 2008, 2009 and 2010. The CBR system will enable health professionals can use it as a support tool in the diagnosis of cases of the disease. This dissertation also applies GIS techniques to visually express and produce environmental analysis, temporal and socioeconomic of leptospirosis epidemiology scenario in a neighborhood in the city of Belém, in the years mentioned above, due to yours epidemiological importance. So, there will be the environmental and socioeconomic characteristics, in order to analyze the spatial distribution of the main reservoirs of this disease and to identify the habits of life of people living or working in these places, considering the environmental factor to provide subsidies to adopt preventive measures to control of disease incidence / A presente dissertação tem como objetivo utilizar as técnicas de Raciocínio Baseado em Casos (RBC) e Geoprocessamento, em conjunto, para identificar e caracterizar a incidência de um agravo infectocontagioso, na cidade de Belém, estado do Pará, Brasil, ocasionado pela bactéria Leptospira interrogans, conhecido como leptospirose humana. Questões ambientais e sanitárias, como a presença, principalmente, de excrementos de roedores, como urina e fezes influenciam o estabelecimento deste agravo. Esta pesquisa realiza um estudo do uso da técnica de RBC, com o objetivo de formular e desenvolver um modelo para identificar e auxiliar no diagnóstico da doença, por meio de dados coletados dos pacientes, tais como antecedentes epidemiológicos e clínicos, ocorridos na região, nos anos de 2008, 2009 e 2010. O sistema de RBC possibilitará que profissionais da saúde possam utilizá-lo como ferramenta de apoio na diagnose de casos da doença. Esta dissertação também aplica técnicas de Geoprocessamento para expressar visualmente e produzir análises ambientais, temporais e socioeconômicas do cenário epidemiológico da leptospirose em um bairro na cidade de Belém, nos anos citados anteriormente, devido à importância epidemiológica do mesmo. Para tal, será realizada a caracterização ambiental e socioeconômica, a fim de analisar a distribuição espacial dos principais reservatórios deste agravo e identificar os hábitos de vida das populações que moram ou trabalham nestes locais, considerando o fator ambiental para fornecer subsídios à adoção de medidas preventivas para o controle da incidência desta doença

