Ana Mendonça

Bio: Ana Mendonça is an academic researcher from University of Minho. The author has contributed to research in topics: Business intelligence & Osteosarcoma. The author has an hindex of 1, co-authored 2 publications receiving 5 citations.

Business intelligence for nutrition therapy

Abstract: The assessment of health status in communities throughout the world is a massive information technology challenge. Data warehousing provides a flexible environment to support the business management and serve as an integrated repository for data. With the addition of models and analytic tools that have the potential to provide actionable information resources and support effective problem identification, critical decision-making, and strategy formulation, implementation, and evaluation. Of particular interest are the factors of influence like the patient's height or weight and its impact on processes and results. A multidimensional process is a way to discover health care processes according to certain factors of influence. This study aims to implement a data warehousing environment for decision support, in the context of nutrition evaluation, to integrate data obtained from a health care facility. This paper highlights the implementation of Business Intelligence in health care settings allows searching and interpreting stored information to support decisions concerning people's life.

A Case-Based Reasoning Approach to GBM Evolution

Abstract: GlioBastoma Multiforme (GBM) is an aggressive primary brain tumor characterized by a heterogeneous cell population that is genetically unstable and resistant to chemotherapy. Indeed, despite advances in medicine, patients diagnosed with GBM have a median survival of just one year. Magnetic Resonance Imaging (MRI) is the most widely used imaging technique for determining the location and size of brain tumors. Indisputably, this technique plays a major role in the diagnosis, treatment planning, and prognosis of GBM. Therefore, this study proposes a new Case Based Reasoning approach to problem solving that attempts to predict a patient’s GBM volume after five months of treatment based on features extracted from MR images and patient attributes such as age, gender, and type of treatment.

Machine Learning in Nutritional Follow-up Research

Abstract: Abstract Healthcare is one of the world’s fastest growing industries, having large volumes of data collected on a daily basis. It is generally perceived as being ‘information rich’ yet ‘knowledge poor’. Hidden relationships and valuable knowledge can be discovered in the collected data from the application of data mining techniques. These techniques are being increasingly implemented in healthcare organizations in order to respond to the needs of doctors in their daily decision-making activities. To help the decision-makers to take the best decision it is fundamental to develop a solution able to predict events before their occurrence. The aim of this project was to predict if a patient would need to be followed by a nutrition specialist, by combining a nutritional dataset with data mining classification techniques, using WEKA machine learning tools. The achieved results showed to be very promising, presenting accuracy around 91%, specificity around 97% and precision about 95%.

Formalisation de la démarche de conception d'un système de production mobile : intégration des concepts de mobilité et de reconfigurabilité

Abstract: Dans cette these nous analysons dans quelle mesure le concept de mobilite peut etre pris en compte dans la demarche d'analyse et de conception de systemes de production. Notre apport vise a formaliser la demarche d'analyse et de conception de ce dernier, explicitant les decisions a prendre, les informations necessaires et les criteres de decision a mettre en place. Dans cet objectif, deux niveaux d'analyse ont ete distingues : un niveau local concernant un site de production et un niveau global comprenant un ensemble de sites.Le premier niveau local considere un seul site de production. A ce niveau nous avons propose une approche prenant en compte les caracteristiques du site de production. Dans notre contexte, le choix de la localisation geographique de production est impose par le client. De ce fait, la conception du systeme de production doit s'adapter a cette contrainte. D'un point de vue conception, quatre questions sont abordees : (1) dans quelle mesure le concept de mobilite peut etre integre dans une demarche de conception de systeme de production mobile ? (2) quelles caracteristiques de l'environnement de production doivent etre prises en compte ? (3) comment determiner ce qu'il faut produire sur site ou ce qu'il serait opportun d'externaliser ? et (4) compte tenu des informations obtenues quelle est la meilleure configuration du SPM a envisager et selon quels criteres de choix ? La reponse a ces questions conduit a la proposition d'une configuration du SPM adaptee pour un seul site de production.Le deuxieme niveau global traite la problematique de mobilite successive multi sites. En effet, pour etre rentabilise le systeme de production doit etre mobilise sur plusieurs sites de production. A chaque changement de site de production, une reconfiguration du systeme de production s'impose en se basant sur la configuration existante (version i-1). LaThese de Youssef BENAMAreconfigurabilite concerne d'une part l'architecture interne du systeme (choix des machines, recrutement de nouvelles equipes locales, etc) et d'autre part l'organisation de la chaine d'approvisionnement du SPM (faire en interne ou externaliser, fournisseur local, etc.). A ce niveau global d'analyse, nous proposons deux modeles d'analyse : (1) un premier modele pour l'analyse de la reconfigurabilite interne. Ce modele d'analyse permet d'adapter le nombre de lignes de production et le nombre d'equipes en fonction d'un scenario de demande (localisations geographiques, capacite necessaire par site). L'originalite de notre proposition consiste d'une part en l'evaluation des couts de reconfiguration necessaires et d'autre part l'appreciation du niveau d'adequation de la configuration proposee avec le contexte du site de production via l'utilisation de l'indicateur de mobilite. (2) Le deuxieme modele d'analyse concerne la reconfigurabilite de la chaine d'approvisionnement amont du SPM. Il consiste en une adaptation du modele d'aide a la decision "faire ou faire faire" par l'integration d'un cote de l'importance du site de production et d'un autre cote des specificites de chaque site de production.La demarche d'analyse proposee est illustree sur le cas industriel concernant la conception d'une usine mobile pour la fabrication et l'installation sur site de composants de centrales solaires thermodynamiques.

