scholarly journals Digital conversations about depression among Hispanics and non-Hispanics in the US: a big‐data, machine learning analysis identifies specific characteristics of depression narratives in Hispanics

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Ruby Castilla-Puentes ◽  
Anjali Dagar ◽  
Dinorah Villanueva ◽  
Laura Jimenez-Parrado ◽  
Liliana Gil Valleta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Open Source ◽  
Cross Sectional Study ◽  
Source Analysis ◽  
Learning Technology ◽  
Cross Sectional ◽  
The Us ◽  
Negative Tone ◽  
Online Conversation

Abstract Background Digital conversations can offer unique information into the attitudes of Hispanics with depression outside of formal clinical settings and help generate useful information for medical treatment planning. Our study aimed to explore the big data from open‐source digital conversations among Hispanics with regard to depression, specifically attitudes toward depression comparing Hispanics and non-Hispanics using machine learning technology. Methods Advanced machine‐learning empowered methodology was used to mine and structure open‐source digital conversations of self‐identifying Hispanics and non-Hispanics who endorsed suffering from depression and engaged in conversation about their tone, topics, and attitude towards depression. The search was limited to 12 months originating from US internet protocol (IP) addresses. In this cross-sectional study, only unique posts were included in the analysis and were primarily analyzed for their tone, topic, and attitude towards depression between the two groups using descriptive statistical tools. Results A total of 441,000 unique conversations about depression, including 43,000 (9.8%) for Hispanics, were posted. Source analysis revealed that 48% of conversations originated from topical sites compared to 16% on social media. Several critical differences were noted between Hispanics and non-Hispanics. In a higher percentage of Hispanics, their conversations portray “negative tone” due to depression (66% vs 39% non-Hispanics), show a resigned/hopeless attitude (44% vs. 30%) and were about ‘living with’ depression (44% vs. 25%). There were important differences in the author's determined sentiments behind the conversations among Hispanics and non-Hispanics. Conclusion In this first of its kind big data analysis of nearly a half‐million digital conversations about depression using machine learning, we found that Hispanics engage in an online conversation about negative, resigned, and hopeless attitude towards depression more often than non-Hispanic.

scholarly journals Digital conversations about depression among Hispanics and non-Hispanics in the US: A big‐data, machine learning analysis

2021 ◽  
Author(s):  
Ruby Castilla-Puentes ◽  
Anjali Dagar ◽  
Dinorah Villanueva ◽  
Laura Jimenez-Parrado ◽  
Liliana. Gil Valleta ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Open Source ◽  
Source Analysis ◽  
Clinical Settings ◽  
Learning Technology ◽  
The Us ◽  
Negative Tone ◽  
Online Conversation ◽  
Learning Analysis

Abstract Background Digital conversations can offer unique information into the attitudes of Hispanics with depression outside of formal clinical settings and help generate useful information for medical treatment planning. Our study aimed to explore the big data from open-source digital conversations among Hispanics with regard to depression, specifically attitudes toward depression comparing Hispanics and non-Hispanics using machine learning technology. Methods Advanced machine‐learning empowered methodology was used to mine and structure open‐source digital conversations of self‐identifying Hispanics and non-Hispanics who endorsed suffering from depression and engaged in conversation about their tone, topics, and attitude towards depression. The search was limited to 12 months originating from US internet protocol (IP) addresses. Results A total of 441, 000 unique conversations about depression, including 43,000 (9.8%) for Hispanics, were posted. Source analysis revealed that 48% of conversations originated from topical sites compared to 16% on social media. Several critical differences were noted between Hispanics and non-Hispanics. In a higher percentage of Hispanics, their conversations portray “negative tone” due to depression (66% vs 39% non-Hispanics), show a resigned/hopeless attitude (44% vs. 30%) and were about ‘living with’ depression (44% vs. 25%). There were important differences in the author's determined sentiments behind the conversations among Hispanics and non-Hispanics. Conclusion In this first of its kind big data analysis of nearly a half-million digital conversations about depression using machine learning we found that Hispanics engage in an online conversation about negative, resigned, and hopeless attitude towards depression more often than non-Hispanic.

