Objective: As available data increases, so does the opportunity to develop risk scores on more refined patient populations. In this paper we assessed the ability to derive a risk score for a patient no-showing to a clinic visit. Methods: Using data from 2 264 235 outpatient appointments we assessed the performance of models built across 14 different specialties and 55 clinics. We used regularized logistic regression models to fit and assess models built on the health system, specialty, and clinic levels. We evaluated fits based on their discrimination and calibration. Results: Overall, the results suggest that a relatively robust risk score for patient no-shows could be derived with an average C-statistic of 0.83 across clinic level models and strong calibration. Moreover, the clinic specific models, even with lower training set sizes, often performed better than the more general models. Examination of the individual models showed that risk factors had different degrees of predictability across the different specialties. Implementation of optimal modeling strategies would lead to capturing an additional 4819 no-shows per-year. Conclusion: Overall, this work highlights both the opportunity for and the importance of leveraging the available electronic health record data to develop more refined risk models.

This paper describes the Worldmapper Project, which makes use of novel visualization techniques to represent a broad variety of social and economic data about the countries of the world. The goal of the project is to use the map projections known as cartograms to depict comparisons and relations between different territories, and its execution raises many interesting design challenges that were not all apparent at the outset. We discuss the approaches taken towards these challenges, some of which may have considerably broad application. We conclude by commenting on the positive initial response to the worldmapper images published on the web, which we believe is due, at least in part, to the particular effectiveness of the cartogram as a tool for communicating quantitative geographic data.

This paper presents simple randomized algorithms for dynamically embedding M-node binary trees in either a butterfly or a hypercube network of N processors. These algorithms are dynamic in the sense that the tree to be embedded may start as one node and grow by dynamically spawning children. The nodes are incrementally embedded as they are spawned. Thus, the algorithm is especially suited for maintaining dynamic tree structures like those in divide-and-conquer and branch-and-bound algorithms. In the embeddings, the paper seeks to optimize the load on the processors of the network, the dilation of the tree edges, and the congestion on the network edges, in order to satisfy the demands of load balancing, process locality, and communication efficiency. The paper begins by presenting a simple level-by-level scheme for dynamically embedding trees in a butterfly network, and by successive modifications. The following results are obtained: 1. An embedding algorithm for the hypercube that achieves dilation 1 and, with high probability, load $O((M /N) + \log N)$. 2. An embedding algorithm for the butterfly that achieves dilation 2 and, with high probability, load $O((M/ N) + \log N)$. 3. An embedding algorithm for the hypercube that achieves dilation $O(1)$ and, with high probability, load $O((M/N) + 1)$ and congestion $O((M/ N) + 1)$. The third embedding simultaneously optimizes load and dilation to within constant factors, optimizing congestion as well when $M = O(N)$. The first two embeddings are also optimal to within constant factors when the tree to be embedded is large (i.e., $M = \Omega (N\log N)$. In addition, this paper proves a lower bound of $\Omega (\sqrt {\log N} )$ dilation for deterministic embedding algorithms that achieve load $O((M/ N) + 1)$, which implies that any embedding algorithm that simultaneously optimizes load and dilation must be randomized.

In this paper, we describe an O(log N) bit-step randomized algorithm for bit-serial message routing on a hypercube.The result is asymptotically optimal, and improves upon the best previously known algorithms by a logarithmic factor.The result also solves the problem of on-line circuit switching in an O(l)-dilated hypercube (i.e., partitioning the edges of a dilated hypercube so as to realize an arbitrary permutation of point-to-point connections between the nodes).Our algorithm is adaptive and we show that this is necessary to achieve the logarithmic speedup.We generalize the Borodin-Hopcroft lower bound on oblivious routing by proving that any oblivious randomized algorithm on a polylogarithmic degree network requires sZ(log' N/log log N) bit steps with high probability for almost all permutations.

Article Dynamic tree embeddings in butterflies and hypercubes Share on Authors: T. Leighton M.I.T. Mathematics Department and Laboratory for Computer Science, Cambridge, MA M.I.T. Mathematics Department and Laboratory for Computer Science, Cambridge, MAView Profile , M. Newman M.I.T. Mathematics Department and Laboratory for Computer Science, Cambridge, MA M.I.T. Mathematics Department and Laboratory for Computer Science, Cambridge, MAView Profile , A. G. Ranade Department of EECS, University of California, Berkeley, CA Department of EECS, University of California, Berkeley, CAView Profile , E. Schwabe M.I.T. Mathematics Department and Laboratory for Computer Science, Cambridge, MA M.I.T. Mathematics Department and Laboratory for Computer Science, Cambridge, MAView Profile Authors Info & Claims SPAA '89: Proceedings of the first annual ACM symposium on Parallel algorithms and architecturesMarch 1989 Pages 224–234https://doi.org/10.1145/72935.72959Online:01 March 1989Publication History 23citation365DownloadsMetricsTotal Citations23Total Downloads365Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

We consider the computational power of a hypercube containing a potentially large number of randomly located faulty components. We describe a randomized algorithm which embeds an N-node hypercube in an N-node hypercube with faulty processors. Provided that the processors of the N-node hypercube are faulty with probability p < 1, and that the faults are independently distributed, we show that with high probability, the faulty hypercube can emulate the fault-free hypercube with only constant slowdown. In other words, an N-node hypercube with faults can simulate T steps of an N-node fault-free hypercube in O(T) steps. The embedding is easy to construct in polylogarithmic time using only local control. We also describe O(log N)-step routing algorithms which ensure the delivery of messages with high probability even when a constant fraction of the nodes and edges have failed. The routing results represent the first adaptive routing algorithms for which an effective theoretical analysis has been achieved.

We consider the computational power of a hypercube containing a potentially large number of randomly located faulty components. In particular, we describe algorithms for embedding an N/2-node hypercube in an N-node hypercube with faulty processors. Provided that the processors of the N-node hypercube are faulty with probability p < 1/2, and that the faults are independently distributed, we show that with high probability, adjacent cells in the N/2-node hypercube are mapped to functioning cells at distance 3 or less apart in the N-node hypercube. The algorithm is deterministic, easy to implement and runs in Ο(log N) steps using only local control. We also describe ways to produce embeddings which allow for low delay simulations, as well as ways to use a faulty hypercube to efficiently simulate a completely functioning hypercube of the same size.