scholarly journals Heart Disease Prediction using Fog Computing based Wireless Body Sensor Networks (WSNs)

2021 ◽  
Vol 3 (1) ◽  
pp. 49-58
Author(s):  
Subarna Shakya ◽  
Joby P P
Keyword(s):  
Early Stage ◽  
Fog Computing ◽  
Ensemble Classifier ◽  
Quality Data ◽  
Body Sensor Networks ◽  
Human Beings ◽  
High Quality ◽  
Data Collector ◽  
Computation Process ◽  
Wireless Body Sensor Networks

Wireless Body Sensor Network (BSNs) are devices that can be worn by human beings. They have sensors with transmission, computation, storage and varying sensing qualities. When there are multiple devices to obtain data from, it is necessary to merge these data to avoid errors from being transmitted, resulting in a high quality fused data. In this proposed work, we have designed a data fusion approach with the help of data obtained from the BSNs, using Fog computing. Everyday activities are gathered in the form of data using an array of sensors which are then merged together to form high quality data. The data so obtained is then given as the input to ensemble classifier to predict heart-related diseases at an early stage. Using a fog computing environment, the data collector is established and the computation process is done with a decentralised system. A final output is produced on combining the result of the nodes using the fog computing database. A novel kernel random data collector is used for classification purpose to result in an improved quality. Experimental analysis indicates an accuracy of 96% where the depth is about 10 with an estimator count of 45 along with 7 features parameters considered.

Evidence-Based Practice & Practice-Based Evidence Applied to Adult, Medical Speech-Language Pathology

2013 ◽  
Vol 18 (1) ◽  
pp. 14-26 ◽  
Author(s):  
Rik Lemoncello ◽  
Bryan Ness
Keyword(s):  
Evidence Based Practice ◽  
Single Case ◽  
Quality Data ◽  
Evidence Based ◽  
Speech Language Pathology ◽  
Clinical Practices ◽  
High Quality ◽  
Randomized Controlled Clinical Trials ◽  
Relative Paucity ◽  
Language Pathology

In this paper, we review concepts of evidence-based practice (EBP), and provide a discussion of the current limitations of EBP in terms of a relative paucity of efficacy evidence and the limitations of applying findings from randomized controlled clinical trials to individual clinical decisions. We will offer a complementary model of practice-based evidence (PBE) to encourage clinical scientists to design, implement, and evaluate our own clinical practices with high-quality evidence. We will describe two models for conducting PBE: the multiple baseline single-case experimental design and a clinical case study enhanced with generalization and control data probes. Gathering, analyzing, and sharing high-quality data can offer additional support through PBE to support EBP in speech-language pathology. It is our hope that these EBP and PBE strategies will empower clinical scientists to persevere in the quest for best practices.

Collecting High-Quality Data

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Social Desirability ◽  
Measurement Errors ◽  
Monitoring And Evaluation ◽  
Quality Data ◽  
Social Desirability Bias ◽  
High Quality ◽  
High Quality Data ◽  
Evaluation Systems ◽  
Demand Effect

Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.

scholarly journals Structural and functional radiomics for lung cancer

2021 ◽  
Author(s):  
Guangyao Wu ◽  
Arthur Jochems ◽  
Turkey Refaee ◽  
Abdalla Ibrahim ◽  
Chenggong Yan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Treatment Strategy ◽  
Early Stage ◽  
Quality Data ◽  
Clinical Implementation ◽  
Early Stage Lung Cancer ◽  
Treatment Response Evaluation ◽  
Advanced Stage Lung Cancer ◽  
Stage Lung Cancer

Abstract Introduction Lung cancer ranks second in new cancer cases and first in cancer-related deaths worldwide. Precision medicine is working on altering treatment approaches and improving outcomes in this patient population. Radiological images are a powerful non-invasive tool in the screening and diagnosis of early-stage lung cancer, treatment strategy support, prognosis assessment, and follow-up for advanced-stage lung cancer. Recently, radiological features have evolved from solely semantic to include (handcrafted and deep) radiomic features. Radiomics entails the extraction and analysis of quantitative features from medical images using mathematical and machine learning methods to explore possible ties with biology and clinical outcomes. Methods Here, we outline the latest applications of both structural and functional radiomics in detection, diagnosis, and prediction of pathology, gene mutation, treatment strategy, follow-up, treatment response evaluation, and prognosis in the field of lung cancer. Conclusion The major drawbacks of radiomics are the lack of large datasets with high-quality data, standardization of methodology, the black-box nature of deep learning, and reproducibility. The prerequisite for the clinical implementation of radiomics is that these limitations are addressed. Future directions include a safer and more efficient model-training mode, merge multi-modality images, and combined multi-discipline or multi-omics to form “Medomics.”