The Cross-Sectional Pricing of Corporate Bonds Using Big Data and Machine Learning

2020 ◽  
Author(s):  
Turan G. Bali ◽  
Amit Goyal ◽  
Dashan Huang ◽  
Fuwei Jiang ◽  
Quan Wen
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Corporate Bonds ◽  
Cross Sectional ◽  
The Cross

scholarly journals Ultra-processed foods and added sugars in the US diet: evidence from a nationally representative cross-sectional study

BMJ Open ◽  
2016 ◽  
Vol 6 (3) ◽  
pp. e009892 ◽  
Author(s):  
Eurídice Martínez Steele ◽  
Larissa Galastri Baraldi ◽  
Maria Laura da Costa Louzada ◽  
Jean-Claude Moubarac ◽  
Dariush Mozaffarian ◽  
...  
Keyword(s):  
Cross Sectional Study ◽  
Processed Foods ◽  
Sectional Study ◽  
Cross Sectional ◽  
Added Sugars ◽  
The Us ◽  
Nationally Representative

The Rain Prediction in Australia Based Big Data Analysis and Machine Learning Technology

2021 ◽  
Author(s):  
Bohdan Polishchuk ◽  
Andrii Berko ◽  
Lyubomyr Chyrun ◽  
Myroslava Bublyk ◽  
Vadim Schuchmann
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Big Data Analysis ◽  
Learning Technology

Model of predicting customer behavior based on big data analysis technologies

2021 ◽  
Vol 1 (2 (6)) ◽  
Author(s):  
Anastasiia Ivanitska ◽  
Dmytro Ivanov ◽  
Ludmila Zubik
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Customer Behavior ◽  
Learning Technologies ◽  
User Preferences ◽  
Data Sets ◽  
Learning Technology ◽  
Behavior Prediction ◽  
Network Information ◽  
Selection Of

The analysis of the available methods and models of formation of recommendations for the potential buyer in network information systems for the purpose of development of effective modules of selection of advertising is executed. The effectiveness of the use of machine learning technologies for the analysis of user preferences based on the processing of data on purchases made by users with a similar profile is substantiated. A model of recommendation formation based on machine learning technology is proposed, its work on test data sets is tested and the adequacy of the RMSE model is assessed. Keywords: behavior prediction; advertising based on similarity; collaborative filtering; matrix factorization; big data; machine learning

Self-Care of African Immigrant Adults with Chronic Illness

2021 ◽  
pp. 105477382110561
Author(s):  
Onome Henry Osokpo ◽  
Lisa M. Lewis ◽  
Uchechukwu Ikeaba ◽  
Jesse Chittams ◽  
Frances K. Barg ◽  
...  
Keyword(s):  
Chronic Illness ◽  
Self Efficacy ◽  
Self Care ◽  
African Immigrants ◽  
Cross Sectional Study ◽  
The Self ◽  
Cross Sectional ◽  
Income Adequacy ◽  
The Us ◽  
Scale Scores

This cross-sectional study aims to describe the self-care of adult African immigrants in the US with chronic illness and explore the relationship between acculturation and self-care. A total of 88 African immigrants with chronic illness were enrolled. Self-care was measured with the Self Care of Chronic Illness Inventory v3 and the Self-Care Self-Efficacy scale. Scores are standardized 0 to 100 with scores >70 considered adequate. Acculturation was measured using a modified standardized acculturation instrument and predefined acculturation proxies. The self-care scores showed adequate self-care, with the mean scores of 78.6, 77.9, and 75.6 for self-care maintenance, monitoring, and management. Self-care self-efficacy mean score was 81.3. Acculturation was not significantly associated with self-care. Self-care self-efficacy was a strong determinant of self-care maintenance ( p < .0001), monitoring ( p < .0001), and management ( p < .0001). The perception of inadequate income was a significant determinant of poor self-care management ( p = .03). Self-care self-efficacy and perceived income adequacy were better determinants of self-care than acculturation.

scholarly journals In-House, Open-Source 3D-Software-Based, CAD/CAM-Planned Mandibular Reconstructions in 20 Consecutive Free Fibula Flap Cases: An Explorative Cross-Sectional Study With Three-Dimensional Performance Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Lucas M. Ritschl ◽  
Paul Kilbertus ◽  
Florian D. Grill ◽  
Matthias Schwarz ◽  
Jochen Weitz ◽  
...  
Keyword(s):  
Open Source ◽  
Dimensional Analysis ◽  
Open Source Software ◽  
Three Dimensional ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Free Fibula Flap ◽  
Cross Sectional ◽  
Cad Cam ◽  
Free Fibula

