Improving the transfer experience at intermodal transit stations through innovative dispatch strategies

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2006.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references.This research introduces the concept of a bus hold light system that is based on headways of arriving trains in an effort to improve rail-to-bus transfer connectivity through a simple-to-implement, low-cost dispatching strategy. An analytical model and a simulation model are developed to analyze the impacts of the proposed headway-based hold light system on total passenger wait time and other relevant measures of transfer performance. The application of the two models to the cases of Alewife and Wellington Stations in the Massachusetts Bay Transportation Authority (MBTA) system and to 79 Street Station in the Chicago Transit Authority (CTA) system shows that the headway-based hold light system can produce substantial passenger wait time savings if implemented in an appropriate setting. Throughout these analyses, the sensitivity of the headway-based hold light system to various factors is analyzed, and the results obtained with the headway-based hold light system are compared with those obtained from the application of other bus dispatching strategies, most notably the strategy of holding each departing bus for passengers transferring from the next train arrival.(cont.) Based on the case study results and sensitivity analyses, a set of guidelines for the implementation of headway-based hold light systems is proposed. In .the comparison of the headway-based hold light system and the hold-all-buses strategy, it is shown that the headway-based hold light system is superior when a large number of downstream boardings occur, due to its tendency to avoid holding bus trips with very few transferring passengers but many downstream passengers.by Andrew T. Desautels.S.M

ESICM LIVES 2017 : 30th ESICM Annual Congress. September 23-27, 2017.

INTRODUCTION. Unplanned readmission to intensive care is highly undesirable in that it contributes to increased variance in care, disruption, difficulty in resource allocation and may increase length of stay and mortality particularly if subject to delays. Unlike the ICU admission from the ward, readmission prediction has received relatively little attention, perhaps in part because at the point of ICU discharge, full physiological information is systematically available to the clinician and so it is expected that readmission should be largely due to unpredictable factors. However it may be that there are multidimensional trends that are difficult for the clinician to perceive that may nevertheless be predictive of readmission. OBJECTIVES. We investigated whether machine learning (ML) techniques could be used to improve on the simple published SWIFT score [1] for the prediction of unplanned readmission to ICU within 48 hours. METHODS. We extracted systolic BP, pulse pressure, heart and respiration rate, temperature, SpO2, bilirubin, creatinine, INR, lactate, white cell count, platelet count, pH, FiO2, and total Glasgow Coma Score from ICU stays of over 2000 adult patients from our hospital electronic patient record system. We trained our own custom multidimensional / time-sensitive algorithmic ML system to predict failed discharges defined as either readmission or unexpected death within 48 hours of discharge. We used 10-fold cross validation to assess performance. We also assessed the effect of augmenting our system by transfer learning (TL) with 44,000 additional cases from the MIMIC III database. RESULTS. The SWIFT score performed relatively poorly with an AUROC of around 0.6 which our ML system trained on local data was also able to match. However when augmented with an additional dataset by TL, the AUROC for the ML system improved statistically and clinically significantly to over 0.7. CONCLUSIONS. Machine learning is able to improve on predictors based on simple multiple logistic regression. Thus there is likely to be information in the trends and in combinations of variables. A disadvantage with this technique is that ML approaches require large amounts of data for training. However, ML approaches can be improved by TL. Basing prediction models on locally derived data augmented by TL is a potentially novel approach to generating tools that customised to the institution yet can exploit the potential power of ML algorithms. REFERENCES [1] Gajic O, Malinchoc M, Comfere TB, et al. The Stability and Workload Index for Transfer score predicts unplanned intensive care unit patient readmission: initial development and validation. Crit Care Med. 2008;36(3):676–82. Grant Acknowledgement This work was internally funded

Discovering Valuable Items from Massive Data

Suppose there is a large collection of items, each with an associated cost and an inherent utility that is revealed only once we commit to selecting it. Given a budget on the cumulative cost of the selected items, how can we pick a subset of maximal value? This task generalizes several important problems such as multi-arm bandits, active search and the knapsack problem. We present an algorithm, GP-Select, which utilizes prior knowledge about similarity be- tween items, expressed as a kernel function. GP-Select uses Gaussian process prediction to balance exploration (estimating the unknown value of items) and exploitation (selecting items of high value). We extend GP-Select to be able to discover sets that simultaneously have high utility and are diverse. Our preference for diversity can be specified as an arbitrary monotone submodular function that quantifies the diminishing returns obtained when selecting similar items. Furthermore, we exploit the structure of the model updates to achieve an order of magnitude (up to 40X) speedup in our experiments without resorting to approximations. We provide strong guarantees on the performance of GP-Select and apply it to three real-world case studies of industrial relevance: (1) Refreshing a repository of prices in a Global Distribution System for the travel industry, (2) Identifying diverse, binding-affine peptides in a vaccine de- sign task and (3) Maximizing clicks in a web-scale recommender system by recommending items to users

Comparison of bioinspired algorithms applied to cancer database

Cancer is not just a disease; it is a set of diseases. Breast cancer is the second most common cancer worldwide after lung cancer, and it represents the most frequent cause of cancer death in women (Thurtle et al. in: PLoS Med 16(3):e1002758, 2019, 1]). If it is diagnosed at an early age, the chances of survival are greater. The objective of this research is to compare the performance of method predictions: (i) Logistic Regression, (ii) K-Nearest Neighbor, (iii) K-means, (iv) Random Forest, (v) Support Vector Machine, (vi) Linear Discriminant Analysis, (vii) Gaussian Naive Bayes, and (viii) Multilayer Perceptron within a cancer database

