What works? Interventions to reduce readmission after hip fracture: A rapid review of systematic reviews

Background: Hip fracture is a common serious injury in older people and reducing readmission after hip fracture is a priority in many healthcare systems. Interventions which significantly reduce readmission after hip fracture have been identified and the aim of this review is to collate and summarise the efficacy of these interventions in one place. Methods: In a rapid review of systematic reviews one reviewer (ELS) searched the Ovid SP version of Medline and the Cochrane Database of Systematic Reviews. Titles and abstracts of 915 articles were reviewed. Nineteen systematic reviews were included. (ELS) used a data extraction sheet to capture data on interventions and their effect on readmission. A second reviewer (RK) verified data extraction in a random sample of four systematic reviews. Results were not meta-analysed. Odds and risk ratios are presented where available. Results: Three interventions significantly reduce readmission in elderly populations after hip fracture: personalised discharge planning, self-care and regional anaesthesia. Three interventions are not conclusively supported by evidence: Oral Nutritional Supplementation, integration of care, and case management. Two interventions do not affect readmission after hip fracture: Enhanced Recovery pathways and comprehensive geriatric assessment. Conclusions: Three interventions are most effective at reducing readmissions in older people: discharge planning, self-care, and regional anaesthesia. Further work is needed to optimise interventions and ensure the most at-risk populations benefit from them, and complete development work on interventions (e.g. interventions to reduce loneliness) and intervention components (e.g. adapting self-care interventions for dementia patients) which have not been fully tested yet.</p

Regret Bounds for Reinforcement Learning with Policy Advice

In some reinforcement learning problems an agent may be provided with a set of input policies, perhaps learned from prior experience or provided by advisors. We present a reinforcement learning with policy advice (RLPA) algorithm which leverages this input set and learns to use the best policy in the set for the reinforcement learning task at hand. We prove that RLPA has a sub-linear regret of \tilde O(\sqrt{T}) relative to the best input policy, and that both this regret and its computational complexity are independent of the size of the state and action space. Our empirical simulations support our theoretical analysis. This suggests RLPA may offer significant advantages in large domains where some prior good policies are provided

New Error Bounds for Approximations from Projected Linear Equations

Joint Technical Report of U.H. and M.I.T. Technical Report C-2008-43 Dept. Computer Science University of Helsinki and LIDS Report 2797 Dept. EECS M.I.T. July 2008; revised July 2009We consider linear fixed point equations and their approximations by projection on a low dimensional subspace. We derive new bounds on the approximation error of the solution, which are expressed in terms of low dimensional matrices and can be computed by simulation. When the fixed point mapping is a contraction, as is typically the case in Markov decision processes (MDP), one of our bounds is always sharper than the standard contraction-based bounds, and another one is often sharper. The former bound is also tight in a worst-case sense. Our bounds also apply to the non-contraction case, including policy evaluation in MDP with nonstandard projections that enhance exploration. There are no error bounds currently available for this case to our knowledge

Actor-Critic Policy Learning in Cooperative Planning

In this paper, we introduce a method for learning and adapting cooperative control strategies in real-time stochastic domains. Our framework is an instance of the intelligent cooperative control architecture (iCCA)[superscript 1]. The agent starts by following the "safe" plan calculated by the planning module and incrementally adapting its policy to maximize the cumulative rewards. Actor-critic and consensus-based bundle algorithm (CBBA) were employed as the building blocks of the iCCA framework. We demonstrate the performance of our approach by simulating limited fuel unmanned aerial vehicles aiming for stochastic targets. In one experiment where the optimal solution can be calculated, the integrated framework boosted the optimality of the solution by an average of %10, when compared to running each of the modules individually, while keeping the computational load within the requirements for real-time implementation.Boeing Scientific Research LaboratoriesUnited States. Air Force Office of Scientific Research (Grant FA9550-08-1-0086

Fermionic Molecular Dynamics for nuclear dynamics and thermodynamics

A new Fermionic Molecular Dynamics (FMD) model based on a Skyrme functional is proposed in this paper. After introducing the basic formalism, some first applications to nuclear structure and nuclear thermodynamics are presentedComment: 5 pages, Proceedings of the French-Japanese Symposium, September 2008. To be published in Int. J. of Mod. Phys.

Experimental analysis of sample-based maps for long-term SLAM

This paper presents a system for long-term SLAM (simultaneous localization and mapping) by mobile service robots and its experimental evaluation in a real dynamic environment. To deal with the stability-plasticity dilemma (the trade-off between adaptation to new patterns and preservation of old patterns), the environment is represented at multiple timescales simultaneously (5 in our experiments). A sample-based representation is proposed, where older memories fade at different rates depending on the timescale, and robust statistics are used to interpret the samples. The dynamics of this representation are analysed in a five week experiment, measuring the relative influence of short- and long-term memories over time, and further demonstrating the robustness of the approach

A platform for electronic commerce with adaptive agents

Market research suggests that organisations, in general, have adifferentiation strategy when approaching Electronic Commerce. Thus, in orderto be useful, agent technology must take into account this market characteristic.When extending its application to the negotiation stage of the shoppingexperience, one should consider a multi-issue approach, from which bothbuyers and sellers can profit. We here present SMACE, a layered platform foragent-mediated Electronic Commerce, supporting multilateral and multi-issuenegotiations. In this system, the negotiation infrastructure through which thesoftware agents interact is independent from their negotiation strategies. Takingadvantage of this concept, the system assists agent construction, allowing usersto focus in the development of their negotiation strategies. In particular, andaccording to experiments here reported, we have implemented a type of agentthat is capable of increasing the performance with its own experience, byadapting to the market conditions, using a specific kind of ReinforcementLearning technique