Using data mining techniques to support breast cancer diagnosis

More than ever, there is an increase of the number of decision support methods and computer aided diagnostic systems applied to various areas of medicine. In breast cancer research, many works have been done in order to reduce false-positives when used as a double reading method. In this study, we aimed to present a set of data mining techniques that were applied to approach a decision support system in the area of breast cancer diagnosis. This method is geared to assist clinical practice in identifying mammographic findings such as microcalcifications, masses and even normal tissues, in order to avoid misdiagnosis. In this work a reliable database was used, with 410 images from about 115 patients, containing previous reviews performed by radiologists as microcalcifications, masses and also normal tissue findings. Throughout this work, two feature extraction techniques were used: the gray level co-occurrence matrix and the gray level run length matrix. For classification purposes, we considered various scenarios according to different distinct patterns of injuries and several classifiers in order to distinguish the best performance in each case described. The many classifiers used were Naïve Bayes, Support Vector Machines, k-nearest Neighbors and Decision Trees (J48 and Random Forests). The results in distinguishing mammographic findings revealed great percentages of PPV and very good accuracy values. Furthermore, it also presented other related results of classification of breast density and BI-RADS® scale. The best predictive method found for all tested groups was the Random Forest classifier, and the best performance has been achieved through the distinction of microcalcifications. The conclusions based on the several tested scenarios represent a new perspective in breast cancer diagnosis using data mining techniques.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.

Strategic Bidding for Electricity Markets Negotiation Using Support Vector Machines

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which simulates the electricity markets environment. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from the market context. This paper presents the application of a Support Vector Machines (SVM) based approach to provide decision support to electricity market players. This strategy is tested and validated by being included in ALBidS and then compared with the application of an Artificial Neural Network, originating promising results. The proposed approach is tested and validated using real electricity markets data from MIBEL - Iberian market operator.

Particle Swarm Optimization of Electricity Market Negotiating Players Portfolio

Energy systems worldwide are complex and challenging environments. Multi-agent based simulation platforms are increasing at a high rate, as they show to be a good option to study many issues related to these systems, as well as the involved players at act in this domain. In this scope the authors’ research group has developed a multi-agent system: MASCEM (Multi- Agent System for Competitive Electricity Markets), which performs realistic simulations of the electricity markets. MASCEM is integrated with ALBidS (Adaptive Learning Strategic Bidding System) that works as a decision support system for market players. The ALBidS system allows MASCEM market negotiating players to take the best possible advantages from each market context. However, it is still necessary to adequately optimize the players’ portfolio investment. For this purpose, this paper proposes a market portfolio optimization method, based on particle swarm optimization, which provides the best investment profile for a market player, considering different market opportunities (bilateral negotiation, market sessions, and operation in different markets) and the negotiation context such as the peak and off-peak periods of the day, the type of day (business day, weekend, holiday, etc.) and most important, the renewable based distributed generation forecast. The proposed approach is tested and validated using real electricity markets data from the Iberian operator – MIBEL.

A Clinical Support System Based on Quality of Life Estimation

Quality of life is a concept influenced by social, economic, psychological, spiritual or medical state factors. More specifically, the perceived quality of an individual's daily life is an assessment of their well-being or lack of it. In this context, information technologies may help on the management of services for healthcare of chronic patients such as estimating the patient quality of life and helping the medical staff to take appropriate measures to increase each patient quality of life. This paper describes a Quality of Life estimation system developed using information technologies and the application of data mining algorithms to access the information of clinical data of patients with cancer from Otorhinolaryngology and Head and Neck services of an oncology institution. The system was evaluated with a sample composed of 3013 patients. The results achieved show that there are variables that may be significant predictors for the Quality of Life of the patient: years of smoking (p value 0.049) and size of the tumor (p value < 0.001). In order to assign the variables to the classification of the quality of life the best accuracy was obtained by applying the John Platt's sequential minimal optimization algorithm for training a support vector classifier. In conclusion data mining techniques allow having access to patients additional information helping the physicians to be able to know the quality of life and produce a well-informed clinical decision.

Decision Support for Energy Contracts Negotiation with Game Theory and Adaptive Learning

This paper presents a decision support methodology for electricity market players’ bilateral contract negotiations. The proposed model is based on the application of game theory, using artificial intelligence to enhance decision support method’s adaptive features. This model is integrated in AiD-EM (Adaptive Decision Support for Electricity Markets Negotiations), a multi-agent system that provides electricity market players with strategic behavior capabilities to improve their outcomes from energy contracts’ negotiations. Although a diversity of tools that enable the study and simulation of electricity markets has emerged during the past few years, these are mostly directed to the analysis of market models and power systems’ technical constraints, making them suitable tools to support decisions of market operators and regulators. However, the equally important support of market negotiating players’ decisions is being highly neglected. The proposed model contributes to overcome the existing gap concerning effective and realistic decision support for electricity market negotiating entities. The proposed method is validated by realistic electricity market simulations using real data from the Iberian market operator—MIBEL. Results show that the proposed adaptive decision support features enable electricity market players to improve their outcomes from bilateral contracts’ negotiations.

