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Pharmacogenomics-driven Prediction of Antidepressant Treatment Outcomes: A Machine-Learning Approach With Multi-trial Replication

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CLINICAL PHARMACOLOGY & THERAPEUTICS 2019年第4期106卷 855-865页

作者： Athreya, Arjun P. Neavin, Drew Carrillo-Roa, Tania Skime, Michelle Biernacka, Joanna Frye, Mark A. Rush, A. John Wang, Liewei Binder, Elisabeth B. Iyer, Ravishankar K. Weinshilboum, Richard M. Bobo, William V. Univ Illinois Dept Elect & Comp Engn Urbana IL USA Mayo Clin Dept Mol Pharmacol & Expt Therapeut Rochester MN USA Max Planck Inst Psychiat Dept Translat Res Psychiat Munich Germany Mayo Clin Dept Psychiat & Psychol Rochester MN USA Duke Univ Sch Med Dept Med Dept Psychiat & Behav SciDuke Inst Brain Sci Durham NC 27706 USA Texas Tech Univ Hlth Sci Ctr Permian Basin TX USA Duke Natl Univ Singapore Med Sch Singapore Singapore Emory Univ Sch Med Dept Psychiat & Behav Sci Atlanta GA USA Mayo Clin Dept Psychiat & Psychol Jacksonville FL 32224 USA

We set out to determine whether machine learning-based algorithms that included functionally validated pharmacogenomic biomarkers joined with clinical measures could predict selective serotonin reuptake inhibitor (SSRI) remission/response in patients with major depressive disorder (MDD). We studied 1,030 white outpatients with MDD treated with citalopram/escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS;n = 398), Sequenced Treatment Alternatives to Relieve Depression (STAR*D;n = 467), and International SSRI Pharmacogenomics Consortium (ISPC;n = 165) trials. A genomewide association study for PGRN-AMPS plasma metabolites associated with SSRI response (serotonin) and baseline MDD severity (kynurenine) identified single nucleotide polymorphisms (SNPs) in DEFB1, ERICH3, AHR, and TSPAN5 that we tested as predictors. Supervised machine-learning methods trained using SNPs and total baseline depression scores predicted remission and response at 8 weeks with area under the receiver operating curve (AUC) > 0.7 (P < 0.04) in PGRN-AMPS patients, with comparable prediction accuracies > 69% (P <= 0.07) in STAR*D and ISPC. These results demonstrate that machine learning can achieve accurate and, importantly, replicable prediction of SSRI therapy response using total baseline depression severity combined with pharmacogenomic biomarkers.

关键词： Pharmacogenomics citalopram data-driven analytics escitalopram machine learning major depressive disorder selective serotonin reuptake inhibitor (SSRI)

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data-driven Driver Dispatching System with Allocation Constraints and Operational Risk Management for a Ride-Sharing Platform

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DECISION SCIENCES 2020年第6期51卷 1490-1520页

作者： Wang, Tianyu Wu, Desheng Univ Toronto 1265 Mil Trail Toronto ON M1C 1A4 Canada Univ Chinese Acad Sci Econ & Management Sch Beijing 100190 Peoples R China Stockholm Univ Stockholm Business Sch SE-10691 Stockholm Sweden

In this article, we develop and analyze a driver dispatching system for a control center that aims to minimize passengers' waiting time. The system imposes allocation constraints that ensure a minimum number of drivers in different regions to manage operational risk. The data-driven system is based on Rolling Time Horizon approach and utilizes knowledge learned from historical data. It incorporates a hybrid forecasting model and a heuristic algorithm to solve the off-line problem in each iteration. We show that the NP-hardness of the off-line problem lies in allocation constraints. We test the performance of the system with a simulation study based on actual past taxi order data. The result suggests that the system markedly decreases the average waiting time and saves planning time in comparison with the request-driven dispatching mode. The result also demonstrates that in nonextreme cases, the dispatching system finds an acceptable solution which approximately satisfies allocation constraints while guaranteeing a short increase in waiting time.

关键词： data-driven analytics Driver Dispatching System Operational Risk Management and Ride-Sharing

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Smart Trucking - Status of Digital Transformation of the Trucking Industry: A Bibliometric Analysis 7

Smart Trucking - Status of Digital Transformation of the Tru...

