A Fuzzy Approach Model for Uncovering Hidden Latent Semantic Structure in Medical Text Collections

One of the challenges for text analysis in the medical domain including the clinical notes and research papers is analyzing large-scale medical documents. As a consequence, finding relevant documents has become more difficult and previous work has also shown unique problems of medical documents. The themes in documents help to retrieve documents on the same topic with and without a query. One of the popular methods to retrieve information based on discovering the themes in the documents is topic modeling. In this paper we describe a novel approach in topic modeling, FATM, using fuzzy clustering. To assess the value of FATM, we experiment with two text datasets of medical documents. The quantitative evaluation carried out through log-likelihood on held-out data shows that FATM produces superior performance to LDA. This research contributes to the emerging field of understanding the characteristics of the medical documents and how to account for them in text mining.ye

Revisiting the Technology Challenges and Proposing Enhancements in Ambient Assisted Living for the Elderly

Several social and technical trends support the elderly’s desire to live independently in their preferred environment, despite their increasing medical needs, and enhance their quality of life at home. Ambient-assisted living (AAL) has the capabilities to support the elderly and to decrease their dependency on formal or informal caregivers. We provide a review of the technological challenges that were identified as inhibiting factors in the past decade and then present recent technological advances, e.g., cloud computing, machine learning, artificial intelligence, the Internet of Things. We also fill the gap in the current literature in regard to specific AAL solutions and propose fourth-generation AAL technology design. We find that most informal caregivers are family members who are medically untrained and that the use of advanced analytical processes on AAL-generated data could significantly increase symptom identification. We also present the implications and remaining challenges along with recommendations for future research

Modelo de gestión para control de diabetes en el Ecuador utilizando tecnologías móviles.

Hoy en día, las enfermedades crónicas son cada vez más comunes debido a diversos factores, como pueden ser el estilo de vida vertiginoso de algunas ciudades, una pobre alimentación, el cuidado personal y factores medioambientales entre otros. Por ejemplo, la incidencia de la diabetes en la población ha ido en aumento de forma alarmante. La diabetes es una enfermedad que no mata por sí misma, pero detona otros problemas de salud que afectan la calidad de vida del paciente, y en determinados casos provocan una muerte prematura. Las personas con diabetes son muy propensas a desarrollar complicaciones, especialmente si no tienen un tratamiento adecuado de su enfermedad. El costo financiero que emplea el sector salud para controlar dichas complicaciones es muy alto. Las complicaciones de la diabetes generalmente se presentan de forma asintomática. Por otra parte, la gran mayoría de los estudios formales reportados en la literatura se centran en el análisis estadístico de la presencia de la enfermedad y las complicaciones que de ella derivan, como son la gestión de tratamientos, la necesidad de unidades médicas especializadas, los costos de hospitalización entre otros. Este proyecto pretende proporcionar un modelo para mejorar la auto-gestión de la salud por parte de los pacientes en enfermedades crónicas degenerativas como la diabetes. Así, tanto pacientes como centros médicos y hospitales podrán ahorrar costos y molestias en la prevención, monitorización y cuidado de esta enfermedad. Se propone construir un modelo de gestión integrado para la monitorización de distintos parámetros vitales, físicos y mentales como también las actividades físicas y mentales. Por otro lado, se podrán generar recomendaciones específicas al paciente. Además, este modelo permitirá definir los objetivos y acciones correspondientes para cumplir las recomendaciones realizadas

A review of the role of sensors in mobile context-aware recommendation systems

Recommendation systems are specialized in offering suggestions about specific items of different types (e.g., books, movies, restaurants, and hotels) that could be interesting for the user. They have attracted considerable research attention due to their benefits and also their commercial interest. Particularly, in recent years, the concept of context-aware recommendation system has appeared to emphasize the importance of considering the context of the situations in which the user is involved in order to provide more accurate recommendations. The detection of the context requires the use of sensors of different types, which measure different context variables. Despite the relevant role played by sensors in the development of context-aware recommendation systems, sensors and recommendation approaches are two fields usually studied independently. In this paper, we provide a survey on the use of sensors for recommendation systems. Our contribution can be seen from a double perspective. On the one hand, we overview existing techniques used to detect context factors that could be relevant for recommendation. On the other hand, we illustrate the interest of sensors by considering different recommendation use cases and scenarios

