Making Effective Use of Healthcare Data Using Data-to-Text Technology

The factors influencing the effective utilization coefficient of irrigation water are not understood well. It is usually considered that this coefficient is lower in areas with large-scale irrigation. With this background, we analyzed the effective utilization coefficient of irrigation water using the analytic hierarchy process using data from 2014 to 2019 in Shihezi City, Xinjiang. The weights of the influencing factors on the effective utilization coefficient of irrigation water in different irrigation areas were analyzed. Predictions of the coefficient’s values for different years were made by understanding the trends based on the grey model. The results show that the scale of the irrigation area is not the only factor determining the effective utilization coefficient of irrigation water. Irrigation technology, organizational integrity, crop types, water price management, local economic level, and channel seepage prevention are the most critical factors affecting the effective use of irrigation water. The grey model prediction results show that the effective utilization coefficient of farmland irrigation water will continuously increase and reach 0.7204 in 2029. This research can serve as a reference for government authorities to make scientific decisions on water-saving projects in irrigation districts in terms of management, operation, and investment.

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A Suggestion for an Effective Use of a Computer-aided Diagnosis System in Screening for Hyperthyroidism

Methods of Information in Medicine ◽

10.1055/s-0038-1636575 ◽

1977 ◽

Vol 16 (02) ◽

pp. 93-95 ◽

Cited By ~ 6

Author(s):

A. Alpérovitch ◽

P. Fragu

Keyword(s):

Clinical Signs ◽

Test Sample ◽

Bayes Theorem ◽

Computer Assisted ◽

Thyroid Status ◽

Screening Strategies ◽

Computer Aided ◽

Using Data ◽

Effective Use ◽

Aided Diagnosis

A computer-assisted program for diagnosing hyperthyroidism, specially devoted to the screening of functional thyroid status, has been written using data provided by 359 patients, 282 euthyroid and 77 hyperthyroid. The model is based on Bayes’ theorem.Using only 9 clinical signs and the free thyroxin index, the program achieved, on a test-sample of 117 new patients, an overall accuracy of 90% ; 10% of the cases were in the zone of uncertainty, and none was misdiagnosed.Different screening strategies are examined and their results discussed.

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Obtaining and using data of operational monitoring of atmospheric air

RUDN Journal of Ecology and Life Safety ◽

10.22363/2313-2310-2019-27-4-337-352 ◽

2019 ◽

Vol 27 (4) ◽

pp. 337-352

Author(s):

Ekaterina S. Kulakova ◽

Ayrat M. Safarov ◽

Liliya A. Nasyrova ◽

Dmitry S. Mizgirev

Keyword(s):

Air Monitoring ◽

Atmospheric Air ◽

Marker Substances ◽

Zone Of Influence ◽

Using Data ◽

Residential Quarters ◽

Effective Use ◽

Factor Regression ◽

Air Control ◽

The City

In order to control the chemical composition of atmospheric air in cities located in the zone of influence of petrochemical enterprises, automatic atmospheric air monitoring stations (ASKAV) are installed. For the effective use of experimental data, the authors of the article developed a data collection system with ASKAV, in which the air control in residential quarters and the gas mixture at the source of organized emissions are synchronized. The analysis of data on the concentration of pollutants in the atmospheric air of the Sterlitamak obtained from ASKAV, on the basis of which a list of marker substances for enterprises of the city was compiled. Priority sources of air pollution with marker substances during a different wind regime were identified. The authors developed models for changing the concentration of marker substances in the air using the method of factor regression. Based on the results of the cross-correlation function, the time of movement of the contaminated gas cloud from the source of emission to the residential area of the city is obtained. Conclusions are made about the effectiveness of the developed air monitoring system and the field of application of the acquired models.

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IN-HOUSE OR OUTSOURCING SKILLS: HOW BEST TO MANAGE FOR INNOVATION?

International Journal of Innovation Management ◽

10.1142/s1363919620500103 ◽

2019 ◽

Vol 24 (01) ◽

pp. 2050010 ◽

Cited By ~ 2

Author(s):

JUSTIN DORAN ◽

GERALDINE RYAN ◽

JANE BOURKE ◽

FRANK CROWLEY

Keyword(s):

Competitive Advantage ◽

Product Innovation ◽

Community Innovation Survey ◽

Workplace Skills ◽

Business Survival ◽

Organisational Innovation ◽

Using Data ◽

Effective Use ◽

Innovation Type ◽

Substantial Heterogeneity

Innovation is essential for driving business survival, development, and growth. Today, managers within firms continuously search for new ways to gain competitive advantage. In many cases, this comes from the effective use of intangible assets such as workplace skills and abilities. Despite this, little is known about what types of skills are required for innovation, whether these vary by innovation-type, or whether it matters if these skills are outsourced. This paper addresses these issues using data collected on eight skill types as part of the 2008–2010 Irish Community Innovation Survey. We find that there is substantial heterogeneity in the effectiveness of skills at generating different kinds of innovation. In addition, for some types of innovation, it is best to develop the skills in-house (e.g., Engineering skills for product innovation) while for others it is best to outsource the skills (e.g., Multimedia skills for process and organisational innovation).