Prediction of early unplanned intensive care unit readmission in a UK tertiary-care hospital: A cross-sectional machine learning approach

Objectives: Unplanned readmissions to the intensive care unit (ICU) are highly undesirable, increasing variance in care, making resource planning difficult, and potentially increasing length of stay and mortality in some settings. Identifying patients who are likely to suffer unplanned ICU readmission could reduce the frequency of this adverse event. Setting: A single academic, tertiary care hospital in the United Kingdom. Participants: A set of 3,326 ICU episodes collected between October 2014 and August 2016. All records were of patients who visited an ICU at some point during their stay. We excluded patients who: were ≤ 16 years of age; visited intensive care units other than the general and neurosciences ICU; were missing crucial electronic patient record measurements; or had indeterminate ICU discharge outcomes or very early or extremely late discharge times. After exclusion, 2,018 outcome-labeled episodes remained. Primary and Secondary Outcome Measures: Area under the receiver operating characteristic curve (AUROC) for prediction of unplanned ICU readmission or in-hospital death within 48 hours of first ICU discharge. Results: In ten-fold cross-validation, an ensemble predictor was trained on data from both the target hospital and the MIMIC-III database and tested on the target hospital’s data. This predictor discriminated between patients with the unplanned ICU readmission or death outcome and those without this outcome, attaining mean AUROC of 0.7095 (SE 0.0260), superior to the purpose-built SWIFT score (AUROC = 0.6082, SE 0.0249; p = 0.014, pairwise t-test). Conclusions: Despite the inherent difficulties, we demonstrate that a novel ML algorithm based on transfer learning could achieve good discrimination, over and above that of the treating clinicians or the value added by the SWIFT score. Accurate prediction of unplanned readmission could be used to target resources more efficiently.esearch reported in this publication was supported by the National Institute of Nursing Research, of the National Institutes of Health, under award number R43NR015945

ϵ-shotgun: ϵ-greedy batch bayesian optimisation

Bayesian optimisation is a popular surrogate model-based approach for optimising expensive black-box functions. Given a surrogate model, the next location to expensively evaluate is chosen via maximisation of a cheap-to-query acquisition function. We present an ϵ-greedy procedure for Bayesian optimisation in batch settings in which the black-box function can be evaluated multiple times in parallel. Our ϵ-shotgun algorithm leverages the model's prediction, uncertainty, and the approximated rate of change of the landscape to determine the spread of batch solutions to be distributed around a putative location. The initial target location is selected either in an exploitative fashion on the mean prediction, or - with probability ϵ - from elsewhere in the design space. This results in locations that are more densely sampled in regions where the function is changing rapidly and in locations predicted to be good (i.e. close to predicted optima), with more scattered samples in regions where the function is flatter and/or of poorer quality. We empirically evaluate the ϵ-shotgun methods on a range of synthetic functions and two real-world problems, finding that they perform at least as well as state-of-the-art batch methods and in many cases exceed their performance

Management of patients with biliary sphincter of Oddi disorder without sphincter of Oddi manometry

<p>Abstract</p> <p>Background</p> <p>The paucity of controlled data for the treatment of most biliary sphincter of Oddi disorder (SOD) types and the incomplete response to therapy seen in clinical practice and several trials has generated controversy as to the best course of management of these patients. In this observational study we aimed to assess the outcome of patients with biliary SOD managed without sphincter of Oddi manometry.</p> <p>Methods</p> <p>Fifty-nine patients with biliary SOD (14% type I, 51% type II, 35% type III) were prospectively enrolled. All patients with a dilated common bile duct were offered endoscopic retrograde cholangiopancreatography (ERCP) and sphincterotomy whereas all others were offered medical treatment alone. Patients were followed up for a median of 15 months and were assessed clinically for response to treatment.</p> <p>Results</p> <p>At follow-up 15.3% of patients reported complete symptom resolution, 59.3% improvement, 22% unchanged symptoms, and 3.4% deterioration. Fifty-one percent experienced symptom resolution/improvement on medical treatment only, 12% after sphincterotomy, and 10% after both medical treatment/sphincterotomy. Twenty percent experienced at least one recurrence of symptoms after initial response to medical and/or endoscopic treatment. Fifty ERCP procedures were performed in 24 patients with an 18% complication rate (16% post-ERCP pancreatitis). The majority of complications occurred in the first ERCP these patients had. Most complications were mild and treated conservatively. Age, gender, comorbidity, SOD type, dilated common bile duct, presence of intact gallbladder, or opiate use were not related to the effect of treatment at the end of follow-up (p > 0.05 for all).</p> <p>Conclusions</p> <p>Patients with biliary SOD may be managed with a combination of endoscopic sphincterotomy (performed in those with dilated common bile duct) and medical therapy without manometry. The results of this approach with regards to symptomatic relief and ERCP complication rate are comparable to those previously published in the literature in cohorts of patients assessed by manometry.</p