Platform for AI-driven medical data analysis to support clinical decision

Cancer is one of the leading causes of death on the world and surviving its treatment does not mean that the process is over. Several patients that have undergone cancer treatment, feel insecure in relation to their health, due to the stress and anxiety of cancer reappearance and post-treatment symptoms such as: sleeping disorders, fatigue and memory problems, pain, anxiety, and stress. Patients that undergone cancer treatment are followed periodically by a clinician, that evaluates its clinical situation, but also, his Quality of Life. This information is vital to understand the patient well-being, since cancer as a huge impact on all aspects of the patient’s life. Nevertheless, clinicians lack on tools capable of measuring objectively the patient’s Quality of Life, nor tools that enable more data visualization that could improve the clinician’s decision-making. So, the purposed aim of this dissertation is to provide a Clinical Decision Support System Platform with visualization tools capable of giving information from patients, gathered from a wearable device and a smart scale, and using Fuzzy Logic, an Artificial Intelligence subset, to give new insights about patient well-being. The designed CDSS Platform was able to integrate commercially used smart device, with minimal human intervention required. Also, the data gathered from those devices was used to create a continuous monitoring system, associated with visualization tools that enhanced the clinician knowledge of the patient. Furthermore, an indicator denominated as Patient Progression Indicator was developed with the use of the Fuzzy Logic algorithm, that provides an indirect but objective measurement of the patient well-being. Although the results seem promising, more in-depth research is required such as a trial study capable of validating the results obtained.O cancro é umas das maiores causas de morte no mundo e sobreviver ao seu tratamento não significa que o processo tenha terminado. Vários pacientes que ultrapassaram o processo de tratamento permanecem inseguros em relação à sua saúde, devido ao stress e ansiedade causados pelo medo de reaparecimento do cancro e pelos efeitos do tratamento tais como: problemas de sono, cansaço e problemas de memória, dor, ansiedade e stress. Os pacientes que terminam o tratamento são seguidos periodicamente por clínicos, que avaliam a sua Qualidade de Vida. Esta informação é essencial para compreender o seu estado de saúde, dado que o cancro tem um impacto enorme em todos os aspetos da vida do paciente. No entanto, os clínicos têm à sua disposição poucas ferramentas capazes de mensurar objetivamente a Qualidade de Vida, ou de ferramentas que possibilitem uma maior visualização de dados que proporcione uma melhor tomada de decisão. Portanto, a solução proposta nesta dissertação é a de desenvolver um Sistema de Apoio à Decisão Clínica com ferramentas de visualização capazes de disponibilizar mais informação do paciente, obtidas com o uso de uma pulseira inteligente e uma balança inteligente. Também com o uso de Lógica Difusa, um subconjunto da Inteligência Artificial, proporcionar uma nova informação sobre o estado de saúde do paciente. A plataforma projetada foi capaz de integrar dispositivos inteligentes de uso comercial, de forma a necessitar o mínimo de interação humana. Além disso, os dados adquiridos pelos dispositivos foram usados para criar um sistema de monitorização contínuo, associado a ferramentas de visualização de dados que proporcionam mais informação em relação ao paciente. Mais ainda, foi desenvolvido um indicador designado por Indicador de Progresso do Paciente com a utilização do algoritmo de Lógica Difusa, que providência uma forma indireta, mas objetiva de mensurar o estado de saúde do paciente. Apesar dos resultados parecerem promissores, um estudo mais aprofundado é necessário, tal como um ensaio clínico capaz de validar os resultados obtidos

Tecnologias de Informação e Comunicação na análise contextual da situação-alvo: aprimorando o desenho de cursos de CEAP online em países em desenvolvimento