Making Sense of Water Quality: Multispecies Encounters on the Mystic River

2013 ◽  
Vol 17 (2) ◽  
pp. 150-160 ◽  
Author(s):  
Caterina Scaramelli
Keyword(s):  
Water Quality ◽  
Laboratory Data ◽  
Quality Data ◽  
Human Beings ◽  
Water Lily ◽  
Water Quality Data ◽  
Water Flows ◽  
Making Sense ◽  
Quality Of Water

This paper takes water quality as an ethnographic subject. It looks at how water quality monitors in Boston make sense of the quality of water through mundane engagement with three non-human beings who they encounter during their monitoring activities: herring, bacteria and water lily. Each of these organisms suggests a different understanding of water quality for the monitors and poses a dilemma. Water quality monitors who contribute to the production of water quality data come to know water quality as through direct interactions with these beings, mediated by both sensorial experience and laboratory data. These experiences, at the same time, confuse and redraw relationships between science, water flows, non-human vitality, including that of invasive species, and people.

Developmental Impacts of Remittances on Migrant-Sending Households: Micro-Level Evidence from Punjab, Pakistan

2019 ◽  
Vol 14 (3) ◽  
pp. 338-366
Author(s):  
Kashif Imran ◽  
Evelyn S. Devadason ◽  
Cheong Kee Cheok
Keyword(s):  
Human Development ◽  
Human Development Index ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Development Index ◽  
Policy Interventions ◽  
Education And Health ◽  
Micro Level ◽  
The Government

This article analyzes the overall and type of developmental impacts of remittances for migrant-sending households (HHs) in districts of Punjab, Pakistan. For this purpose, an HH-based human development index is constructed based on the dimensions of education, health and housing, with a view to enrich insights into interactions between remittances and HH development. Using high-quality data from a HH micro-survey for Punjab, the study finds that most migrant-sending HHs are better off than the HHs without this stream of income. More importantly, migrant HHs have significantly higher development in terms of housing in most districts of Punjab relative to non-migrant HHs. Thus, the government would need policy interventions focusing on housing to address inequalities in human development at the district-HH level, and subsequently balance its current focus on the provision of education and health.

Capturing sexual assault data: An information system designed by forensic clinicians and healthcare researchers

2017 ◽  
Vol 47 (1) ◽  
pp. 46-55 ◽  
Author(s):  
S Aqif Mukhtar ◽  
Debbie A Smith ◽  
Maureen A Phillips ◽  
Maire C Kelly ◽  
Renate R Zilkens ◽  
...  
Keyword(s):  
Information System ◽  
Sexual Assault ◽  
Data Management ◽  
Sexual Violence ◽  
Western Australia ◽  
Management System ◽  
Data Management System ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data

Background: The Sexual Assault Resource Center (SARC) in Perth, Western Australia provides free 24-hour medical, forensic, and counseling services to persons aged over 13 years following sexual assault. Objective: The aim of this research was to design a data management system that maintains accurate quality information on all sexual assault cases referred to SARC, facilitating audit and peer-reviewed research. Methods: The work to develop SARC Medical Services Clinical Information System (SARC-MSCIS) took place during 2007–2009 as a collaboration between SARC and Curtin University, Perth, Western Australia. Patient demographics, assault details, including injury documentation, and counseling sessions were identified as core data sections. A user authentication system was set up for data security. Data quality checks were incorporated to ensure high-quality data. Results: An SARC-MSCIS was developed containing three core data sections having 427 data elements to capture patient’s data. Development of the SARC-MSCIS has resulted in comprehensive capacity to support sexual assault research. Four additional projects are underway to explore both the public health and criminal justice considerations in responding to sexual violence. The data showed that 1,933 sexual assault episodes had occurred among 1881 patients between January 1, 2009 and December 31, 2015. Sexual assault patients knew the assailant as a friend, carer, acquaintance, relative, partner, or ex-partner in 70% of cases, with 16% assailants being a stranger to the patient. Conclusion: This project has resulted in the development of a high-quality data management system to maintain information for medical and forensic services offered by SARC. This system has also proven to be a reliable resource enabling research in the area of sexual violence.

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