scholarly journals Controlling transient gas flow in real-world pipeline intersection areas

2020 ◽  
Author(s):  
Felix Hennings ◽  
Lovis Anderson ◽  
Kai Hoppmann-Baum ◽  
Mark Turner ◽  
Thorsten Koch
Keyword(s):  
Real World ◽  
Gas Flow ◽  
Mixed Integer ◽  
Real World Data ◽  
Operation Modes ◽  
Intersection Area ◽  
Demand Scenario ◽  
Changes Over Time ◽  
Ongoing Project ◽  
Over Time

Abstract Compressor stations are the heart of every high-pressure gas transport network. Located at intersection areas of the network, they are contained in huge complex plants, where they are in combination with valves and regulators responsible for routing and pushing the gas through the network. Due to their complexity and lack of data compressor stations are usually dealt with in the scientific literature in a highly simplified and idealized manner. As part of an ongoing project with one of Germany’s largest transmission system operators to develop a decision support system for their dispatching center, we investigated how to automatize the control of compressor stations. Each station has to be in a particular configuration, leading in combination with the other nearby elements to a discrete set of up to 2000 possible feasible operation modes in the intersection area. Since the desired performance of the station changes over time, the configuration of the station has to adapt. Our goal is to minimize the necessary changes in the overall operation modes and related elements over time while fulfilling a preset performance envelope or demand scenario. This article describes the chosen model and the implemented mixed-integer programming based algorithms to tackle this challenge. By presenting extensive computational results on real-world data, we demonstrate the performance of our approach.

scholarly journals Concept Drift Detection in Dynamic Probabilistic Relational Models

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Nils Finke ◽  
Tanya Braun ◽  
Marcel Gehrke ◽  
Ralf Möller
Keyword(s):  
Real World ◽  
Joint Distribution ◽  
Concept Drift ◽  
Probability Distributions ◽  
Real World Data ◽  
Relational Models ◽  
World Data ◽  
Probabilistic Relational Models ◽  
Changes Over Time ◽  
Over Time

Dynamic probabilistic relational models, which are factorized w.r.t. a full joint distribution, are used to cater for uncertainty and for relational and temporal aspects in real-world data. While these models assume the underlying temporal process to be stationary, real-world data often exhibits non-stationary behavior where the full joint distribution changes over time. We propose an approach to account for non-stationary processes w.r.t. to changing probability distributions over time, an effect known as concept drift. We use factorization and compact encoding of relations to efficiently detect drifts towards new probability distributions based on evidence.

Trends in end-of- life (EOL) systemic oncologic treatment in contemporary clinical practice: Insights from real-world data.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 253-253
Author(s):  
Maureen Canavan ◽  
Xiaoliang Wang ◽  
Mustafa Ascha ◽  
Rebecca A. Miksad ◽  
Timothy N Showalter ◽  
...  
Keyword(s):  
Cancer Treatment ◽  
End Of Life ◽  
Real World ◽  
Cost Of Care ◽  
Real World Data ◽  
Logistic Regression Models ◽  
Oncolytic Therapy ◽  
Immune Therapies ◽  
Using Data ◽  
Over Time