Enhanced context-aware framework for individual and crowd condition prediction

Context-aware framework is basic context-aware that utilizes contexts such as user with their individual activities, location and time, which are hidden information derived from smartphone sensors. These data are used to monitor a situation in a crowd scenario. Its application using embedded sensors has the potential to monitor tasks that are practically complicated to access. Inaccuracies observed in the individual activity recognition (IAR) due to faulty accelerometer data and data classification problem have led to its inefficiency when used for prediction. This study developed a solution to this problem by introducing a method of feature extraction and selection, which provides a higher accuracy by selecting only the relevant features and minimizing false negative rate (FNR) of IAR used for crowd condition prediction. The approach used was the enhanced context-aware framework (EHCAF) for the prediction of human movement activities during an emergency. Three new methods to ensure high accuracy and low FNR were introduced. Firstly, an improved statistical-based time-frequency domain (SBTFD) representing and extracting hidden context information from sensor signals with improved accuracy was introduced. Secondly, a feature selection method (FSM) to achieve improved accuracy with statistical-based time-frequency domain (SBTFD) and low false negative rate was used. Finally, a method for individual behaviour estimation (IBE) and crowd condition prediction in which the threshold and crowd density determination (CDD) was developed and used, achieved a low false negative rate. The approach showed that the individual behaviour estimation used the best selected features, flow velocity estimation and direction to determine the disparity value of individual abnormality behaviour in a crowd. These were used for individual and crowd density determination evaluation in terms of inflow, outflow and crowd turbulence during an emergency. Classifiers were used to confirm features ability to differentiate individual activity recognition data class. Experimenting SBTFD with decision tree (J48) classifier produced a maximum of 99:2% accuracy and 3:3% false negative rate. The individual classes were classified based on 7 best features, which produced a reduction in dimension, increased accuracy to 99:1% and had a low false negative rate (FNR) of 2:8%. In conclusion, the enhanced context-aware framework that was developed in this research proved to be a viable solution for individual and crowd condition prediction in our society

Mining Extremes through Fuzzy Clustering

Archetypes are extreme points that synthesize data representing "pure" individual types. Archetypes are assigned by the most discriminating features of data points, and are almost always useful in applications when one is interested in extremes and not on commonalities. Recent applications include talent analysis in sports and science, fraud detection, profiling of users and products in recommendation systems, climate extremes, as well as other machine learning applications. The furthest-sum Archetypal Analysis (FS-AA) (Mørup and Hansen, 2012) and the Fuzzy Clustering with Proportional Membership (FCPM) (Nascimento, 2005) propose distinct models to find clusters with extreme prototypes. Even though the FCPM model does not impose its prototypes to lie in the convex hull of data, it belongs to the framework of data recovery from clustering (Mirkin, 2005), a powerful property for unsupervised cluster analysis. The baseline version of FCPM, FCPM-0, provides central prototypes whereas its smooth version, FCPM-2 provides extreme prototypes as AA archetypes. The comparative study between FS-AA and FCPM algorithms conducted in this dissertation covers the following aspects. First, the analysis of FS-AA on data recovery from clustering using a collection of 100 data sets of diverse dimensionalities, generated with a proper data generator (FCPM-DG) as well as 14 real world data. Second, testing the robustness of the clustering algorithms in the presence of outliers, with the peculiar behaviour of FCPM-0 on removing the proper number of prototypes from data. Third, a collection of five popular fuzzy validation indices are explored on accessing the quality of clustering results. Forth, the algorithms undergo a study to evaluate how different initializations affect their convergence as well as the quality of the clustering partitions. The Iterative Anomalous Pattern (IAP) algorithm allows to improve the convergence of FCPM algorithm as well as to fine-tune the level of resolution to look at clustering results, which is an advantage from FS-AA. Proper visualization functionalities for FS-AA and FCPM support the easy interpretation of the clustering results

Análise de tipos de ontologias nas áreas de ciência da informação e ciência da computação

