Elements About Exploratory, Knowledge-Based, Hybrid, and Explainable Knowledge Discovery

International audienceKnowledge Discovery in Databases (KDD) and especially pattern mining can be interpreted along several dimensions, namely data, knowledge, problem-solving and interactivity. These dimensions are not disconnected and have a direct impact on the quality, applicability, and efficiency of KDD. Accordingly, we discuss some objectives of KDD based on these dimensions, namely exploration, knowledge orientation, hybridization, and explanation. The data space and the pattern space can be explored in several ways, depending on specific evaluation functions and heuristics, possibly related to domain knowledge. Furthermore, numerical data are complex and supervised numerical machine learning methods are usually the best candidates for efficiently mining such data. However, the work and output of numerical methods are most of the time hard to understand, while symbolic methods are usually more intelligible. This calls for hybridization, combining numerical and symbolic mining methods to improve the applicability and interpretability of KDD. Moreover, suitable explanations about the operating models and possible subsequent decisions should complete KDD, and this is far from being the case at the moment. For illustrating these dimensions and objectives, we analyze a concrete case about the mining of biological data, where we characterize these dimensions and their connections. We also discuss dimensions and objectives in the framework of Formal Concept Analysis and we draw some perspectives for future research

Context Classifier for Service Robots

Part 7: Perception and Signal ProcessingInternational audienceIn this paper a context classifier for service robots is presented. Independently of the application, service robots need to have the notion of their context in order to behave appropriately. A context classification architecture that can be integrated in service robots reliability calculation is proposed. Sensorial information is used as input. This information is then fused (using Fuzzy Sets) in order to create a knowledge base that is used as an input to the classifier. The classification technique used is Bayes Networks, as the object of classification is partially observable, stochastic and has a sequential activity. Although the results presented refer to indoor/outdoor classification, the architecture is scalable in order to be used in much wider and detailed context classification. A community of service robots, contributing with their own contextual experience to dynamically improve the classification architecture, can use cloud-based technologies

Ubiquitous healthcare systems and medical rules in COPD Domain

Chronic Obstructive Pulmonary Disease (COPD) is a severe lung illness that causes a progressive deterioration in the function and structure of the respiratory system. Recently, COPD became the fifth cause of mortality and the seventh cause of morbidity in Canada. The advancement of context-aware technology creates a new and important opportunity to transform the standard shape of healthcare services into a more dynamic and interactive form. This research project design and validates a rule-based ontology-reasoning framework that provides a context-aware system for COPD patients. The originality of the proposed approach consists in its methodology to prove the efficiency of this model in simulated examples of real-life scenarios based on collaborative data analysis, recognized by specialized medical experts

Efficacy and safety of the direct switch to indacaterol/glycopyrronium from salmeterol/fluticasone in non-frequently exacerbating COPD patients: The FLASH randomized controlled trial

WOS: 000450142800014PubMed ID: 30074294Background and objective Combination long-acting beta(2)-agonist/long-acting muscarinic antagonist (LABA/LAMA) has demonstrated superior clinical outcomes over LABA/inhaled corticosteroid (ICS) in chronic obstructive pulmonary disease (COPD) patients; however, data from blinded randomized controlled trials on direct switching from LABA/ICS to LABA/LAMA are lacking. FLASH (Assessment of switching salmeterol/Fluticasone to indacateroL/glycopyrronium in A Symptomatic COPD patient coHort) investigated if direct switch, without a washout period, from salmeterol/fluticasone (SFC) to indacaterol/glycopyrronium (IND/GLY) in COPD patients improves lung function and is well tolerated. Methods In this 12-week, multicentre, double-blind study, patients with moderate-to-severe COPD and up to one exacerbation in previous year, receiving SFC for >= 3 months, were randomized to continue SFC 50/500 mu g twice daily (bd) or switch to IND/GLY 110/50 mu g once daily (od). Primary endpoint was pre-dose trough forced expiratory volume in 1 s (FEV1) at Week 12. Results In total, 502 patients were randomized (1:1) to IND/GLY or SFC. Patients switched to IND/GLY demonstrated superior lung function (pre-dose trough FEV1) versus SFC at Week 12 (treatment difference (Delta) = 45 mL; P = 0.028). IND/GLY provided significant improvements in pre-dose trough forced vital capacity (FVC; Delta = 102 mL; P = 0.002) and numerical improvements in transition dyspnoea index (TDI; Delta = 0.46; P = 0.063). Rescue medication use and COPD assessment test (CAT) scores were comparable between groups. Both treatments had similar safety profiles. Conclusion FLASH demonstrated that a direct switch to IND/GLY from SFC improved pre-dose FEV1 and FVC in COPD patients with up to one exacerbation in the previous year. No new safety signals were identified.NovartisNovartis; Good Publication Practice (GPP3) guidelinesThe authors would like to thank the patients, investigators and staff at participating centres in this study (a full list of principal investigators and centres is provided in Appendix S3 (Supplementary Information)). Authors also thank Peggy Hours-Zesiger (Novartis) for contribution to the conduct of the study. The authors thank Geetika Kainthla (M. Sc) and M. Fahad Haroon (PhD), Novartis, Hyderabad, India, for providing medical writing support/editorial support, which was funded by Novartis, in accordance with Good Publication Practice (GPP3) guidelines

A comparison of propofol-to-BIS post-operative intensive care sedation by means of target controlled infusion, Bayesian-based and predictive control methods: an observational, open-label pilot study

PurposeWe evaluated the feasibility and robustness of three methods for propofol-to-bispectral index (BIS) post-operative intensive care sedation, a manually-adapted target controlled infusion protocol (HUMAN), a computer-controlled predictive control strategy (EPSAC) and a computer-controlled Bayesian rule-based optimized control strategy (BAYES).MethodsThirty-six patients undergoing short lasting sedation following cardiac surgery were included to receive propofol to maintain a BIS between 40 and 60. Robustness of control for all groups was analysed using prediction error and spectrographic analysis.ResultsAlthough similar time courses of measured BIS were obtained in all groups, a higher median propofol effect-site concentration (CePROP) was required in the HUMAN group compared tothe BAYES and EPSACgroups. The time course analysis of the remifentanil effect-site concentration (CeREMI) revealed a significant increase in CeREMI in the EPSAC group compared to BAYES and HUMAN during the case. Although similar bias and divergence in control was found in all groups, larger control inaccuracy was observed in HUMAN versus EPSAC and BAYES. Spectrographic analysis of the system behavior shows that BAYES covers the largest spectrum of frequencies, followed by EPSAC and HUMAN.ConclusionsBoth computer-based control systems are feasible to be used during ICU sedation with overall tighter control than HUMAN and even with lower required CePROP. EPSAC control required higher CeREMI than BAYES or HUMAN to maintain stable control.Clinical trial number: NCT00735631