This paper discusses the benefits of information and communication technologies (ICT) in the contextual target situation analysis at a distance to design online critical English for academic purposes (CEAP) courses. A contextual investigation of the publishing process of high-impact journals in computer science is presented, which is aimed at identifying issues that might influence the publication of Brazilian researchers’ papers. The beliefs and experiences of Brazilian authors in the aforementioned process were analyzed through electronic questionnaires and remote interviews. Moreover, research articles’ reviews were studied to identify the community´s criteria for accepting or rejecting papers. Via ICT, a wider contextual analysis was possible with participants located in different parts of the world. Results provided relevant information about researching and publishing in the field, which may substantiate the design of tasks aimed at improving students’ critical academic literacy. For developing continental countries such as Brazil, saving time and resources through ICT in CEAP courses’ design means the possibility of investing in a more relevant instruction, which better attends to students’ contextual needs and wants. Este trabalho discute os benefícios de Tecnologias de Informação e Comunicação (TIC) na análise contextual a distância da situação-alvo durante a elaboração de um curso online de inglês para fins acadêmicos em sua perspectiva crítica. Essa análise, com foco no processo de publicação acadêmica em periódicos de alto impacto na área de ciência da computação, buscou identificar fatores que possam influenciar o aceite de artigos produzidos por pesquisadores brasileiros. Crenças e a experiência de autores brasileiros foram analisadas no referido processo via questionários eletrônicos e entrevistas remotas. Ademais, pareceres de artigos de pesquisa foram estudados, e os critérios avaliativos dessa comunidade, identificados. Por meio das TICs, uma ampla análise contextual foi possível, contemplando pesquisadores localizados em diversas partes do globo. Os resultados revelam práticas de pesquisa e publicação na área que podem fundamentar o desenho de tarefas voltadas à promoção do letramento acadêmico dos alunos. Em países em desenvolvimento, como o Brasil, economizar tempo e recursos, por meio das TICs, durante o desenho de cursos significa a possibilidade de investimento em um ensino relevante, que melhor atenda às necessidades discentes

Stabilization of Idiosyncratic Fixed Expressions in the Wild.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Keyword Assisted Topic Models

For a long time, many social scientists have conducted content analysis by using their substantive knowledge and manually coding documents. In recent years, however, fully automated content analysis based on probabilistic topic models has become increasingly popular because of their scalability. Unfortunately, applied researchers find that these models often fail to yield topics of their substantive interest by inadvertently creating multiple topics with similar content and combining different themes into a single topic. In this paper, we empirically demonstrate that providing topic models with a small number of keywords can substantially improve their performance. The proposed keyword assisted topic model (keyATM) offers an important advantage that the specification of keywords requires researchers to label topics prior to fitting a model to the data. This contrasts with a widespread practice of post-hoc topic interpretation and adjustments that compromises the objectivity of empirical findings. In our applications, we find that the keyATM provides more interpretable results, has better document classification performance, and is less sensitive to the number of topics than the standard topic models. Finally, we show that the keyATM can also incorporate covariates and model time trends. An open-source software package is available for implementing the proposed methodology

Parenting, Vaccines, and COVID-19: A Machine-Learning Approach

COVID-19 is currently at the forefront of both out-of-school time program providers’ and parents’ minds, with additional policies and procedures added existing operating standards to protect the health of participants, staff, and parents (Environmental Health & Engineering, 2020). A failure to adequately prepare and react to different parenting styles may have both operational and financial implications for out-of-school time programs. These implications are only further exacerbated in the additional context of a global pandemic. While the COVID-19 vaccine is a hope to many that the end of the pandemic is near, parental vaccine hesitancy or refusal may pose a significant hurdle to the safe operation of out-of-school time programs. By exploring the topics of vaccine hesitancy, children, and parents in an online environment, this study offers a closer look into a digital leisure space. In order to better explore the conversations and commentaries occurring on social media about parents, children, vaccines, and COVID-19, web-scraping technologies were employed to aid in a more robust data collection. Due to the nature of web-scraped data as large in size and unruly, a machine learning method was used to analyze the data: Latent Dirichlet Allocation (i.e., LDA), a specific form of topic modelling. After establishing model parameters for the LDA, 25 latent topics were identified from the cleaned dataset (N = 31,925). These 25 topics were subsequently sorted into seven categories: Government, Feelings, School, Public Health, Christmas, Risk & Safety, and Parents & Families. Interpretation of the 25 latent topics was aided by a visualization of the top words most relevant to individual topics, in context to the overall dataset. Representative tweets from each category further identified the range of conversations and commentaries occurring on social media about parents, children, vaccines, and COVID-19. Challenges with research at the cusp of innovation for leisure sciences, as well as implications of practice for out-of-school-time professionals, are also discussed

Intervention and revision: Expertise and interaction in text mediation

Many EAL (English as an Additional Language) scholars enlist text mediators’ support when faced with the challenges of writing for international publication. However, the contributions these individuals are able to make in improving scientific manuscripts remains unclear, especially when language professionals such as English teachers do this work. In this article, we explore this topic by examining how three mediators employed their very different expertise and brought different processes to bear on the same discussion section of a medical manuscript written by a novice scholar in China. We find that successfully mediated texts are often the result of an interplay between the mediator’s expertise and the relationship between the participants. Our findings contradict those of previous studies that question the role of English teachers in this process and have the potential to inform both text mediation practices and revision studies