Design of a case-based reasoner for information security in military organizations

Information security is concerned with the protection of information, which can be stored, processed or transmitted within critical information systems of the organizations, against loss of confidentiality, integrity or availability. Protection measures to prevent these problems result through the implementation of controls at several dimensions: technical, administrative or physical. A vital objective for military organizations is to ensure superiority in contexts of information warfare and competitive intelligence. Therefore, the problem of information security in military organizations has been a topic of intensive work at both national and transnational levels, and extensive conceptual and standardization work is being produced. A current effort is therefore to develop automated decision support systems to assist military decision makers, at different levels in the command chain, to provide suitable control measures that can effectively deal with potential attacks and, at the same time, prevent, detect and contain vulnerabilities targeted at their information systems. The concept and processes of the Case-Based Reasoning (CBR) methodology outstandingly resembles classical military processes and doctrine, in particular the analysis of “lessons learned” and definition of “modes of action”. Therefore, the present paper addresses the modeling and design of a CBR system with two key objectives: to support an effective response in context of information security for military organizations; to allow for scenario planning and analysis for training and auditing processes.

Adoção e difusão de aplicações das tecnologias da informação: iniciativas lançadas no âmbito de processos de implantação de aplicações em organizações – estudo de práticas profissionais e proposta de ferramenta de suporte

Tese de Doutoramento Tecnologias e Sistemas de Informação

Step towards multiplatform framework for supporting pediatric and neonatology care unit decision process

Children are an especially vulnerable population, particularly in respect to drug administration. It is estimated that neonatal and pediatric patients are at least three times more vulnerable to damage due to adverse events and medication errors than adults are. With the development of this framework, it is intended the provision of a Clinical Decision Support System based on a prototype already tested in a real environment. The framework will include features such as preparation of Total Parenteral Nutrition prescriptions, table pediatric and neonatal emergency drugs, medical scales of morbidity and mortality, anthropometry percentiles (weight, length/height, head circumference and BMI), utilities for supporting medical decision on the treatment of neonatal jaundice and anemia and support for technical procedures and other calculators and widespread use tools. The solution in development means an extension of INTCare project. The main goal is to provide an approach to get the functionality at all times of clinical practice and outside the hospital environment for dissemination, education and simulation of hypothetical situations. The aim is also to develop an area for the study and analysis of information and extraction of knowledge from the data collected by the use of the system. This paper presents the architecture, their requirements and functionalities and a SWOT analysis of the solution proposed.

Intelligent decision support to predict patient barotrauma risk in intensive care units

The occurrence of Barotrauma is identified as a major concern for health professionals, since it can be fatal for patients. In order to support the decision process and to predict the risk of occurring barotrauma Data Mining models were induced. Based on this principle, the present study addresses the Data Mining process aiming to provide hourly probability of a patient has Barotrauma. The process of discovering implicit knowledge in data collected from Intensive Care Units patientswas achieved through the standard process Cross Industry Standard Process for Data Mining. With the goal of making predictions according to the classification approach they several DM techniques were selected: Decision Trees, Naive Bayes and Support Vector Machine. The study was focused on identifying the validity and viability to predict a composite variable. To predict the Barotrauma two classes were created: “risk” and “no risk”. Such target come from combining two variables: Plateau Pressure and PCO2. The best models presented a sensitivity between 96.19% and 100%. In terms of accuracy the values varied between 87.5% and 100%. This study and the achieved results demonstrated the feasibility of predicting the risk of a patient having Barotrauma by presenting the probability associated.

Urbanismo participativo e resiliência das comunidades: especificação de uma aplicação

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Application of Fuzzy Variables for Systems of Management Decision Support

The following article describes an approach covering the variety of opinions and uncertainties of estimates within the chosen technique of decision support. Mathematical operations used for assessment of options are traced to operations of working with functions that are used for assessment of possible options of decision-making. Approach proposed could be used within any technique of decision support based on elementary mathematical operations. In this article the above-mentioned approach is described under analytical hierarchy process.