With the rise of Deep Learning the field of Artificial Intelligence (AI) Research has entered a new era. Together with an increasing amount of data and vastly improved computing capabilities, Machine Learning builds the backbone of AI, providing many of the tools and algorithms that drive development and applications. While we have already achieved many successes in the fields of image recognition, language processing, recommendation engines, robotics, or autonomous systems, most progress was achieved when the algorithms were focused on learning only a single task with little regard to effort and reusability. Since learning a new task from scratch often involves an expensive learning process, in this work, we are considering the use of previously acquired knowledge to speed up the learning of a new task. For that, we investigated the application of Transfer Learning methods for Deep Reinforcement Learning (DRL) agents and propose a novel framework for knowledge preservation and reuse. We show, that the knowledge transfer can make a big difference if the source knowledge is chosen carefully in a systematic approach. To get to this point, we provide an overview of existing literature of methods that realize knowledge transfer for DRL, a field which has been starting to appear frequently in the relevant literature only in the last two years. We then formulate the Case-based Reasoning methodology, which describes a framework for knowledge reuse in general terms, in Reinforcement Learning terminology to facilitate the adaption and communication between the respective communities. Building on this framework, we propose Deep Case-based Policy Inference (DECAF) and demonstrate in an experimental evaluation the usefulness of our approach for sequential task learning with knowledge preservation and reuse. Our results highlight the benefits of knowledge transfer while also making aware of the challenges that come with it. We consider the work in this area as an important step towards more stable general learning agents that are capable of dealing with the most complex tasks, which would be a key achievement towards Artificial General Intelligence. / Com a evolução da Aprendizagem Profunda (Deep Learning), o campo da Inteligência Artificial (IA) entrou em uma nova era. Juntamente com uma quantidade crescente de dados e recursos computacionais cada vez mais aprimorados, o Aprendizado de Máquina estabelece a base para a IA moderna, fornecendo muitas das ferramentas e algoritmos que impulsionam seu desenvolvimento e aplicações. Apesar dos muitos sucessos nas áreas de reconhecimento de imagem, processamento de linguagem natural, sistemas de recomendação, robótica e sistemas autônomos, a maioria dos avanços foram feitos focando no aprendizado de apenas uma única tarefa, sem muita atenção aos esforços dispendidos e reusabilidade da solução. Como o aprendizado de uma nova tarefa geralmente envolve um processo de aprendizado despendioso, neste trabalho, estamos considerando o reúso de conhecimento para acelerar o aprendizado de uma nova tarefa. Para tanto, investigamos a aplicação dos métodos de Transferência de Aprendizado (Transfer Learning) para agentes de Aprendizado por Reforço profundo (Deep Reinforcement Learning - DRL) e propomos um novo arcabouço para preservação e reutilização de conhecimento. Mostramos que a transferência de conhecimento pode fazer uma grande diferença no aprendizado se a origem do conhecimento for escolhida cuidadosa e sistematicamente. Para chegar a este ponto, nós fornecemos uma visão geral da literatura existente de métodos que realizam a transferência de conhecimento para DRL, um campo que tem despontado com frequência na literatura relevante apenas nos últimos dois anos. Em seguida, formulamos a metodologia Raciocínio baseado em Casos (Case-based Reasoning), que descreve uma estrutura para reutilização do conhecimento em termos gerais, na terminologia de Aprendizado por Reforço, para facilitar a adaptação e a comunicação entre as respectivas comunidades. Com base nessa metodologia, propomos Deep Casebased Policy Inference (DECAF) e demonstramos, em uma avaliação experimental, a utilidade de nossa proposta para a aprendizagem sequencial de tarefas, com preservação e reutilização do conhecimento. Nossos resultados destacam os benefícios da transferência de conhecimento e, ao mesmo tempo, conscientizam os desafios que a acompanham. Consideramos o trabalho nesta área como um passo importante para agentes de aprendizagem mais estáveis, capazes de lidar com as tarefas mais complexas, o que seria um passo fundamental para a Inteligência Geral Artificial.

<p>A difficult issue in stress management is to use biomedical sensor signal in the diagnosis and treatment of stress. Clinicians often make their diagnosis and decision based on manual inspection of physiological signals such as, ECG, heart rate, finger temperature etc. However, the complexity associated with manual analysis and interpretation of the signals makes it difficult even for experienced clinicians. Today the diagnosis and decision is largely dependent on how experienced the clinician is interpreting the measurements.  A computer-aided decision support system for diagnosis and treatment of stress would enable a more objective and consistent diagnosis and decisions.</p><p>A challenge in the field of medicine is the accuracy of the system, it is essential that the clinician is able to judge the accuracy of the suggested solutions. Case-based reasoning systems for medical applications are increasingly multi-purpose and multi-modal, using a variety of different methods and techniques to meet the challenges of the medical domain. This research work covers the development of an intelligent clinical decision support system for diagnosis, classification and treatment in stress management. The system uses a finger temperature sensor and the variation in the finger temperature is one of the key features in the system. Several artificial intelligence techniques have been investigated to enable a more reliable and efficient diagnosis and treatment of stress such as case-based reasoning, textual information retrieval, rule-based reasoning, and fuzzy logic. Functionalities and the performance of the system have been validated by implementing a research prototype based on close collaboration with an expert in stress. The case base of the implemented system has been initiated with 53 reference cases classified by an experienced clinician. A case study also shows that the system provides results close to a human expert. The experimental results suggest that such a system is valuable both for less experienced clinicians and for experts where the system may function as a second option.</p> / IPOS, PROEK