253 Background: Among patients with cancer, receipt of systemic oncolytic therapy near the end-of-life (EOL) does not improve outcomes and worsens patient and caregiver experience. Accordingly, the ASCO/NQF measure, Proportion Receiving Chemotherapy in the Last 14 Days of Life, was published in 2012. Over the last decade there has been exponential growth in high cost targeted and immune therapies which may be perceived as less toxic than traditional chemotherapy. In this study, we identified rates and types of EOL systemic therapy in today’s real-world practice; these can serve as benchmarks for cancer care organizations to drive improvement efforts. Methods: Using data from the nationwide Flatiron Health electronic health record (EHR)-derived de-identified database we included patients who died during 2015 through 2019, were diagnosed after 2011, and who had documented cancer treatment. We identified the use of aggressive EOL systemic treatment (including, chemotherapy, immunotherapy, and combinations thereof) at both 30 days and 14 days prior to death. We estimated standardized EOL rates using mixed-level logistic regression models adjusting for patient and practice-level factors. Year-specific adjusted rates were estimated in annualized stratified analysis. Results: We included 57,127 patients, 38% of whom had documentation of having received any type of systemic cancer treatment within 30 days of death (SD: 5%; range: 25% - 56%), and 17% within 14 days of death (SD: 3%; range: 10% - 30%). Chemotherapy alone was the most common EOL treatment received (18% at 30 days, 8% at 14 days), followed by immunotherapy (± other treatment) (11% at 30 days, 4% at 14 days). Overall rates of EOL treatment did not change over the study period: treatment within 30 days (39% in 2015 to 37% in 2019) and within 14 days (17% in 2015 to 17% in 2019) of death. However, the rates of chemotherapy alone within 30 days of death decreased from 24% to 14%, and within 14 days, from 10% to 6% during the study period. In comparison, rates for immunotherapy with chemotherapy (0%-6% for 30 days, 0% -2% for 14 days), and immunotherapy alone or with other treatment types (4%-13% for 30 days, 1%-4% for 14 days) increased over time for both 30 and 14 days. Conclusions: End of life systemic cancer treatment rates have not substantively changed over time despite national efforts and expert guidance. While rates of traditional chemotherapy have decreased, rates of costly immunotherapy and targeted therapy have increased, which has been associated with higher total cost of care and overall healthcare utilization. Future work should examine the drivers of end-of-life care in the era of immune-oncology.

scholarly journals Optimising Daily Fantasy Sports Teams with Artificial Intelligence

2020 ◽  
Vol 19 (2) ◽  
pp. 21-35
Author(s):  
Ryan Beal ◽  
Timothy J. Norman ◽  
Sarvapali D. Ramchurn
Keyword(s):  
Real World ◽  
National Football League ◽  
Mixed Integer ◽  
Data Sets ◽  
Fantasy Sports ◽  
Real World Data ◽  
Sports Teams ◽  
Novel Approach ◽  
Four Seasons ◽  
Daily Fantasy Sports

AbstractThis paper outlines a novel approach to optimising teams for Daily Fantasy Sports (DFS) contests. To this end, we propose a number of new models and algorithms to solve the team formation problems posed by DFS. Specifically, we focus on the National Football League (NFL) and predict the performance of real-world players to form the optimal fantasy team using mixed-integer programming. We test our solutions using real-world data-sets from across four seasons (2014-2017). We highlight the advantage that can be gained from using our machine-based methods and show that our solutions outperform existing benchmarks, turning a profit in up to 81.3% of DFS game-weeks over a season.

scholarly journals Lifelong Learning with a Changing Action Set

2020 ◽  
Vol 34 (04) ◽  
pp. 3373-3380
Author(s):  
Yash Chandak ◽  
Georgios Theocharous ◽  
Chris Nota ◽  
Philip Thomas
Keyword(s):  
Lifelong Learning ◽  
Real World ◽  
Large Scale ◽  
Learning Problems ◽  
Sequential Decision Making ◽  
Sequential Decision ◽  
Transition Dynamics ◽  
Unknown Structure ◽  
Changes Over Time ◽  
Over Time

In many real-world sequential decision making problems, the number of available actions (decisions) can vary over time. While problems like catastrophic forgetting, changing transition dynamics, changing rewards functions, etc. have been well-studied in the lifelong learning literature, the setting where the size of the action set changes remains unaddressed. In this paper, we present first steps towards developing an algorithm that autonomously adapts to an action set whose size changes over time. To tackle this open problem, we break it into two problems that can be solved iteratively: inferring the underlying, unknown, structure in the space of actions and optimizing a policy that leverages this structure. We demonstrate the efficiency of this approach on large-scale real-world lifelong learning problems.