Decision-support tools to manage drug incompatibilities: evaluation by nurses

Introduction Preventing drug incompatibilities has a high impact onthe safety of drug therapy. Although there are no internationalguidelines to manage drug incompatibilities, different decision-supporttools such as handbooks, cross-tables and databases are available.In a previous study, two decision-support tools have been pre-selectedby pharmacists as fitting nurses' needs on the wards1. The objective ofthis study was to have these both tools evaluated by nurses todetermine which would be the most suitable for their daily practice.Materials & Methods Evaluated tools were:1. Cross-table of drug pairs (http://files.chuv.ch/internet-docs/pha/medicaments/pha_phatab_compatibilitessip.pdf)2. Colour-table (a colour for each drug according to the pH: red =acid; blue = basic; yellow = neutral; black = to be infused alone)2Tools were assessed by 48 nurses in 5 units (PICU, adult andgeriatric intensive care, surgery, onco-hematology) using a standardizedform1. The scientific accuracy of the tools was evaluated bydetermining the compatibility of five drugs pairs (rate of correctanswers according to the Trissel's Handbook on Injectable Drugs,chi-square test). Their ergonomics, design, reliability and applicabilitywere estimated using visual analogue scales (VAS 0-10; 0 =null, 10 = excellent). Results are expressed as the median and interquartilerange (IQR) for 25% and 75% (Wilcoxon rank sum test).Results The rate of correct answers was above 90% for both tools(cross-table 96.2% vs colour-table 92.5%, p[0.05).The ergonomics and the applicability were higher for the crosstable[7.1 (IQR25 4.0, IQR75 8.0) vs 5.0 (IQR25 2.7, IQR75 7.0), p =0.025 resp. 8.3 (IQR25 7.4, IQR75 9.2) vs 7.6 (IQR25 5.9, IQR75 8.8)p = 0.047].The design of the colour-table was judged better [4.6 (IQR25 2.9,IQR75 7.1) vs 7.1 (IQR25 5.4, IQR75 8.4) p = 0.002].No difference was observed in terms of reliability [7.3 (IQR25 6.5,IQR75 8.4) vs 6.7 (IQR25 5.0, IQR758.6) p[0.05].The cross-table was globally preferred by 65% of the nurses (27%colour-table, 8% undetermined) and 68% would like to have thisdecision-support tool available for their daily practice.Discussion & Conclusion Both tools showed the same accuracy toassess drug compatibility. In terms of ergonomics and applicabilitythe cross-table was better than the colour-table, and was preferred bythe nurses for their daily practice. The cross-table will be implementedin our hospital as decision-support tool to help nurses tomanage drug incompatibilities.

Cost system design and cost management in the Spanish public sector

Cost systems have been shown to have developed considerably in recent years andactivity-based costing (ABC) has been shown to be a contribution to cost management,particularly in service businesses. The public sector is composed to a very great extentof service functions, yet considerably less has been reported of the use of ABC tosupport cost management in this sector.In Spain, cost systems are essential for city councils as they are obliged to calculate thecost of the services subject to taxation (eg. waste collection, etc). City councils musthave a cost system in place to calculate the cost of services, as they are legally requirednot to profit , from these services.This paper examines the development of systems to support cost management in theSpanish Public Sector. Through semi-structured interviews with 28 subjects within oneCity Council it contains a case study of cost management. The paper contains extractsfrom interviews and a number of factors are identified which contribute to thesuccessful development of the cost management system.Following the case study a number of other City Councils were identified where activity-based techniques had either failed or stalled. Based on the factors identified inthe single case study a further enquiry is reported. The paper includes a summary usingstatistical analysis which draws attention to change management, funding and politicalincentives as factors which had an influence on system success or failure.

Bridge to life: the Lifebridge B2T extracorporeal life support system in an in vitro trial.

Extracorporeal life support systems (ECLS) have become common in cardiothoracic surgery, but are still "Terra Incognita" in other medical fields due to the fact that perfusion units are normally bound to cardiothoracic centres. The Lifebridge B2T is an ECLS that is meant to be used as an easy and fast-track extracorporeal cardiac support to provide short-term perfusion for the transport of a patient to a specialized centre. With the Lifebridge B2T it is now possible to provide extracorporeal bypass for patients in hospitals without a perfusion unit. The Lifebridge B2T was tested on three calves to analyze the handling, performance and security of this system. The Lifebridge B2T safely can be used clinically and can provide full extracorporeal support for patients in cardiac or pulmonary failure. Flows up to 3.9 +/- 0.2l/min were reached, with an inflow pressure of -103 +/- 13mmHg, using a 21Fr. BioMedicus (Medtronic, Minneapolis, MN, USA) venous cannula. The "Plug and Play" philosophy, with semi-automatic priming, integrated check-list, a long battery time of over two hours and instinctively designed user interface, makes this device very interesting for units with high-risk interventions, such as catheterisation labs. If a system is necessary in an emergency unit, the Lifebridge can provide a high security level, even in centres not acquainted with cardiopulmonary bypass.