Pedro Gago

A Metric for Selection of the Most Promising Rules

The process of Knowledge Discovery in Databases pursues the goal of extracting useful knowledge from large amounts of data. It comprises a pre-processing step, application of a data-mining algorithm and post-processing of results. When rule induction is applied for data-mining one must be prepared to deal with the generation of a large number of rules. In these circumstances it is important to have a way of selecting the rules that have the highest predictive power. We propose a metric for selection of the n rules with the highest average distance between them. We defend that applying our metric to select the rules that are more distant improves the system prediction capabilities against other criteria for rule selection. We present an application example and empirical results produced from a synthesized data set on a financial domain.

INTCare : a knowledge discovery based intelligent decision support system for intensive care medicine

This paper introduces the INTCare system, an intelligent information system based on a completely automated Knowledge Discovery process and on the Agents paradigm. The system was designed to work in Hospital Intensive Care Units, supporting the physicians’ decisions by means of prognostic Data Mining models. In particular, these techniques were used to predict organ failure and mortality assessment. The main intention is to change the current reactive behaviour to a pro-active one, enhancing the quality of service. Current applications and experimentations, the functional and structural aspects, and technological options are presented.

Implementing a Pervasive Real-Time Intelligent System for Tracking Critical Events with Intensive Care Patients

Nowadays, it is increasingly important to utilize intelligent systems to support the decision making process DMP in challenging areas such as Intensive Medicine. In Intensive Care Units ICU, some of the biggest challenges relate both to the number and the different types of available data sources. Even though in such a setting the values for some variables are easy to collect, data collection is still performed manually in particular instances. In order to improve the DMP in ICU, a Pervasive Intelligent Decision Support System, called INTCare was deployed in the ICU of Centro Hospitalar do Porto in Portugal. This system altered the way information is collected and presented. Moreover, the tracking system deployed as a specific module of INTCare-Electronic Nursing Record ENR is made accessible anywhere and anytime. The system allows for the calculation of the critical events regarding five variables that are typically monitored in an ICU. Specifically, the INTCare tracking system characterizes a grid that shows the events by type and duration, empowers a warning system to alert the doctors and promotes intuitive graphics that allow care providers to follow the patient care journey. User acceptance was measured through a questionnaire designed in accordance with the Technology Acceptance Model TAM and results of implementing the INTCare tracking system, and its interface are reported.

Learning to verify design solutions from failure knowledge

Learning is an intrinsic product of case-based reasoning. Acquiring new cases is one possible way of learning in a case-based system. These cases comprise mainly success knowledge. The successful cases are essentially used to generate new design solutions. But a case-based system also can make use of failure knowledge. In this paper we present how a case-based system can acquire failure cases for verification of the solution created by success cases. We describe IM-RECIDE, a system that uses case-based reasoning for solving design problems that are imperfectly described and explained. The learning aspect is focused and some of the machine learning dimensions in design are criticized. Experimental results in the domain of room configuration also are presented.

INTCare: On-line knowledge discovery in the intensive care unit

In our work aim to automate the knowledge discovery process. In this paper we present the INTCare system, an intelligent decision support system for intensive care medicine. INTCare is an agent based system that has (autonomous) agents responsible both for data acquisition and model updating thus reducing the need for human intervention. In the present, INTCare is predicting organ failure and probability of in-hospital death. Reliable prediction results facilitate a change from the current reactive behavior to a pro-active one thus enhancing the quality of service. The functional and structural aspects are presented as are some results obtained using data collected from the bedside monitors.

Marketing database knowledge extraction - towards a domain ontology

Ontologies are currently the most prominent computer science research area under development. With this paper we use ontologies at an almost unexplored research area within the marketing discipline, throughout ontological approach to the database marketing. We propose a generic framework supported by ontologies for the knowledge extraction from marketing databases. Therefore this work has two purposes: to integrate ontological approach in Database Marketing and to create domain ontology with a knowledge base that will enhance the entire process at both levels: marketing and knowledge extraction techniques. This research was developed according to two methodological principles, ontology domain double articulation and ontology modularization. At the end of our work we use ontologies to pre-generalize the Database Marketing knowledge through a knowledge base.

A framework proposal for ontologies usage in marketing databases

The knowledge extraction in databases has being known as a long term and interactive project. Nevertheless the complexity and different options for the knowledge achievement here is a research opportunity that could be explored, throughout the ontologies support. This support may be used for knowledge sharing and reuse. This work describes a research of an ontological approach for leveraging the semantic content of ontologies to improve knowledge discovery in marketing databases. Here we analyze how ontologies and knowledge discovery process may interoperate and present our efforts to prose a possible framework for a formal integration.