Visual media are of great importance to designers. Understanding a new design, for example, often means understanding a drawing. From the perspective of artificial intelligence, this implies that automated knowledge acquisition in computer-aided design can productively occur using drawings as a knowledge source. However, this requires machines that are able to interpret design drawings. I view the task of interpreting drawings as one of constructing a teleological model of the design depicted in the drawings, where the model enables causal and functional inferences about the depicted design. I have developed a novel analogical method for constructing a teleological model of a mechanical device from an unlabelled 2D line drawing. The source case is organized in a Drawing Shape Structure Behavior Function (DSSBF) abstraction hierarchy. This knowledge organization enables the analogical mapping and transfer to occur at multiple levels of abstraction. Given a target drawing and a relevant source case, my method of compositional analogy first constructs a graphical representation of the lines and the intersections in the target drawing, then uses the mappings at the level of line intersections to transfer the shape representations from the source case to the target. It next uses the mappings at the level of shapes to transfer the structural model of the device from the source to the target. Finally, the mappings from the source to the target structural model enable the transfer of behaviors and the functional specification from source to target, completing the analogy and yielding a complete DSSBF model of the input drawing. The Archytas system implements this method of compositional analogy and evaluates it in the domain of kinematic devices such as piston and crankshaft devices, door latches, and pulley systems.

老人居家照護是近來愈趨重視的議題，過去ㄧ直以來主要著重在老人生理狀態的偵測及相關居家醫療儀器的研究，但除了生理上訊號所顯現的不適之外，尚有其它的問題困擾著老人的生活。對於在老人身上所產生的許多不適，最直接的就是反映在老人的情緒上，若是能針對老人目前所處的環境狀態分析出造成老人情緒狀態轉變的因素，將有助於提升老人的生活品質。本研究所採用的替換調適模式之案例式推理，有別於一般案例式推理的應用，一般案例式推理需要對於應用領域的知識有相當了解才能達到有效地案例調適，因此在發展案例式推理的應用時，需要經過相當長的資訊收集，而替換調適模式運用一些已經存在的案例，從中萃取出案例間的關聯性，並藉由案例的不斷累積來自動化的調適案例庫中的知識，因此將使得推理的結果更符合老人過去的生活習性，因此能針對老人的情緒狀態找出形成的因素，而找出改變情緒的形成因素之後，將有機會的進一步解決老人目前所遭遇的生活難題，最終本研究期望能藉此達成提升老人生活品質的目的。 / e-Care for aging has become an increasingly important research topic in recent years. Most research focus on the detection of Physiological state or the study of the e-Care medical devices. Nevertheless, there are still other problems tormenting an aging’s life besides physiological discomfort detected from physiological signals. For instance, it is often the case that the discomfort comes from the aging's atypical mood status. In other words, causes behind the change of the aging’s mood status would help improve the quality of the aging’s life. Accordingly, this paper presents a substitution-based case adaptation CBR to analyze the causes of effecting the change of the aging’s mood status. Substitution-based case adaptation CBR differs from general CBR in lean adaptation knowledge required. Most existing CBR systems rely on an enormous amount of built-in adaptation knowledge in the form of adaptation rules (that require a deep analysis of the domain). Substitution-based case adaptation can make use of a limited number of cases to extract the relations between the cases and reach automatic adaptation. With the accumulation of cases in the case library, the result of inference fit in line with the habit of the aging’s life would be improved based on this automatic adaptation. The contribution of our method aims at reaching the e-Care goal of improving the aging’s life quality from the mental perspective.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

In 1998 the Institutes of Nursing in the United Arab Emirates adopted a new approach of Case-Based learning in their three year nursing program. This categorical change created a need to examine the development of nursing students' self-efficacy and motivation under this new curriculum. The aims of this study were to investigate whether the students in the final year of the program will evidence higher levels of motivation and self-efficacy compared to those in the second and first year of the program, and to demonstrate that the higher the level of self-efficacy, the more internal, unstable and controllable the attributions for success and/or failure. This study was based on Bandura's theory of self-efficacy. A randomly selected cross-sectional survey involving nursing students in the three-year diploma nursing program of Abu-Dhabi and AI Ain Institutes was studied (N=178). The participants of this study involved a total of 86 (48.3%) enrolled in year one diploma, 51 (28.7%) enrolled in year two and 41 (23.0%) in year three diploma. On the whole the participants' levels of motivation and self-efficacy were high, but both ANOVA and Kruskall Wallis tests did not support any level increase by year of education as was expected. However, the Spearman's rho (r) test yielded low but positive and significant correlations between the levels of self-efficacy and students attribution for success and/or failure. / Thesis (M.Cur.)-University of Natal, Durban, 2002.