Predictive Data Mining in Nutrition Therapy

Abstract: The assessment and measurement of health status in communities throughput the world is a massive information technology challenge. Data mining, plays a vital role in health care industry since it really has the potential to generate a knowledge-rich environment that reduces medical errors, decreases costs by increasing efficiency, improves the quality of clinical decisions and significantly enhances patient's outcomes and quality of life. This study falls within the context of nutrition evaluation and its main goal is to apply classification algorithms in order to predict if a patient needs to be followed by a nutrition specialist. One of the tools resorted in this study was the Waikato Environment for Knowledge Analysis (Weka in advance) Workbench since it allows to quickly try out and compare different machine learning solutions. The tasks involved in the development of this project included data preparation, data preprocessing, data transformation and cleaning, application of several classifiers and its respective evaluation through performance measures that include the confusion matrix, accuracy, error rate, and others. The accomplished results showed to be quite optimistic presenting promising values of performance measures. specifically an accuracy around 91 %.

Mobile Caching for Location-Based Services

Abstract: Location-based services (LBS) are services that answer queries based on the locations with which the queries are associate; normally the locations where the queries are issued. With a variety of promising applications, such as local information access (e.g., traffic reports, news, and navigation maps) and nearest neighbor queries (e.g., finding the nearest restaurants) (Barbara, 1999; Ren & Dunham, 2000; D. L. Lee, Lee, Xu, & Zheng, 2002; W. C. Lee, Xu, & Zheng, 2004), LBS is emerging as an integral part of daily life. The greatest potential of LBS is met in a mobile computing environment, where users enjoy unrestricted mobility and ubiquitous information access. For example, a traveler could issue a query like “Find the nearest hotel with a room rate below $100” from a wireless portable device in the middle of a journey. To answer such a query, however, three major challenges have to be overcome:

The development of a business intelligence web application to support the decision-making process regarding absenteeism in the workplace

Abstract: Nowadays, one of the biggest concerns of industries all over the world is situations regarding absenteeism, since it has a great impact on the productivity and economy of companies, as well as on the health of their employees. The major causes of absenteeism appear to be work accidents and sickness leaves, which lead to the attempt by companies of understanding how the workload is related to the health of their collaborators and, consequently, to absenteeism. Thus, this paper proposes the design and development of a Web Application based on Business Intelligence indicators in order to help the health and human resources professionals of a Portuguese company analyse the relation between absenteeism and the health and lifestyle of employees, with the intention of concluding whether the work executed on the company is harming workers’ health. Furthermore, it is intended to discover the principal motives for the numerous and more frequent absences in this company, so that it is possible to decrease the absenteeism rate and, hence, improve the decision-making process. This platform will also provide higher quality healthcare and the possibility to find patterns in the absence of collaborators, as well as reduce time-waste and errors.