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Artificial Intelligence Examining ECGs Predicts Irregular Heartbeat, Death Risk - Docwire News

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Artificial intelligence can be used to accurately examine electrocardiogram (ECG) test results, according to the findings of two preliminary studies being presented at the American Heart Association Scientific Sessions 2019 in Philadelphia, PA. In the first study, researchers evaluated 1.1 million ECGs that did indicate atrial fibrillation (AF) from more than 237,000 patients. They used specialized computational hardware to train a deep neutral network to assess 30,000 data points for each respective ECG. The results showed that approximately one in three people received an AF diagnosis within a year. Moreover, the model demonstrated the capacity for long-term prognostic significance as patients predicted to develop AF after one year had a 45% higher hazard rate in developing AF over a follow-up duration of 25-years compared to other patients.


ABBYY Announces Its Agreement to Acquire TimelinePI to Deliver Digital Intelligence for Enterprise Processes

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ABBYY, a global leader in Content IQ technologies and solutions, today announced it has signed an agreement to acquire Philadelphia, Pennsylvania-based TimelinePI. TimelinePI provides a comprehensive process intelligence platform designed to empower users to understand, monitor and optimize any business process. The global process analytics market size is expected to grow to USD 1,421.7 million by 2023 according to Research and Markets. The acquisition of TimelinePI is a strategic investment by ABBYY into the emerging process intelligence market which is critical to truly understanding the impact and effectiveness of business processes and opportunities for productivity gains from digital transformation investments. TimelinePI's vision of combining the most versatile process mining and operational monitoring with cutting-edge, process-centric AI and machine learning will serve as a critical cornerstone to ABBYY's Digital IQ strategy.


Non-negative matrix factorization based on generalized dual divergence

arXiv.org Machine Learning

Nonnegative matrix factorization based on generalized dual divergence Karthik Devarajan Department of Biostatistics & Bioinformatics, Fox Chase Cancer Center, Temple University Health System, Philadelphia, PA karthik.devarajan@fccc.edu Keywords: nonnegative matrix factorization, Kullback-Leibler divergence, dual divergence, EM algorithm, high dimensional data, tensor Abstract A theoretical framework for nonnegative matrix factorization based on generalized dual Kullback-Leibler divergence, which includes members of the exponential family of models, is proposed. A family of algorithms is developed using this framework and its convergence proven using the Expectation-Maximization algorithm. The proposed approach generalizes some existing methods for different noise structures and contrasts with the recently proposed quasi-likelihood approach, thus providing a useful alternative for nonnegative matrix factorizations. A measure to evaluate the goodness-of-fit of the resulting factorization is described.


Finding a Healthier Approach to Managing Medical Data

Communications of the ACM

One of the formidable challenges healthcare providers face is putting medical data to maximum use. Somewhere between the quest to unlock the mysteries of medicine and design better treatments, therapies, and procedures, lies the real world of applying data and protecting patient privacy. "Today, there are many barriers to putting data to work in the most effective way possible," observes Drew Harris, director of health policy and population health at Thomas Jefferson University's College of Population Health in Philadelphia, PA. "The goals of protecting patients and finding answers are frequently at odds." It is a critical issue and one that will define the future of medicine. Medical advances are increasingly dependent on the analysis of enormous datasets--as well as data that extends beyond any one agency or enterprise.


TechVisor - Het vizier op de tech industrie

#artificialintelligence

Date: June 13-14, 2018 Venue: Sonesta Hotel Philadelphia 1800 Market Street Philadelphia, PA 19103 Malaikannan Sankarasubbu is speaking.


Mathematical Foundations for Social Computing

#artificialintelligence

Yiling Chen (yiling@seas.harvard.edu) is Gordon McKay Professor of Computer Science at Harvard University, Cambridge, MA. Arpita Ghosh (arpitaghosh@cornell.edu) is an associate professor of information science at Cornell University, Ithaca, NY. Michael Kearns (mkearns@cis.upenn.edu) is a professor and National Center Chair of Computer and Information Science at the University of Pennsylvania, Philadelphia, PA. Tim Roughgarden (tim@cs.stanford.edu) is an associate professor of CS at Stanford University, Stanford, CA. Jennifer Wortman Vaughan (jenn@microsoft.com) is a senior researcher at Microsoft Research, New York, NY.


Mathematical Foundations for Social Computing

Communications of the ACM

Yiling Chen (yiling@seas.harvard.edu) is Gordon McKay Professor of Computer Science at Harvard University, Cambridge, MA. Arpita Ghosh (arpitaghosh@cornell.edu) is an associate professor of information science at Cornell University, Ithaca, NY. Michael Kearns (mkearns@cis.upenn.edu) is a professor and National Center Chair of Computer and Information Science at the University of Pennsylvania, Philadelphia, PA. Tim Roughgarden (tim@cs.stanford.edu) is an associate professor of CS at Stanford University, Stanford, CA. Jennifer Wortman Vaughan (jenn@microsoft.com) is a senior researcher at Microsoft Research, New York, NY.


SENEX: A CLOS/CLIM APPLICATION FOR MOLECULAR PATHOLOGY Sheldon S. Ball and Vei H. Mah

AAAI Conferences

University of Mississippi and Thomas Jefferson University 2500 North State Street and 130 South 9th Sa'eet, Suite 400 Jackson, MS 39216 and Philadelphia, PA 19107 SENEX is a computer system under development to explore issues related to representation of molecular information, presentation of data, and reasoning with molecular information. It is written entirely in a portable programming environment supported by Common Lisp, the Common Lisp Object System (CLOS), and the Common Lisp Interface Manager (CLIM). SENEX contains information about molecules, molecular events and disease processes, and provides tools for reasoning with and displaying this information in useful ways. Molecular pathology is a discipline characterized by structures of variable complexity, events constrained by a variable number of factors, and incompletely understood phenomena. Representational issues inherent in the domain are complicated by the use of a language with a rigid/inflexible design. However, the CLOS metaobject protocol allows a programmer to adjust the design and implementation of the language to fit an application domain. Thus the first objective of the SENEX project is to exploit this feature of the CLOS metaobject protocol in designing a language tailored to the domain of molecular pathology.


SS95-01-008.pdf

AAAI Conferences

CIS Department, Moore School University of Pennsylvania Philadelphia, PA 19104 mpalmer linc.cis.upenn.edu 1 Introduction We are interested in building a lexicon for interlingual machine translation (MT) and in examining the formal properties of an interlingua as a language in its own right. As such it should be possible to define a lexicalized grammar for the representation of lexical entries and a set of operations over that grammar that can be used to both analyze and generate interlingua representations. The interlingua we discuss in this paper is Le.,dcal Conceptual Structure (LCS) as formulated by Dorr (1993) based on work by Jackendoff (1983, 1990). This is described in the next section, and is followed by the presentation of a grammar for LCS as a representation language. The grammar formalism whose operations we examine with respect to their ability to compose LCS representations is Feature-Based Lexicalized Adjoining Grammar, (FB-LTAG), a version of Tree Adjoining Grammar (TAG)(Joshi et al (1975), Schabes (1990), Vijay-Shanker (1987)), and its description, along with example TAG structures, forms our final section.