User modelling for robotic companions using stochastic context-free grammars

Creating models about others is a sophisticated human ability that robotic companions need to develop in order to have successful interactions. This thesis proposes user modelling frameworks to personalise the interaction between a robot and its user and devises novel scenarios where robotic companions may apply these user modelling techniques. We tackle the creation of user models in a hierarchical manner, using a streamlined version of the Hierarchical Attentive Multiple-Models for Execution and Recognition (HAMMER) architecture to detect low-level user actions and taking advantage of Stochastic Context-Free Grammars (SCFGs) to instantiate higher-level models which recognise uncertain and recursive sequences of low-level actions. We discuss a couple of distinct scenarios for robotic companions: a humanoid sidekick for power-wheelchair users and a companion of hospital patients. Next, we address the limitations of the previous scenarios by applying our user modelling techniques and designing two further scenarios that fully take advantage of the user model. These scenarios are: a wheelchair driving tutor which models the user abilities, and the musical collaborator which learns the preferences of its users. The methodology produced interesting results in all scenarios: users preferred the actual robot over a simulator as a wheelchair sidekick. Hospital patients rated positively their interactions with the companion independently of their age. Moreover, most users agreed that the music collaborator had become a better accompanist with our framework. Finally, we observed that users' driving performance improved when the robotic tutor instructed them to repeat a task. As our workforce ages and the care requirements in our society grow, robots will need to play a role in helping us lead better lives. This thesis shows that, through the use of SCFGs, adaptive user models may be generated which then can be used by robots to assist their users.Open Acces

Contemporary use of cefazolin for MSSA infective endocarditis: analysis of a national prospective cohort

Objectives: This study aimed to assess the real use of cefazolin for methicillin-susceptible Staphylococcus aureus (MSSA) infective endocarditis (IE) in the Spanish National Endocarditis Database (GAMES) and to compare it with antistaphylococcal penicillin (ASP). Methods: Prospective cohort study with retrospective analysis of a cohort of MSSA IE treated with cloxacillin and/or cefazolin. Outcomes assessed were relapse; intra-hospital, overall, and endocarditis-related mortality; and adverse events. Risk of renal toxicity with each treatment was evaluated separately. Results: We included 631 IE episodes caused by MSSA treated with cloxacillin and/or cefazolin. Antibiotic treatment was cloxacillin, cefazolin, or both in 537 (85%), 57 (9%), and 37 (6%) episodes, respectively. Patients treated with cefazolin had significantly higher rates of comorbidities (median Charlson Index 7, P <0.01) and previous renal failure (57.9%, P <0.01). Patients treated with cloxacillin presented higher rates of septic shock (25%, P = 0.033) and new-onset or worsening renal failure (47.3%, P = 0.024) with significantly higher rates of in-hospital mortality (38.5%, P = 0.017). One-year IE-related mortality and rate of relapses were similar between treatment groups. None of the treatments were identified as risk or protective factors. Conclusion: Our results suggest that cefazolin is a valuable option for the treatment of MSSA IE, without differences in 1-year mortality or relapses compared with cloxacillin, and might be considered equally effective

Pneumonia treated in the internal medicine department: Focus on healthcare-associated pneumonia

Patients with pneumonia treated in the internal medicine department (IMD) are often at risk of healthcare-associated pneumonia (HCAP). The importance of HCAP is controversial. We invited physicians from 72 IMDs to report on all patients with pneumonia hospitalized in their department during 2weeks (one each in January and June 2010) to compare HCAP with community-acquired pneumonia (CAP) and hospital-acquired pneumonia (HAP). We analysed 1002 episodes of pneumonia: 58.9% were CAP, 30.6% were HCAP and 10.4% were HAP. A comparison between CAP, HCAP and HAP showed that HCAP patients were older (77, 83 and 80.5years; p<0.001), had poorer functional status (Barthel 100, 30 and 65; p<0.001) and had more risk factors for aspiration pneumonia (18, 50 and 34%; p<0.001). The frequency of testing to establish an aetiological diagnosis was lower among HCAP patients (87, 72 and 79; p<0.001), as was adherence to the therapeutic recommendations of guidelines (70, 23 and 56%; p<0.001). In-hospital mortality increased progressively between CAP, HCAP and HAP (8, 19 and 27%; p<0.001). Streptococcus pneumoniae was the main pathogen in CAP and HCAP. Pseudomonas aeruginosa and methicillin-resistant Staphylococcus aureus (MRSA) caused 17 and 12.3% of HCAP. In patients with a confirmed aetiological diagnosis, the independent risk factors for pneumonia due do difficult-to-treat microorganisms (Enterobacteriaceae, P. aeruginosa or MRSA) were HCAP, chronic obstructive pulmonary diseases and higher Port Severity Index. Our data confirm the importance of maintaining high awareness of HCAP among patients treated in IMDs, because of the different aetiologies, therapy requirements and prognosis of this population. © 2011 The Authors. Clinical Microbiology and Infection © 2011 European Society of Clinical Microbiology and Infectious Diseases

MACVIA Clinical Decision Algorithm in Allergic Rhinitis in adolescents and adults

International audienceThe selection of pharmacotherapy for patients with allergic rhinitis depends on several factors, including age, prominent symptoms, symptom severity, control of allergic rhinitis, patient preferences and cost. Allergen exposure and resulting symptoms vary and treatment adjustment is required. Clinical decision support systems (CDSS) may be beneficial for the assessment of disease control. Clinical decision support systems should be based on the best evidence and algorithms to aid patients and health care professionals to jointly determine the treatment and its step-up or step-down strategy depending on AR control. MACVIA-LR (Fighting chronic diseases for active and healthy ageing) one of the reference sites of the European Innovation Partnership on Active and Healthy Ageing, has initiated an allergy sentinel network (MASK: MACVIA-ARIA Sentinel networK). A clinical decision support system is currently being developed to optimize allergic rhinitis control. An algorithm developed by consensus is presented in this paper. This algorithm should be confirmed by appropriate trials