Marilena Vendittelli

Publications (3)3.16 Total impact

• Source

  Available from: Giovanni Ulivi

  Article: Construction, training and clinical validation of an interpretation system for genotypic HIV-1 drug resistance based on fuzzy rules revised by virological outcomes.

  Andrea De Luca, Marilena Vendittelli, Francesco Baldini, Simona Di Giambenedetto, Maria Paola Trotta, Antonella Cingolani, Alessandra Bacarelli, Caterina Gori, Carlo Federico Perno, Andrea Antinori, Giovanni Ulivi
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  ABSTRACT: To evaluate whether fuzzy operators can be usefully applied to the interpretation of genotypic HIV-1 drug resistance by experts, and to improve the prediction of salvage therapy outcome by adapting interpretation rules of genotypic resistance on the basis of their association with virological response data. We used a clinical dataset of 231 patients failing highly active antiretroviral therapy (HAART) and starting salvage therapy with baseline resistance genotyping and virological outcomes after 3 and 6 months. A set of rules predicting genotypic resistance was initially derived from an expert (ADL). Rules were implemented using a fuzzy logic approach and the virological outcomes dataset used for the training phase. The resulting algorithm was validated using a separate set of 184 selected patients by correlating the resulting predicted activity with observed virological response at 3 months. For comparison, the expert systems from the drug resistance group of the Agence Nationale de Recherches sur le SIDA (ANRS-AC11) and the algorithm from the Stanford's HIV drug resistance database (Stanford HIVdb) were evaluated on the same set. The starting algorithm had a correlation with virological outcomes of R2=0.06 (P=0.0001). After the training phase the correlation with virological outcomes increased to R2=0.19 (P<0.000001). In the validation set of patients, the activity of the salvage regimen predicted by the fuzzy algorithm was the only variable independently predictive of the 3-month viral load change even after adjusting by the activity predicted by the two expert systems and baseline viral load (for each 10% salvage regimen's activity increase, mean HIV RNA change from baseline: -0.27 log10 copies/ml; 95% CI -0.39, -0.15). Using fuzzy operators in a virological outcomes training database to implement a rules-based algorithm for genotypic resistance interpretation, significant improvements of outcomes prediction were obtained. The resulting algorithm showed an independent predictive capability of virological outcomes over that of two rules-based interpretation algorithms made by experts. Although the system was trained and validated on a limited number of cases, the approach deserves further evaluation.
  Antiviral therapy 08/2004; 9(4):583-93. · 3.16 Impact Factor
• Source

  Available from: Giovanni Ulivi

  Article: Fuzzy Maps: A New Tool for Mobile Robot Perception and Planning

  Giovanni Ulivi, Marilena Vendittelli
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  ABSTRACT: An essential component of an autonomous mobile robot is the heteroceptive sensory system. Sensing capabilities should be integrated with a method for extracting a representation of the environment from uncertain sensor data and with an appropriate planning algorithm. In this paper, fuzzy logic concepts are used to introduce a tool useful for robot perception as well as for planning collision-free motions. In particular, a map of the environment is defined as the fuzzy set of unsafe points, whose membership function quantifies the possibility for each point to belong to an obstacle. The computation of this set is based on a specific sensor model and makes use of intermediate sets generated from range measures and aggregated by means of fuzzy set operators. This general approach is applied to a robot with ultrasonic rangefinders. The resulting map building algorithm performs well, as confirmed by a comparison with stochastic methods. The planning problem on fuzzy maps can be ...
  01/2000;
• Article: WMR Control Via Dynamic Feedback Linearization: Design, Implementation, and Experimental Validation

  Ro De Luca, Marilena Vendittelli
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  ABSTRACT: The subject of this paper is the motion control problem of wheeled mobile robots (WMRs) in environments without obstacles. With reference to the popular unicycle kinematics, it is shown that dynamic feedback linearization is an efficient design tool leading to a solution simultaneously valid for both trajectory tracking and setpoint regulation problems. The implementation of this approach on the laboratory prototype SuperMARIO, a two-wheel differentially driven mobile robot, is described in detail. To assess the quality of the proposed controller, we compare its performance with that of several existing control techniques in a number of experiments. The obtained results provide useful guidelines for WMR control designers.
  02/1970;