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Integrated approach to drilling dynamics challenges in the Browse Basin

The APPEA Journal ◽

10.1071/aj11080 ◽

2012 ◽

Vol 52 (2) ◽

pp. 666

Author(s):

Yezid Arevalo ◽

Cathal O'Sullivan ◽

Ashley Fernandes

Keyword(s):

Real Time ◽

Technology Implementation ◽

Integrated Approach ◽

Planning Stage ◽

Time Dynamics ◽

Drilling Performance ◽

Planning Cycle ◽

Downhole Tool ◽

Using Data ◽

Effective Use

The use of drilling dynamics measurements has traditionally focused on improving downhole tool reliability. This, however, is a limited scope and in recognition of this, drilling dynamics is approached as a process that starts early in the planning stage of a project and targets the performance of the complete drillstring. Failures or inefficiencies associated with drillstring dynamics continue to occur in spite of the sophistication of today's measurements, particularly in exploratory projects that extend the present drilling envelope. Several methodologies were integrated to address the challenges of drilling dynamics and overcome frequent failures observed on the initial exploratory work on the Browse Basin. A steep learning curve was achieved by accelerating the improvement cycle using advanced modelling techniques and obtaining optimum designs without the need of multiple trial and error cycles. This extended abstract also describes the use of real-time dynamics measurements to quantify the risks related to drillstring vibration, a critical need for the drilling environment observed in the basin that ties planning work into the execution stage. Finally, the project cycle is closed with the evaluation of drilling performance using data-handling tools that allow the effective use of large amounts of drilling data generated during the execution and feedback into a new planning cycle. The extended abstract describes the implementation of drilling dynamics modelling to assist performance improvement, but more importantly, the methodology to incorporate it into a real-time decision-making process that maximises the value of technology implementation.

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Building confidence in digital health through metrology

British Journal of Radiology ◽

10.1259/bjr.20190574 ◽

2020 ◽

Vol 93 (1109) ◽

pp. 20190574

Author(s):

Nadia A. S. Smith ◽

David Sinden ◽

Spencer A. Thomas ◽

Marina Romanchikova ◽

Jessica E. Talbott ◽

...

Keyword(s):

Digital Health ◽

National Physical Laboratory ◽

Data Curation ◽

Data Sets ◽

Healthcare Data ◽

Future Challenges ◽

National Measurement ◽

The Uk ◽

Minimal Bias ◽

Effective Use

Healthcare is increasingly and routinely generating large volumes of data from different sources, which are difficult to handle and integrate. Confidence in data can be established through the knowledge that the data are validated, well-curated and with minimal bias or errors. As the National Measurement Institute of the UK, the National Physical Laboratory (NPL) is running an interdisciplinary project on digital health data curation. The project addresses one of the key challenges of the UK’s Measurement Strategy, to provide confidence in the intelligent and effective use of data. A workshop was organised by NPL in which important stakeholders from NHS, industry and academia outlined the current and future challenges in healthcare data curation. This paper summarises the findings of the workshop and outlines NPL’s views on how a metrological approach to the curation of healthcare data sets could help solve some of the important and emerging challenges of utilising healthcare data.

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Integrated Prediction System for Chronic Disease Diagnosis to Ensure Better Healthcare

International Journal of Health Sciences and Pharmacy ◽

10.47992/ijhsp.2581.6411.0053 ◽

2020 ◽

pp. 25-39

Author(s):

Geetha Poornima K. ◽

Krishna Prasad K.

Keyword(s):

Health Care ◽

Chronic Disease ◽

Chronic Diseases ◽

Predictive Analytics ◽

Disease Diagnosis ◽

Exercise Stress ◽

Healthcare Sector ◽

Human Beings ◽

Healthcare Data ◽

Using Data

Technology innovation has made life easy for human beings. Technology is being used everywhere. This also extends to the healthcare sector. The healthcare sector produces a large amount of data each minute. Because of privacy issues, much of the data generated is not used and is not publicly accessible. Healthcare data comes from diverse sources hence it will be always varied in nature. Keeping track of such data has become much easier these days. Predictive analysis in healthcare is an emerging technology that identifies the person with poor health where the risks of developing chronic conditions are more likely and provide better solutions in the field of healthcare. Statistical methods and algorithms can be used to predict the disease before the actual symptoms are revealed in humans. By using data analytics algorithms one can easily predict chronic diseases such as obesity, high/low Blood Pressure, diabetes, asthma, cardiopulmonary disorders. Because of an unhealthy diet, lack of proper exercise, stress, consumption of tobacco, alcohol, etc. chronic diseases are most common these days. If the symptoms of chronic diseases are detected in the early stages, there will be less risk of hospitalization by cost-effectively maintaining better health. Big data analysis and health care can be mixed to produce accurate results. The application of predictive analytics in healthcare is highlighted in this paper. It provides a broader analysis in the prevention of different chronic diseases by using predictive analytics. The paper also includes various issues that arise when handling health care data. For each chronic disease, diverse models, techniques, and algorithms are used for predicting and analyzing. The paper comprises a conceptual model that integrates the prediction of most common chronic diseases