This study describes the perceptions of 110 diploma nursing students regarding the learning environment in case based and traditional classrooms. The study was conducted in Sharjah and Abu Dhabi Institutes ofNursing in UAE. Sixty Level I and fifty Level II students were randomly selected from the two institutes to participate in this study. The purpose of this study was to examine nursing students' perceptions regarding the classroom learning environment in courses that are taught by case studies and those that are taught by traditional method, and to compare their perceptions toward conventional and CBL learning environments. The questionnaire used was the College and University Classroom Environment Inventory (CUCEI). The students in the CBL class had more positive perceptions toward their learning environment on the subscales Involvement , Innovation , and Individualization. A significant difference emerged favoring traditional classroom environment on the Task orientation subscale. There was no significant difference between the participants' ratings for the case-based and traditional classroom learning environments on the subscales of Personalization, Cohesiveness, and Satisfaction. Thus, the study results support the superiority ofCBL environment regarding the students' perception of Involvement, Innovation, and individualization. Moreover, the study results indicated that students perceived class activities to be more task oriented in traditional than in case based classroom environments. / Thesis (M.Cur.)-University of Natal, Durban, 2002.

Background: Case-based learning (CBL) is one approach which is gaining popularity. The selected School of Nursing adopted this approach to learning in 2000. The school reviewed all the old case studies and introduced new ones in 2010. These new case studies were used for the first time with the 2011 Decentralised PHC programme. Hence, the need for a study that explored students perceptions towards case-based learning. Research Methodology: A quantitative approach and descriptive exploratory design were adopted in this study. A total number of 101 students were from the three Decentralised Primary health care (PHC) programme participated in this study. Data was collected from three decentralised sites used to offer PHC programme to students; Durban, Port Shepstone and Pietermaritzburg. Data was collected by means of a questionnaire – a self-reporting instrument which included 62 structured questions. Data was analysed statistically for frequency distributions and the relationship between the results from three sites were explored using a Chi Square Pearson Test, with a p value set at .005. Results: The results of this study show that, overall, students view case-based learning in a positive light. Although the majority reported that cases were distributed in case study booklet form (n=66) 65.3%, other modes such as cases presented to students in each class session using transparencies (n=52) 51.5% as well as through emails was reported. Regarding benefits about (n=90) 90.1% of the respondents stated that cases presented added a lot of realism. The researcher also explored to see if there were any variations of results across the three delivery centres. The findings of this study demonstrated that there was a significant difference for seven items, some of these items included teachers feedback and advise after class was relevant for students to cope on their own p<.000; case studies preparing student for working in PHC institutions in South Africa p<.042; preferred lectures more than CBL p<.003 and CBL was too demanding in terms of preparation and the content p<.004. Discussion: Students enjoyed the experience but also identified their own limitations and mistakes. They were able to plan their own learning needs. The students experienced personal and professional growth using cases which represented the real-life challenges. They had the opportunity to apply the theory, their experiences and their newly developed skills from the use of CBL in there clinical practice. The end product was visible and of benefit to the clinical settings as students was now competent in analyzing cases and solving practical problems systematically. Providing a real-life challenge to students in CBL instead of teaching a predominantly theoretical course proved to be beneficial. Recommendations: The researcher felt that a follow-up study, taking into account all six sites where students are placed could result in a difference in the students’ perceptions of CBL as a teaching methodology, as the students in the other three sites are in the more rural areas which limits their resources. There was some inconsistency with presentation and distribution of cases also feedback to students, staff may require development with teaching skills to co-ordinate all centres in a similar way. Port Shepstone and Pietermaritzburg struggle with CBL, maybe they are not incline with Self directed learning (SDL), therefore a follow up study in these centres would assess in students are inclined with SDL. / Thesis (M.N.)-University of KwaZulu-Natal, Durban, 2013.