Analyst Performance Measures. Volume 2: Information Quality Tools for Persistent Surveillance Data Sets

2011 ◽  
Author(s):  
Marina Altynova ◽  
Ed Wasser ◽  
Telford Berkey ◽  
Sanjay Boddhu ◽  
Tin Sa ◽  
...  
Keyword(s):  
Performance Measures ◽  
Information Quality ◽  
Surveillance Data ◽  
Data Sets ◽  
Quality Tools

Analyst Performance Measures. Volume 3. Information Quality Tools for Persistent Surveillanec Data Sets

2011 ◽  
Author(s):  
John Talburt ◽  
Serhan Dagtas ◽  
Mariofanna Milanova ◽  
Mihail Tudoreanu ◽  
Brian Tsou
Keyword(s):  
Performance Measures ◽  
Information Quality ◽  
Data Sets ◽  
Quality Tools

Use of an Offset in Assessing Radiation Embrittlement Data and Predictive Methods

2006 ◽  
Author(s):  
Naoki Soneda ◽  
Colin English ◽  
William Server
Keyword(s):  
Surveillance Data ◽  
Data Sets ◽  
Radiation Embrittlement ◽  
Constant Amount ◽  
Predictive Values ◽  
Data Scatter ◽  
The Us ◽  
Predictive Methods ◽  
Potential Use ◽  
Reactor Pressure

Analyses of reactor pressure vessel (RPV) surveillance data from Charpy V-notch shift results coupled with our latest knowledge of the mechanisms of radiation embrittlement have led to new predictive correlations/models that have a strong technical underpinning. In this paper we examine how well the new CRIEPI embrittlement predicts US RPV surveillance data. Secondly, we note that within the US surveillance data sets there are indications that the data may follow the same form as the predictive models, but the data may be offset by a constant amount (either positive or negative) from the predictive values. This offset can be attributed in some cases to inadequate baseline data. In other cases, there does not appear to be a constant offset, or such an offset is hidden by data scatter. This paper also reviews the potential use of an offset adjustment and focuses on several surveillance datasets for comparisons.

scholarly journals Monotonic classification: An overview on algorithms, performance measures and data sets

2019 ◽  
Vol 341 ◽  
pp. 168-182 ◽  
Author(s):  
José-Ramón Cano ◽  
Pedro Antonio Gutiérrez ◽  
Bartosz Krawczyk ◽  
Michał Woźniak ◽  
Salvador García
Keyword(s):  
Performance Measures ◽  
Data Sets ◽  
Monotonic Classification

scholarly journals Modeling Approach to the Elements of TQM Practice

2013 ◽  
Vol 711 ◽  
pp. 719-721 ◽  
Author(s):  
Agamohamadi Basmenj Fazlollah ◽  
M. Yusuff Rosnah ◽  
Zulkifli Norzima ◽  
Ismaiel Yusof ◽  
Sorooshian Shahryar
Keyword(s):  
Qualitative Research ◽  
Quality Management ◽  
Total Quality Management ◽  
Performance Measures ◽  
Critical Success Factors ◽  
Success Factors ◽  
Total Quality ◽  
Critical Success ◽  
Quality Tools ◽  
Selection Of

This paper considers three factors; the selection of the Performance Measures, selection of the Critical Success Factors, and selection of quality tools as three elements of TQM. The qualitative research of this study tries to find and model the interrelation between elements of Total Quality Management (TQM) practice in companies.

scholarly journals Performance Test on Classification Algorithms

2019 ◽  
Vol 8 (2S3) ◽  
pp. 914-917
Keyword(s):  
Performance Measures ◽  
Performance Test ◽  
Confusion Matrix ◽  
Weather Data ◽  
Classification Algorithms ◽  
Data Sets ◽  
Huge Amount ◽  
Data Set ◽  
Random Forest Classification ◽  
Forest Classification

Nowadays, a huge amount of data is generated due to the growth in the technologies. There are different tools used to view this massive amount of data, and these tools contain different data mining techniques which can be applied for the obtained data sets. Classification is required to extract useful information or to predict the result from these enormous amounts of data. For this purpose, there are different classification algorithms. In this paper, we have compared Naive Bayes, K*, and random forest classification algorithm using Weka tool. To analyze the performance of these three algorithms we have considered three data sets. They are diabetes, supermarket and weather data set. In this work, an analysis is made based on the confusion matrix and different performance measures like RMSE, MAE, ROC, etc

Analysis of Freeway Traffic Incident Conditions by Using Second-Order Spatiotemporal Traffic Performance Measures

2005 ◽  
Vol 1925 (1) ◽  
pp. 20-28 ◽  
Author(s):  
Sherif Ishak ◽  
Ciprian Alecsandru
Keyword(s):  
Performance Measures ◽  
Second Order ◽  
Operating Conditions ◽  
Data Sets ◽  
Freeway Traffic ◽  
Contour Maps ◽  
Traffic Conditions ◽  
Statistical Measures ◽  
Study Results ◽  
Texture Characterization

The characteristics of preincident, postincident, and nonincident traffic conditions on freeways are investigated. The characteristics are defined by second-order statistical measures derived from spatiotemporal speed contour maps. Four performance measures are used to quantify properties such as smoothness, homogeneity, and randomness in traffic conditions in a manner similar to texture characterization of digital images. With real-world incident and traffic data sets, statistical analysis was conducted to seek distinctive characteristics of three groups of traffic operating conditions: preincident, postincident, and nonincident. The study results showed that the spatiotemporal characteristics of each of the three groups were not discernible. Although the distributions of performance measures within each group are statistically different, no consistent pattern was detected to imply that certain characteristics could increase the likelihood of incidents or identify precursory conditions to incidents.