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AN EVALUATION OF SWINE FLU (INFLUENZA A H3N2V) VIRUS PREDICTION USING DATA MINING AND CONVENTIONAL NEURAL NETWORK TECHNIQUES

Turkish Journal of Computer and Mathematics Education (TURCOMAT) ◽

10.17762/turcomat.v12i4.1214 ◽

2021 ◽

Vol 12 (4) ◽

pp. 1377-1386

Author(s):

Pilla Srinivas, Et. al.

Keyword(s):

Neural Network ◽

Data Mining ◽

Influenza A ◽

Medical Patient ◽

Swine Flu ◽

Research Trend ◽

Data Mining Technique ◽

Data Mining Techniques ◽

Healthcare Data ◽

Using Data

Nowadays, The health care commercial enterprise collects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information. Data mining plays a significant role in predicting diseases. The database report of medical patient is not more efficient, currently we made an Endeavour to detect the most widely spread disease in all over the world named Swine flu. Swine flu is a respiratory disease which has Numeral number of tests must be requisite from the patient for detecting a disease. Advanced data mining techniques gives us help to remedy this situation. In this work we describes about a prototype using data mining techniques, namely Naive Bayes Classifier. The Data mining is an emerging research trend which helps in finding accurate solutions in many fields. This paper highlights the various data mining technique and Convolution Neural Network used for predicting swine flu diseases.

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An economic analysis of varicella vaccination for health care workers

Epidemiology and Infection ◽

10.1017/s0950268897007887 ◽

1997 ◽

Vol 119 (2) ◽

pp. 209-220 ◽

Cited By ~ 26

Author(s):

A. M. GRAY ◽

P. FENN ◽

J. WEINBERG ◽

E. MILLER ◽

A. McGUIRE

Keyword(s):

Health Care ◽

Health Care Workers ◽

Vaccination Strategy ◽

Cost Effective ◽

Sensitivity Analyses ◽

Varicella Vaccination ◽

Care Workers ◽

The Mean ◽

Using Data ◽

Effective Use

A simulation model was constructed to assess the relative costs and cost-effectiveness of different screening and vaccination strategies for dealing with hospital incidents of varicella exposure, compared with current policies, using data from published sources and a hospital survey. The mean number of incidents per hospital year was 3·9, and the mean annual cost of managing these incidents was £5170. Vaccination of all staff would reduce annual incidents to 2·2 at a net cost of £48900 per incident averted. Screening all staff for previous varicella, testing those who are uncertain or report no previous varicella, and vaccinating those who test negative for VZV antibodies, reduces annual incidents to 2·3 and gives net savings of £440 per incident averted. Sensitivity analyses do not greatly alter the ranking of the options. Some form of VZV vaccination strategy for health care workers may well prove a cost-effective use of health care resources.

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Monitoring Organizational Transactions in Enterprise Information Systems with Continuous Assurance Requirements

International Journal of Enterprise Information Systems ◽

10.4018/ijeis.2015010102 ◽

2015 ◽

Vol 11 (1) ◽

pp. 13-32 ◽

Cited By ~ 10

Author(s):

Rui Pedro Marques ◽

Henrique Santos ◽

Carlos Santos

Keyword(s):

Information Systems ◽

System Support ◽

Enterprise Information ◽

Continuous Auditing ◽

Enterprise Information Systems ◽

Assurance Services ◽

Innovative Solution ◽

Ontological Model ◽

Using Data ◽

Effective Use

This work focuses on issues typically encountered in organizations whose core business largely depends on ICT: continuous monitoring, continuous auditing, controlling and assessment of transactions risk. Organizations have been making efforts to implement methods and systems which enable them to increase reliability of their business and, simultaneously, to be in accordance with their organizational objectives and compliant with external regulations. Thus, this work presents and validates an innovative solution to implement Continuous Assurance services in information systems applicable to any organizational transaction, regardless of its type, dimension, business area or even its information system support technology. This last objective is pursued having as support an ontological model at an abstraction level that guarantees that independence. This research culminated with the development of a prototype and consequent results analysis, using data collected from the near-real implementation, allowing us to ensure the feasibility and the effective use of the proposal.

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