Analysis of medical opinions about the nonrealization of autopsies in a Mexican hospital using association rules and bayesian networks

This research identifies the factors influencing the reduction of autopsies in a hospital of Veracruz. The study is based on the application of data mining techniques such as association rules and Bayesian networks in data sets obtained from opinions of physicians. We analyzed, for the exploration and extraction of the knowledge, algorithms like Apriori, FPGrowth, PredictiveApriori, Tertius, J48, NaiveBayes, MultilayerPerceptron, and BayesNet, all of them provided by the API of WEKA. To generate mining models and present the new knowledge in natural language, we also developed a web application. The results presented in this study are those obtained from the best-evaluated algorithms, which have been validated by specialists in the field of patholog

Generating Knowledge in Maintenance from Experience Feedback

Knowledge is nowadays considered as a significant source of performance improvement, but may be difficult to identify, structure, analyse and reuse properly. A possible source of knowledge is in the data and information stored in various modules of industrial information systems, like CMMS (Computerized Maintenance Management Systems) for maintenance. In that context, the main objective of this paper is to propose a framework allowing to manage and generate knowledge from information on past experiences, for improving the decisions related to the maintenance activity. In that purpose, we suggest an original Experience Feedback process dedicated to maintenance, allowing to capitalize on past interventions by i) formalizing the domain knowledge and experiences using a visual knowledge representation formalism with logical foundation (Conceptual Graphs); ii) extracting new knowledge thanks to association rules mining algorithms, using an innovative interactive approach; iii) interpreting and evaluating this new knowledge thanks to the reasoning operations of Conceptual Graphs. The suggested method is illustrated on a case study based on real data dealing with the maintenance of overhead cranes

An intelligent alarm management system for large-scale telecommunication companies

This paper introduces an intelligent system that performs alarm correlation and root cause analysis. The system is designed to operate in large- scale heterogeneous networks from telecommunications operators. The pro- posed architecture includes a rules management module that is based in data mining (to generate the rules) and reinforcement learning (to improve rule se- lection) algorithms. In this work, we focus on the design and development of the rule generation part and test it using a large real-world dataset containing alarms from a Portuguese telecommunications company. The correlation engine achieved promising results, measured by a compression rate of 70% and as- sessed in real-time by experienced network administrator staff

Data mining by means of generalized patterns

The thesis is mainly focused on the study and the application of pattern discovery algorithms that aggregate database knowledge to discover and exploit valuable correlations, hidden in the analyzed data, at different abstraction levels. The aim of the research effort described in this work is two-fold: the discovery of associations, in the form of generalized patterns, from large data collections and the inference of semantic models, i.e., taxonomies and ontologies, suitable for driving the mining proces

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.Comment: 11 PAGE

Maximal frequent sequences applied to drug-drug interaction extraction

A drug-drug interaction (DDI) occurs when the effects of a drug are modified by the presence of other drugs. DDIs can decrease therapeutic benefit or efficacy of treatments and this could have very harmful consequences in the patient's health that could even cause the patient's death. Knowing the interactions between prescribed drugs is of great clinical importance, it is very important to keep databases up-to-date with respect to new DDI. In this thesis we aim to build a system to assist healthcare professionals to be updated about published drug-drug interactions. The goal of this thesis is to study a method based on maximal frequent sequences (MFS) and machine learning techniques in order to automatically detect interactions between drugs in pharmacological and medical literature. With the study of these methods, the IT community will assist healthcare community to update their drug interactions database in a fast and semi-automatic way. In a first solution, we classify pharmacological sentences depending on whether or not they are describing a drug-drug interaction. This would enable to automatically find sentences containing drug-drug interactions. This solution is completely based in maximal frequent sequences (MFS) extracted from a set of test documents. In a second solution based in machine learning, we go further in the search and perform DDI extraction, determining if two specific drugs appearing in a sentence interact or not. This can be used as an assisting tool to populate databases with drug-drug interactions. The machine learning classifier is trained with several features i.e., bag of words, word categories, MFS, token and char level features and drug level features. The classifier we used was a Random Forest. This system was sent to the DDIExtraction 2011 competition and reached the 6th position. Finally, we introduce Maximal Frequent Discriminative Sequences (MFDS), a novel method of sequential pattern discovery that extends the concept of MFS to adapt it to classification tasks.García Blasco, S. (2012). Maximal frequent sequences applied to drug-drug interaction extraction. http://hdl.handle.net/10251/15342Archivo delegad

A perception and manipulation system for collecting rock samples

An important part of a planetary exploration mission is to collect and analyze surface samples. As part of the Carnegie Mellon University Ambler Project, researchers are investigating techniques for collecting samples using a robot arm and a range sensor. The aim of this work is to make the sample collection operation fully autonomous. Described here are the components of the experimental system, including a perception module that extracts objects of interest from range images and produces models of their shapes, and a manipulation module that enables the system to pick up the objects identified by the perception module. The system was tested on a small testbed using natural terrain