scholarly journals A Phenotyping Algorithm to Identify People With HIV in Electronic Health Record Data (HIV-Phen): Development and Evaluation Study

10.2196/28620 ◽  
2021 ◽  
Vol 5 (11) ◽  
pp. e28620
Author(s):  
Sarah B May ◽  
Thomas P Giordano ◽  
Assaf Gottlieb
Keyword(s):  
Electronic Health Record ◽  
Predictive Value ◽  
Laboratory Tests ◽  
Significant Proportion ◽  
Surveillance Data ◽  
Health Record ◽  
Data Sets ◽  
Local Data ◽  
Electronic Health Record Data ◽  
Electronic Health

Background Identification of people with HIV from electronic health record (EHR) data is an essential first step in the study of important HIV outcomes, such as risk assessment. This task has been historically performed via manual chart review, but the increased availability of large clinical data sets has led to the emergence of phenotyping algorithms to automate this process. Existing algorithms for identifying people with HIV rely on a combination of International Classification of Disease codes and laboratory tests or closely mimic clinical testing guidelines for HIV diagnosis. However, we found that existing algorithms in the literature missed a significant proportion of people with HIV in our data. Objective The aim of this study is to develop and evaluate HIV-Phen, an updated criteria-based HIV phenotyping algorithm. Methods We developed an algorithm using HIV-specific laboratory tests and medications and compared it with previously published algorithms in national and local data sets to identify cohorts of people with HIV. Cohort demographics were compared with those reported in the national and local surveillance data. Chart reviews were performed on a subsample of patients from the local database to calculate the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the algorithm. Results Our new algorithm identified substantially more people with HIV in both national (up to an 85.75% increase) and local (up to an 83.20% increase) EHR databases than the previously published algorithms. The demographic characteristics of people with HIV identified using our algorithm were similar to those reported in national and local HIV surveillance data. Our algorithm demonstrated improved sensitivity over existing algorithms (98% vs 56%-92%) while maintaining a similar overall accuracy (96% vs 80%-96%). Conclusions We developed and evaluated an updated criteria-based phenotyping algorithm for identifying people with HIV in EHR data that demonstrates improved sensitivity over existing algorithms.

scholarly journals Precision Health–Enabled Machine Learning to Identify Need for Wraparound Social Services Using Patient- and Population-Level Data Sets: Algorithm Development and Validation (Preprint)

2019 ◽  
Author(s):  
Suranga N Kasthurirathne ◽  
Shaun Grannis ◽  
Paul K Halverson ◽  
Justin Morea ◽  
Nir Menachemi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Determinants Of Health ◽  
Social Services ◽  
Performance Measures ◽  
Social Determinants ◽  
Population Level ◽  
Decision Models ◽  
Data Sets ◽  
Level Data ◽  
Precision Health

BACKGROUND Emerging interest in precision health and the increasing availability of patient- and population-level data sets present considerable potential to enable analytical approaches to identify and mitigate the negative effects of social factors on health. These issues are not satisfactorily addressed in typical medical care encounters, and thus, opportunities to improve health outcomes, reduce costs, and improve coordination of care are not realized. Furthermore, methodological expertise on the use of varied patient- and population-level data sets and machine learning to predict need for supplemental services is limited. OBJECTIVE The objective of this study was to leverage a comprehensive range of clinical, behavioral, social risk, and social determinants of health factors in order to develop decision models capable of identifying patients in need of various wraparound social services. METHODS We used comprehensive patient- and population-level data sets to build decision models capable of predicting need for behavioral health, dietitian, social work, or other social service referrals within a safety-net health system using area under the receiver operating characteristic curve (AUROC), sensitivity, precision, F1 score, and specificity. We also evaluated the value of population-level social determinants of health data sets in improving machine learning performance of the models. RESULTS Decision models for each wraparound service demonstrated performance measures ranging between 59.2%% and 99.3%. These results were statistically superior to the performance measures demonstrated by our previous models which used a limited data set and whose performance measures ranged from 38.2% to 88.3% (behavioural health: F1 score <i>P</i>&lt;.001, AUROC <i>P</i>=.01; social work: F1 score <i>P</i>&lt;.001, AUROC <i>P</i>=.03; dietitian: F1 score <i>P</i>=.001, AUROC <i>P</i>=.001; other: F1 score <i>P</i>=.01, AUROC <i>P</i>=.02); however, inclusion of additional population-level social determinants of health did not contribute to any performance improvements (behavioural health: F1 score <i>P</i>=.08, AUROC <i>P</i>=.09; social work: F1 score <i>P</i>=.16, AUROC <i>P</i>=.09; dietitian: F1 score <i>P</i>=.08, AUROC <i>P</i>=.14; other: F1 score <i>P</i>=.33, AUROC <i>P</i>=.21) in predicting the need for referral in our population of vulnerable patients seeking care at a safety-net provider. CONCLUSIONS Precision health–enabled decision models that leverage a wide range of patient- and population-level data sets and advanced machine learning methods are capable of predicting need for various wraparound social services with good performance.

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