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Articles 61 - 65 of 65

Full-Text Articles in Data Science

Data: The Good, The Bad And The Ethical, John D. Kelleher, Filipe Cabral Pinto, Luis M. Cortesao Dec 2020

Data: The Good, The Bad And The Ethical, John D. Kelleher, Filipe Cabral Pinto, Luis M. Cortesao

Articles

It is often the case with new technologies that it is very hard to predict their long-term impacts and as a result, although new technology may be beneficial in the short term, it can still cause problems in the longer term. This is what happened with oil by-products in different areas: the use of plastic as a disposable material did not take into account the hundreds of years necessary for its decomposition and its related long-term environmental damage. Data is said to be the new oil. The message to be conveyed is associated with its intrinsic value. But as in …

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Machine Learning Applications For Drug Repurposing, Hansaim Lim Sep 2020

Machine Learning Applications For Drug Repurposing, Hansaim Lim

Dissertations, Theses, and Capstone Projects

The cost of bringing a drug to market is astounding and the failure rate is intimidating. Drug discovery has been of limited success under the conventional reductionist model of one-drug-one-gene-one-disease paradigm, where a single disease-associated gene is identified and a molecular binder to the specific target is subsequently designed. Under the simplistic paradigm of drug discovery, a drug molecule is assumed to interact only with the intended on-target. However, small molecular drugs often interact with multiple targets, and those off-target interactions are not considered under the conventional paradigm. As a result, drug-induced side effects and adverse reactions are often neglected …

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Subsurface Analytics: Contribution Of Artificial Intelligence And Machine Learning To Reservoir Engineering, Reservoir Modeling, And Reservoir Management, Shahab D. Mohaghegh Apr 2020

Subsurface Analytics: Contribution Of Artificial Intelligence And Machine Learning To Reservoir Engineering, Reservoir Modeling, And Reservoir Management, Shahab D. Mohaghegh

Faculty & Staff Scholarship

Subsurface Analytics is a new technology that changes the way reservoir simulation and modeling is performed. Instead of starting with the construction of mathematical equations to model the physics of the fluid flow through porous media and then modification of the geological models in order to achieve history match, Subsurface Analytics that is a completely AI-based reservoir simulation and modeling technology takes a completely different approach. In AI-based reservoir modeling, field measurements form the foundation of the reservoir model. Using data-driven, pattern recognition technologies; the physics of the fluid flow through porous media is modeled through discovering the best, most …

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Taxonomy Development And Knowledge Representation Of Nurses' Personal Cognitive Artifacts, Sharon Mclane, James P Turley Jan 2009

Taxonomy Development And Knowledge Representation Of Nurses' Personal Cognitive Artifacts, Sharon Mclane, James P Turley

Faculty, Staff and Student Publications

Nurses prepare knowledge representations, or summaries of patient clinical data, each shift. These knowledge representations serve multiple purposes, including support of working memory, workload organization and prioritization, critical thinking, and reflection. This summary is integral to internal knowledge representations, working memory, and decision-making. Study of this nurse knowledge representation resulted in development of a taxonomy of knowledge representations necessary to nursing practice.This paper describes the methods used to elicit the knowledge representations and structures necessary for the work of clinical nurses, described the development of a taxonomy of this knowledge representation, and discusses translation of this methodology to the cognitive …

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Using Citation Data To Improve Retrieval From Medline, Elmer V Bernstam, Jorge R Herskovic, Yindalon Aphinyanaphongs, Constantin F Aliferis, Madurai G Sriram, William R Hersh Jan 2006

Using Citation Data To Improve Retrieval From Medline, Elmer V Bernstam, Jorge R Herskovic, Yindalon Aphinyanaphongs, Constantin F Aliferis, Madurai G Sriram, William R Hersh

Faculty, Staff and Student Publications

OBJECTIVE: To determine whether algorithms developed for the World Wide Web can be applied to the biomedical literature in order to identify articles that are important as well as relevant. DESIGN AND MEASUREMENTS A direct comparison of eight algorithms: simple PubMed queries, clinical queries (sensitive and specific versions), vector cosine comparison, citation count, journal impact factor, PageRank, and machine learning based on polynomial support vector machines. The objective was to prioritize important articles, defined as being included in a pre-existing bibliography of important literature in surgical oncology. RESULTS Citation-based algorithms were more effective than noncitation-based algorithms at identifying important articles. …

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