scholarly journals Mobile Access and Adoption of the Swedish National Patient Portal

2020 ◽  
Author(s):  
Maria Hägglund ◽  
Charlotte Blease ◽  
Isabella Scandurra
Keyword(s):  
Mobile Phone ◽  
Real World ◽  
Health Record ◽  
Patient Portal ◽  
Patient Portals ◽  
Real World Data ◽  
Mobile Access ◽  
National Patient ◽  
Administrative Tasks ◽  
Over Time

Patient portals are used as a means to facilitate communication, performing administrative tasks, or accessing one’s health record. In a retrospective analysis of real-world data from the Swedish National Patient Portal 1177.se, we describe the rate of adoption over time, as well as how patterns of device usage have changed over time. In Jan 2013, 53% of all visits were made from a computer, and 38% from a mobile phone. By June 2020, 77% of all visits were made from a mobile phone and only 20% from a computer. These results underline the importance of designing responsive patient portals that allow patients to use any device without losing functionality or usability.

scholarly journals Improved models for operation modes of complex compressor stations

2021 ◽  
Author(s):  
Benjamin Hiller ◽  
René Saitenmacher ◽  
Tom Walther
Keyword(s):  
Large Scale ◽  
Gas Flow ◽  
Operation Mode ◽  
Search Tree ◽  
Combinatorial Structure ◽  
Mixed Integer ◽  
Refined Model ◽  
Gas Network ◽  
Operation Modes ◽  
Average Running Time

AbstractWe study combinatorial structures in large-scale mixed-integer (nonlinear) programming problems arising in gas network optimization. We propose a preprocessing strategy exploiting the observation that a large part of the combinatorial complexity arises in certain subnetworks. Our approach analyzes these subnetworks and the combinatorial structure of the flows within these subnetworks in order to provide alternative models with a stronger combinatorial structure that can be exploited by off-the-shelve solvers. In particular, we consider the modeling of operation modes for complex compressor stations (i.e., ones with several in- or outlets) in gas networks. We propose a refined model that allows to precompute tighter bounds for each operation mode and a number of model variants based on the refined model exploiting these tighter bounds. We provide a procedure to obtain the refined model from the input data for the original model. This procedure is based on a nontrivial reduction of the graph representing the gas flow through the compressor station in an operation mode. We evaluate our model variants on reference benchmark data, showing that they reduce the average running time between 10% for easy instances and 46% for hard instances. Moreover, for three of four considered networks, the average number of search tree nodes is at least halved, showing the effectivity of our model variants to guide the solver’s search.

REAL-WORLD DATA OVER TIME FOR TRANSCATHETER AND SURGICAL AORTIC VALVE INTERVENTIONS IN QUÉBEC

2021 ◽  
Vol 37 (10) ◽  
pp. S79
Author(s):  
D de Verteuil ◽  
L Azzi ◽  
L Lambert ◽  
B Daneault ◽  
E Dumont ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Real World ◽  
Real World Data ◽  
World Data ◽  
Over Time

scholarly journals Health Economics Evidence in Peer-Reviewed Journals: Increase in Reporting of Real-World Data Over Time in Type 2 Diabetes

2017 ◽  
Vol 20 (9) ◽  
pp. A487
Author(s):  
Y Huang ◽  
TE Hartog ◽  
R Vaghjiani ◽  
N Patterson ◽  
H Van Lier ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Health Economics ◽  
Real World ◽  
Real World Data ◽  
World Data ◽  
Over Time

scholarly journals A91 ECONOMIC EVALUATION OF A FECAL MICROBIOTA TRANSPLANTATION PROGRAM FOR RECURRENT CLOSTRIDIOIDES DIFFICILE INFECTION IN ALBERTA