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro de Ciências da Educação, Programa de Pós-Graduação em Ciência da Informação, Florianópolis, 2014.A emergência de tecnologias que visam complementar a web, associada às problemáticas na busca por novos modelos de recuperação de informação mais eficientes, abriram espaço para estudos que utilizam os benefícios da organização semântica da informação e do conhecimento. Sistemas de Organização do Conhecimento (SOCs) permitem representar um domínio por meio da sistematização dos conceitos e das relações semânticas que se estabelecem entre eles. Entre os tipos desses sistemas conceituais estão as ontologias, utilizadas para representar o conhecimento relativo a um dado domínio do conhecimento. A presente pesquisa tem como objetivo, por meio de uma pesquisa documental, identificar as principais características dos tipos de ontologias. Para tanto, foi empregado, nos procedimentos metodológicos, o método de Análise de Conteúdo de Laurence Bardin. Para a construção do corpus de análise foram utilizadas as bases de dados da Library and Information Science Abstracts (LISA) e da Computer and Information Systems Abstracts. A análise dos resultados permitiu identificar um predomínio significativo nas pesquisas relacionadas às ontologias de domínio, utilizando-a como ferramenta para representação de conceitos e relações que estejam inseridas na visão de mundo desejada. Diferentemente, as ontologias de topo definem os conceitos mais básicos e que sejam extensíveis a outras ações e domínios associados a sua área de abordagem. Os tipos aplicação e tarefa permitem um nível de representação mais específico, alinhado a modelagem de ambientes particulares.Abstract : The emergence of technologies that aim at complementing the internet, associated with the problematics that arise in the search for new models of information retrieval that are more efficient, have made room for studies that make use of the benefits of the semantic organization of information and knowledge. Knowledge Organization Systems (KOS) allow the representation of a domain through the systematization of concepts and semantic relations that have been stablished between them. Among these forms of conceptual systems are the ontologies, utilized in the representation of knowledge relative to a given knowledge domain. The goal of this research, therefore, is to identify the main characteristics of the types of ontologies through documentary research. For that, we have employed in the methodological procedures the Laurence Bardin Content Analysis Method. As for the corpus analysis construction we made use of the databases of the Library and Information Science Abstracts (LISA) and Computer and Information Systems Abstracts. The analysis of the results allowed the identification of a significant predominance of researches related to domain ontologies, they were used as tools for the representation of concepts and relations that are inserted in the desired world view. In contrast, top level ontologies define the most basic concepts that are extendable to other actions and domains associated to its approach area. The application and task types allow a representation that is more specific and alligned with the modeling of particular environments

Hybrid approach for context-aware service discovery in healthcare domain

Context-awareness computing is a research field which often refers to healthcare as an interesting and rich area of application. Context aware computing attains environments monitoring by means of sensors to provide relevant information or services according to the identified context. In particular, wireless ad hoc sensor networks for medical purposes are playing an increasing role within healthcare. Body Sensor Networks (BSN) are being designed for prophylactic and follow-up monitoring of patients in e.g. their homes, during hospitalization, and in emergencies. This work presents an integrated environment aimed at providing personalized healthcare services which appropriately meet the user@?s context. Deploying the semantics embedded in web services and context models is a mandatory step in the automation of service discovery, invocation and composition. Nevertheless, in a context aware domain purely logic-based reasoning on respectively context and services may not be enough. The main idea of this work is related to enrich with qualitative representation of context underling data by means of Fuzzy Logic in order to automatically recognize the context and to consequently find the right set of healthcare services among the available ones. Semantic formalisms (e.g., OWL, OWL-S, etc.) enable the context and services modeling in terms of domain ontology concepts. On the other hand, soft computing techniques support activity of unsupervised context analysis and healthcare semantic service discovery. Goal is to define context-aware system whose quality of retrieved services relies on the acquisition of user context by means of a robust theoretical approach. Moreover, this work defines hybrid architecture which attains a synergy between the agent-based paradigm and the fuzzy modeling. Specifically, the system exploits some task oriented agents in order to achieve context recognition, services matchmaking and brokerage activities