Showing papers by "Ron S. Kenett published in 2013"

Systolic Blood Pressure During Acute Stroke is Associated With Functional Status and Long-term Mortality in the Elderly

Abstract: Background and Purpose—The prognostic value of blood pressure (BP) levels during acute stroke has not been adequately studied. Most studies do not use continuous BP measurements, and patients are followed only for a short period. We designed a study to assess, with 24-hour BP monitoring (24H BPM), the impact of BP levels during the first day of stroke, on the short-term functional status and long-term mortality in elderly patients. Methods—We studied 177 patients with acute stroke (89 men), mean age 84±6 years. BP was measured on admission and 24H BPM was recorded within 24 hours of admission. After 7 days, patients were assessed for functional status according to the modified Rankin Scale and were subsequently followed up for mortality ≤5 years (mean, 2.07±1.48). Results—After 7 days, functional status improved and modified Rankin Scale decreased from 4.2 to 3.7. Follow-up analysis disclosed that 71 patients (27 men and 44 women) had died. Mortality rate was higher in women (50% versus 30%; P<0.01) and i...

Industrial statistics applications in the semiconductor industry: some examples

Abstract: The semiconductor industry ranges from the design and production of semiconductors on silicon wafers to automatic placement robots that insert semiconductor devices on hybrid microcircuits.Wafers consist of electronic circuits or chips that are characterized by electrical and mechanical characteristics. Process modeling and simulations provide predictions of geometries and material properties of semiconductor devices and wafer structures and help design and improve manufacturing processes such as photolithography, etching, deposition, and ion implantation. In this paper, we focus on three application areas of industrial statistics to the semiconductor industry. These are: (1) process capability indices, (2) process monitoring, and (3) multivariate statistical process control. We refer to two case studies that set a context and provide examples to the presented techniques. Copyright © 2012 John Wiley & Sons, Ltd.

An Information Quality (InfoQ) Framework for Ex-Ante and Ex-Post Evaluation of Empirical Studies

Abstract: Numbers are not data and data analysis does not necessarily produce information and knowledge. Statistics, data mining, and artificial intelligence are disciplines focused on extracting knowledge from data. They provide tools for testing hypotheses, predicting new observations, quantifying population effects, and summarizing data efficiently. In these fields, measurable data is used to derive knowledge. However, a clean, exact and complete dataset, which is analyzed professionally, might contain no useful information for the problem under investigation. The term Information Quality (InfoQ) was coined by Ref. [15] as the potential of a dataset to achieve a specific (scientific or practical) goal using a given data analysis method. InfoQ is a function of goal, data, data analysis, and utility. Eight dimensions that relate to these components help assess InfoQ: Data Resolution, Data Structure, Data Integration, Temporal Relevance, Generalizability, Chronology of Data and Goal, Construct Operationalization, and Communication. The eight dimensions can be used for developing streamlined evaluation metrics of InfoQ. We describe two studies where InfoQ was integrated into research methods courses, guiding students in evaluating InfoQ of prospective and retrospective studies. The results and feedback indicate the importance and usefulness of InfoQ and its eight dimensions for evaluating empirical studies.

Managing Risks with Data

Abstract: Assessing exposure to potential risk events and initiating proactive risk mitigation actions is currently a clear priority of businesses, organizations and governments world-wide. Managing risks with data is a growing discipline that involves data acquisition and data merging, risk analytics and risk management decisions support systems. The paper will provide an overview of modern integrated risk management, including examples of how qualitative unstructured data, like text and voice recordings, can be combined with quantitative data like balance sheets and technical performance, to generate integrated risk scores. We suggest that data based risk analysis is an essential competency complementing and reinforcing the more traditional subjective scoring methods used in classical risk management. The examples we will use consist of applications of risk scoring models, Bayesian Networks to map cause and effect, Ontologies to interpret automated text annotation, ETL to merge various data bases and a follow up integrated risk management approach. The main theme of the paper is that risk management can be intuitive, based on qualitative assessments and expert opinions, quantitative in scope, exploit semantic unstructured information or integrated. We aim to show the advantages of integrated data based risk management over the more basic intuitive approach practiced in many organizations.