2021 ◽  
Vol 4 (Supplement_1) ◽  
pp. 63-65
Author(s):  
D Y Yang ◽  
T Mullie ◽  
H Sun ◽  
L Russell ◽  
B Roach ◽  
...  
Keyword(s):  
Healthcare Cost ◽  
Real World ◽  
Fecal Microbiota Transplantation ◽  
Fecal Microbiota ◽  
Control Group ◽  
Real World Data ◽  
World Data ◽  
The Cost ◽  
The Impact ◽  
Over Time

Abstract Background Fecal microbiota transplantation (FMT) is the most effective therapy for recurrent C. difficile infection. Although studies using statistical modeling have shown FMT to be cost-effective, real-world data is lacking. Aims To assess the impact of FMT program on the healthcare cost of recurrent C. difficile infections using real-world data from Alberta’s public healthcare system. Methods C. difficile infection patients were identified through provincial laboratory database with positive C. difficile results in Edmonton, Alberta between 2009–16. If an initial positive test was followed by ≧2 positive tests within 183 days, an individual was categorized as recurrent C. difficile infection (RCDI). Otherwise, non-recurrent C. difficile infection (non-RCDI) was assigned. Since the Edmonton FMT program was established in 2013, patients were further divided into pre-FMT (2009–12) and post-FMT (2013–16) eras. This divided patients into four study groups as outlined in Table 1. Administrative data, including inpatient stays, ambulatory or emergency room visits, outpatient prescriptions, and physician billings, were extracted. A cost of $389 was assigned to each FMT procedure to account for cost of donor screening and sample preparation. A difference in differences (DID) approach, a tool which estimates the effect of a treatment by comparing outcome difference between treatment group and control group over time, was used to analyze the impact of FMT program on the cost of RCDI. Non-RCDI patients were used as control group to account for changes in treatment costs over time. Ordinary least squares regression, with log-transformed healthcare cost as the dependent variable, was used for the analysis. Results 4717 non-RCDI and 548 RCDI patients were identified and divided into the 4 groups (Table 1). RCDI patients were significantly older than non-RCDI patients (71.13 vs 62.49; P < 0.001). After adjusting for differences in age, sex, and baseline healthcare utilization, cost for RCDI patients were significantly lower relative to costs for non-RCDI patients in the post-FMT era. Cost of non-RCDI increased by $5,300.08 between the pre- and post-FMT eras, while the cost of RCDI decreased by $7,654.92 in the same time frame (Table 2). FMT program was estimated to have saved $12,954 annually for RCDI patients at mean age, sex, and baseline cost of our overall sample. Conclusions Our data suggest that the healthcare cost of RCDI has decreased with the introduction of an FMT program. Funding Agencies Alberta Health Services, University of Alberta Hospital Foundation

scholarly journals Fast Pareto Optimization for Subset Selection with Dynamic Cost Constraints

2021 ◽  
Author(s):  
Chao Bian ◽  
Chao Qian ◽  
Frank Neumann ◽  
Yang Yu
Keyword(s):  
Objective Function ◽  
Greedy Algorithm ◽  
Real World ◽  
Fundamental Problem ◽  
Subset Selection ◽  
Selection Strategy ◽  
Cost Constraints ◽  
Approximation Guarantee ◽  
Changes Over Time ◽  
Over Time

Subset selection with cost constraints is a fundamental problem with various applications such as influence maximization and sensor placement. The goal is to select a subset from a ground set to maximize a monotone objective function such that a monotone cost function is upper bounded by a budget. Previous algorithms with bounded approximation guarantees include the generalized greedy algorithm, POMC and EAMC, all of which can achieve the best known approximation guarantee. In real-world scenarios, the resources often vary, i.e., the budget often changes over time, requiring the algorithms to adapt the solutions quickly. However, when the budget changes dynamically, all these three algorithms either achieve arbitrarily bad approximation guarantees, or require a long running time. In this paper, we propose a new algorithm FPOMC by combining the merits of the generalized greedy algorithm and POMC. That is, FPOMC introduces a greedy selection strategy into POMC. We prove that FPOMC can maintain the best known approximation guarantee efficiently.

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