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7th CIRP Global Web Conference on Towards Shifted Production Value Stream Patterns through Inference of data, Models, and Technology (CIRPe)

作者： Keepers, Makenzie Wuest, Thorsten West Virginia Univ Ind & Management Syst Engn Morgantown WV 26505 USA

Industry 4.0 and the Smart Manufacturing paradigm are rapidly transforming the way business is conducted in the manufacturing industry. The underlying core technologies of the emerging digital transformation include the Internet of Things (IoT) and data analytics. While the shop floor is the main focus of these paradigms in the narrow sense, it is becoming more prevalent for other companies or business units along the digital supply network to investigate the potential to improve their processes by leveraging these technologies as well. One area that is traditionally reliant on human operators and optimization is the trucking industry. In recent years, this industry has started to explore how the above-mentioned technologies can be utilized to improve their operations, and new policies are further amplifying this process, e.g., by mandating Electronic Logging Device (ELD) to be used by commercial drivers in the USA. This development forces companies to collect digital, high-resolution data and the question emerges how to utilize this data in the most value-adding way. In this paper, we analyze the current state of the art of digital transformation in the trucking industry by conducting a bibliometric study of recently published papers to identify the main research areas and themes in this field, the leading authors, countries, and journals in this domain. This work will provide the basis for further in-depth studies on optimization techniques and available case studies to provide theoretical input for practitioners and scholars. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 7th CIRP Global Web Conference

关键词： data-driven analytics Trucking Logistics IoT Heavy-duty vehicles Industry 4.0 Smart Manufacturing

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AssistantGraph: An Approach for Reusable and Composable data-driven Assistant Components 43

AssistantGraph: An Approach for Reusable and Composable Data...

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43rd IEEE-Computer-Society Annual International Computers, Software and Applications Conference (COMPSAC)

作者： Meurisch, Christian Bayrak, Bekir Muehlhaeuser, Max Tech Univ Darmstadt Telecooperat Lab D-64289 Darmstadt Germany

ISBN: (纸本)9781728126074

Steady progress in ubiquitous technologies and machine learning facilitates ever-new and better digital assistants. However, most of these emerging assistants rely on - partly similar - data-driven analyses that are independent of each other, leading to redundancy issues. In this paper, we propose a novel concept (termed AssistantGraph) for an efficient design and runtime support of digital assistants. More specifically, assistants need to represent their data-driven processing pipelines as a directed acyclic graph of assistant components (modularity) to benefit from serverless computing with data access. Facilitated component sharing across assistants (reusability)leads to a more connected and efficient overall graph: a shared component instance requires to run only once;versioned components are enabled by the proposed backward chains of converters (versioning). We further develop data and control flow mechanisms through recursive filtering on demand to trigger the data-driven components as required. Within a novel proposed and prototypically implemented open assistantinfrastructure,we evaluate our concept in terms of feasibility and performance. The results show reduced redundancy with simultaneous significant performance gains (through component sharing) despite minimal additional overhead (due to modularization and backward compatibility). We believe that our approach gives a new perspective on data-driven assistants and complements an open assistant ecosystem.

关键词： ubiquitous personal assistant data-driven analytics versioning serverless computing operation graph FaaS

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Low-Carbon Transition: How Successful and at What Cost?

Low-Carbon Transition: How Successful and at What Cost?

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作者： Zhou, Xun Aalto University

学位级别：博士

Global climate change is widely recognized as the most severe threat facing humankind. Combating the climate threat depends critically on the transition from unsustainable high-carbon economic growth to sustainable low-carbon development. This dissertation presents three scientific essays that endeavor to provide methodological innovations for evaluating and monitoring the low-carbon transition. The first essay focuses on the evaluation of the marginal abatement costs for greenhouse gases, which is fundamental to effective climate policy. The motivation of this essay stems from the observation that most empirical studies using frontier estimation methods grossly over-estimate the marginal abatement costs due to three sources of upward bias from ignoring input-side abatement options, inefficiency, and noisy data. To address the overestimation issue, we clarify the conceptual distinction between the shadow price and marginal abatement cost and develop a novel data-driven estimation approach based on convex quantile regression. Compared to the traditional approaches, convex quantile regression is more robust to random noise, the choice of the direction vector, and heteroscedasticity. An empirical application to U.S. electric power plants provides empirical evidence and demonstrates the advantages of the proposed approach. The second essay is motivated by a surprising paucity of well-developed studies investigating environmental productivity growth in consumption (a necessary condition for a smooth transition toward low-carbon consumption). This essay seeks to develop an environmental total-factor productivity index specific to the use of consumer durable goods. We first analyze the particular features of consumer durables (taking the passenger car as an example) compared to conventional production units, based on which we elaborate how to model the production activity during the use phase of consumer durables. Then, we present an overview of the existing approaches

关键词： climate change climate policy data-driven analytics environmental performance frontier estimation low-carbon consumption behavior nonparametric regression production theory sustainable transportation

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Smart Trucking - Status of Digital Transformation of the Trucking Industry: A Bibliometric Analysis

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Procedia CIRP 2019年 86卷 26-30页

作者： Makenzie Keepers Thorsten Wuest Industrial and Management Systems Engineering West Virginia University Morgantown WV 26505 USA

