scholarly journals Cognitive-Behavioral Styles of Dementia Care Management: Targeting and Tailoring to Style

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 54-54
Author(s):  
Amanda Leggett ◽  
Hyungjung Koo ◽  
Cathleen Connell ◽  
Laura Gitlin ◽  
Helen Kale
Keyword(s):  
Machine Learning ◽  
Care Management ◽  
Management Strategies ◽  
Primary Caregivers ◽  
Active Management ◽  
Extensive Literature ◽  
Behavioral Strategies ◽  
Poor Understanding ◽  
Behavioral Styles ◽  
Learning Analysis

Abstract Despite an extensive literature on the stress process of caregiving, little attention has focused on how caregivers provide care (caregiving styles). To explore caregiving styles among 100 primary caregivers for persons living with dementia, we utilize k-modes machine learning analysis. This technique clusters caregiver’s use of behavioral (Dementia Management Strategies Scale; criticism, active management, encouragement) and cognitive (Caregiver Readiness Scale; understanding, adaptability) approaches into style profiles. Three styles were identified: Managers (n=25; high use of criticism, moderate use of active management and encouragement, poor understanding and adaptability), Adapters (n=48; low use of criticism, high use of adaptive management and encouragement, good understanding and adaptability), and Avoiders (n=27; low use of all behavioral strategies, moderate adaptability and understanding). Styles differ by demographic and care characteristics. Findings suggest that caregivers have variable approaches to care that are measurable, thus, targeting/tailoring interventions to caregiver style could be an effective approach.

scholarly journals Machine learning analysis using 77,044 genomic and transcriptomic profiles to accurately predict tumor type

2021 ◽  
Vol 14 (3) ◽  
pp. 101016 ◽  
Author(s):  
Jim Abraham ◽  
Amy B. Heimberger ◽  
John Marshall ◽  
Elisabeth Heath ◽  
Joseph Drabick ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tumor Type ◽  
Learning Analysis

scholarly journals Technical Note: A Guide to Annotation of Neurosurgical Intraoperative Video for Machine Learning Analysis and Computer Vision

2021 ◽  
Author(s):  
Dhiraj J. Pangal ◽  
Guillaume Kugener ◽  
Shane Shahrestani ◽  
Frank Attenello ◽  
Gabriel Zada ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Technical Note ◽  
Learning Analysis

Machine learning analysis of multispectral imaging and clinical risk factors to predict amputation wound healing

2021 ◽  
Author(s):  
John J. Squiers ◽  
Jeffrey E. Thatcher ◽  
David Bastawros ◽  
Andrew J. Applewhite ◽  
Ronald D. Baxter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Wound Healing ◽  
Multispectral Imaging ◽  
Clinical Risk Factors ◽  
Clinical Risk ◽  
Learning Analysis

scholarly journals Application of Novel Machine Learning Techniques for Predicting the Surface Chloride Concentration in Concrete Containing Waste Material

Materials ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2297
Author(s):  
Ayaz Ahmad ◽  
Furqan Farooq ◽  
Krzysztof Adam Ostrowski ◽  
Klaudia Śliwa-Wieczorek ◽  
Slawomir Czarnecki
Keyword(s):  
Machine Learning ◽  
Mean Square Error ◽  
Gene Expression Programming ◽  
Chloride Concentration ◽  
Absolute Error ◽  
Chloride Ions ◽  
Machine Learning Techniques ◽  
Extensive Literature ◽  
Mean Square ◽  
Chloride Concentrations

Structures located on the coast are subjected to the long-term influence of chloride ions, which cause the corrosion of steel reinforcements in concrete elements. This corrosion severely affects the performance of the elements and may shorten the lifespan of an entire structure. Even though experimental activities in laboratories might be a solution, they may also be problematic due to time and costs. Thus, the application of individual machine learning (ML) techniques has been investigated to predict surface chloride concentrations (Cc) in marine structures. For this purpose, the values of Cc in tidal, splash, and submerged zones were collected from an extensive literature survey and incorporated into the article. Gene expression programming (GEP), the decision tree (DT), and an artificial neural network (ANN) were used to predict the surface chloride concentrations, and the most accurate algorithm was then selected. The GEP model was the most accurate when compared to ANN and DT, which was confirmed by the high accuracy level of the K-fold cross-validation and linear correlation coefficient (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) parameters. As is shown in the article, the proposed method is an effective and accurate way to predict the surface chloride concentration without the inconveniences of laboratory tests.

Water-demand management: assessing impacts of climate and other changes on water usage in Central Arizona

2011 ◽  
Vol 2 (4) ◽  
pp. 288-312 ◽  
Author(s):  
Netra B. Chhetri
Keyword(s):  
Water Resources ◽  
Water Demand ◽  
Supply And Demand ◽  
Demand Management ◽  
Secondary Data ◽  
Active Management ◽  
Agricultural System ◽  
Extensive Literature ◽  
Water Demand Management ◽  
Central Arizona

Planning for sustainable water management in the arid region of the southwestern USA is challenging mostly due to only partial understanding of factors converging around water supply and demand. Some of the factors that prompt concern about the adequacy of water resources are: (a) a growing urban population seeking a range of services, including the need to preserve and enhance aquatic ecosystems; (b) dwindling water storage due to multi-year drought conditions; and (c) the prospect of human-induced climate changes and its consequences in the hydrologic system of the region. This study analyzes the potential for water saving in the Phoenix Active Management Area (AMA) of Central Arizona, which includes the city of Phoenix, one of the fastest growing metropolitan areas in the country. Based on an extensive literature review and secondary data analysis, this paper investigates multiple factors that place increasing strain on current water resources, and attempts to extend this analysis to 2025. Outdoor water use within the residential landscape is the most important factor that strains water resources in Phoenix AMA. Any gain in efficiency through agricultural water demand management would not only improve the availability of water for other uses in the AMA, but would facilitate adaptation of the agricultural system to climate and other ongoing changes.

scholarly journals Integrative machine learning analysis of multiple gene expression profiles in cervical cancer

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e5285 ◽  
Author(s):  
Mei Sze Tan ◽  
Siow-Wee Chang ◽  
Phaik Leng Cheah ◽  
Hwa Jen Yap
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Cervical Cancer ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Hpv Infection ◽  
Gene Set Enrichment Analysis ◽  
Multiple Gene ◽  
Cervical Cancers ◽  
Learning Analysis

Although most of the cervical cancer cases are reported to be closely related to the Human Papillomavirus (HPV) infection, there is a need to study genes that stand up differentially in the final actualization of cervical cancers following HPV infection. In this study, we proposed an integrative machine learning approach to analyse multiple gene expression profiles in cervical cancer in order to identify a set of genetic markers that are associated with and may eventually aid in the diagnosis or prognosis of cervical cancers. The proposed integrative analysis is composed of three steps: namely, (i) gene expression analysis of individual dataset; (ii) meta-analysis of multiple datasets; and (iii) feature selection and machine learning analysis. As a result, 21 gene expressions were identified through the integrative machine learning analysis which including seven supervised and one unsupervised methods. A functional analysis with GSEA (Gene Set Enrichment Analysis) was performed on the selected 21-gene expression set and showed significant enrichment in a nine-potential gene expression signature, namely PEG3, SPON1, BTD and RPLP2 (upregulated genes) and PRDX3, COPB2, LSM3, SLC5A3 and AS1B (downregulated genes).

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