BackgroundMandibular reconstruction is conventionally performed freehand, CAD/CAM-assisted, or by using partially adjustable resection aids. CAD/CAM-assisted reconstructions are usually done in cooperation with osteosynthesis manufacturers, which entails additional costs and longer lead time. The purpose of this study is to analyze an in-house, open-source software-based solution for virtual planning.Methods and MaterialsAll consecutive cases between January 2019 and April 2021 that underwent in-house, software-based (Blender) mandibular reconstruction with a free fibula flap (FFF) were included in this cross-sectional study. The pre- and postoperative Digital Imaging and Com munications in Medicine (DICOM) data were converted to standard tessellation language (STL) files. In addition to documenting general information (sex, age, indication for surgery, extent of resection, number of segments, duration of surgery, and ischemia time), conventional measurements and three-dimensional analysis methods (root mean square error [RMSE], mean surface distance [MSD], and Hausdorff distance [HD]) were used.ResultsTwenty consecutive cases were enrolled. Three-dimensional analysis of preoperative and virtually planned neomandibula models was associated with a median RMSE of 1.4 (0.4–7.2), MSD of 0.3 (-0.1–2.9), and HD of 0.7 (0.1–3.1). Three-dimensional comparison of preoperative and postoperative models showed a median RMSE of 2.2 (1.5–11.1), MSD of 0.5 (-0.6–6.1), and HD of 1.5 (1.1–6.5) and the differences were significantly different for RMSE (p &lt; 0.001) and HD (p &lt; 0.001). The difference was not significantly different for MSD (p = 0.554). Three-dimensional analysis of virtual and postoperative models had a median RMSE of 2.3 (1.3–10.7), MSD of -0.1 (-1.0–5.6), and HD of 1.7 (0.1–5.9).ConclusionsOpen-source software-based in-house planning is a feasible, inexpensive, and fast method that enables accurate reconstructions. Additionally, it is excellent for teaching purposes.

scholarly journals Consumptions of plain water, moisture in foods and beverages, and total water in relation to dietary micronutrient intakes and serum nutrient profiles among US adults

2014 ◽  
Vol 18 (7) ◽  
pp. 1180-1186 ◽  
Author(s):  
Meng Yang ◽  
Ock K Chun
Keyword(s):  
Water Intake ◽  
Cross Sectional Study ◽  
Plain Water ◽  
Total Water ◽  
Cross Sectional ◽  
Negatively Associated ◽  
Health And Nutrition ◽  
The Us ◽  
Nutrient Profiles ◽  
Total Water Intake

AbstractObjectiveTo investigate water contributors in relation to dietary and serum micronutrient profiles.DesignA cross-sectional study. The main exposures were water contributors. Selected dietary and serum micronutrient levels were outcome measures.SettingsThe US population and its subgroups.SubjectsUS adults (n 2691) aged ≥20 years from the National Health and Nutrition Examination Survey 2005–2006.ResultsThe daily mean total water intake was 3·1 (se 0·047) litres, with 68 % of adults consuming below the Adequate Intake level. Total water intake was higher in adults with higher BMI and physical activity, those taking dietary supplements and alcohol consumers (P < 0·05). Plain water intake was positively associated with food moisture and negatively with beverage moisture (P < 0·001). Beverage moisture was negatively associated with food moisture (P < 0·001). In multivariate regression analyses, plain water and food moisture intakes were positively associated with Fe, Ca, vitamins A, B, C, E and K and carotenoid intakes (P < 0·05). However, beverage moisture was unrelated to Ca, niacin and vitamin B6 intakes, and negatively associated with Fe, vitamin A, folate, vitamins C, E and K and carotenoid intakes (P < 0·05). Concentrations of serum vitamins A and C and carotenoids increased with plain water and food moisture intakes (P < 0·05) but decreased (P < 0·01) or were unrelated to beverage moisture intake.ConclusionsVarious contributors of total water intake differed in their associations with dietary and serum micronutrient profiles in US adults. The study provides evidence of plain water benefits on micronutrient adequacy over beverages.

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