Industry 4.0 and the Smart Manufacturing paradigm are rapidly transforming the way business is conducted in the manufacturing industry. The underlying core technologies of the emerging digital transformation include the Internet of Things (IoT) and data analytics. While the shop floor is the main focus of these paradigms in the narrow sense, it is becoming more prevalent for other companies or business units along the digital supply network to investigate the potential to improve their processes by leveraging these technologies as well. One area that is traditionally reliant on human operators and optimization is the trucking industry. In recent years, this industry has started to explore how the above-mentioned technologies can be utilized to improve their operations, and new policies are further amplifying this process, e.g., by mandating Electronic Logging Device (ELD) to be used by commercial drivers in the USA. This development forces companies to collect digital, high-resolution data – and the question emerges how to utilize this data in the most value-adding way. In this paper, we analyze the current state of the art of digital transformation in the trucking industry by conducting a bibliometric study of recently published papers to identify the main research areas and themes in this field, the leading authors, countries, and journals in this domain. This work will provide the basis for further in-depth studies on optimization techniques and available case studies to provide theoretical input for practitioners and scholars.

关键词： data-driven analytics Trucking Logistics IoT Heavy-duty vehicles Industry 4.0 Smart Manufacturing

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Integrating Brain-Behavior data to Identify Clinically Meaningful Biotypes for Depression and Anxiety

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BIOLOGICAL PSYCHIATRY 2018年第9期83卷 S88-S89页

作者： Williams, Leanne Stanford Univ Stanford CA 94305 USA

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Innovation in Big data analytics Applications of Mathematical Programming in Medicine and Healthcare 5

Innovation in Big Data Analytics Applications of Mathematica...

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IEEE International Conference on Big data (IEEE Big data)

作者： Lee, Eva K. Georgia Inst Technol NSF I UCRC Ctr Hlth Org Transformat Ind & Syst En Ctr Operat Res Med & HealthCare Atlanta GA 30332 USA

ISBN: (纸本)9781538627150

Risk and decision models and predictive analytics have long been cornerstones for advancement of business analytics in industrial, government, and military applications. In particular, multi-source data system modeling and big data analytics and technologies play an increasingly important role in modern business enterprise. Many problems arising in these domains can be formulated into mathematical models and can be analyzed using sophisticated optimization, decision analysis, and computational techniques. In this talk, we will share some of our successes in healthcare, defense, and service sector applications through innovation in predictive and big data analytics through the modeling and computational advances in integer programming. Specifically, the first model is a discrete support vector machine predictive model that incorporates comprehensive factors related to demographics and socioeconomic status, clinical and hospital resources, operations and utilization, and patient complaints and risk factors for global prediction of readmission and treatment outcome of patients. The second model describes an outcome-driven personalized treatment planning model for cancer patients.

关键词： data-driven analytics integer programing predictive models classification machine learning discrete support vector machine personalized treatment planning cancer multi-objective optimization

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data-driven fuel consumption estimation: A multivariate adaptive regression spline approach

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2017年第Oct.期83卷 134-145页

作者： Chen, Yuche Zhu, Lei Gonder, Jeffrey Young, Stanley Walkowicz, Kevin Natl Renewable Energy Lab 15013 Denver West Pkwy Golden CO 80401 USA

Providing guidance and information to drivers to help them make fuel-efficient route choices remains an important and effective strategy in the near term to reduce fuel consumption from the transportation sector. One key component in implementing this strategy is a fuel-consumption estimation model. In this paper, we developed a mesoscopic fuel consumption estimation model that can be implemented into an eco-routing system. Our proposed model presents a framework that utilizes large-scale, real-world driving data, clusters road links by free-flow speed and fits one statistical model for each of cluster. This model includes predicting variables that were rarely or never considered before, such as free-flow speed and number of lanes. We applied the model to a real-world driving data set based on a global positioning system travel survey in the Philadelphia-Camden-Trenton metropolitan area. Results from the statistical analyses indicate that the independent variables we chose influence the fuel consumption rates of vehicles. But the magnitude and direction of the influences are dependent on the type of road links, specifically free-flow speeds of links. A statistical diagnostic is conducted to ensure the validity of the models and results. Although the real-world driving data we used to develop statistical relationships are specific to one region, the framework we developed can be easily adjusted and used to explore the fuel consumption relationship in other regions. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： data-driven analytics Fuel consumption estimation Multivariate adaptive regression spline Eco-routing

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Characterizing the Clinical Relevance of Digital Phenotyping data Quality With Applications to a Cohort With Schizophrenia

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BIOLOGICAL PSYCHIATRY 2018年第9期83卷 S61-S61页

作者： Staples, Patrick Torous, John Barnett, Ian Sandoval, Luis Keshavan, Matcheri Onnela, Jukka Pekka Harvard Sch Publ Hlth Boston MA USA Beth Israel Deaconess Med Ctr Boston MA 02215 USA Univ Penn Perelman Sch Med Philadelphia PA 19104 USA Harvard Med Sch Boston MA USA Harvard Univ Cambridge MA 02138 USA

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