scholarly journals Teaching yourself about structural racism will improve your machine learning

Biostatistics ◽  
2019 ◽  
Author(s):  
Whitney R Robinson ◽  
Audrey Renson ◽  
Ashley I Naimi
Keyword(s):  
Machine Learning ◽  
Health Research ◽  
Structural Racism ◽  
Body Of Knowledge ◽  
Health Related ◽  
Almost All

Summary In this commentary, we put forth the following argument: Anyone conducting machine learning in a health-related domain should educate themselves about structural racism. We argue that structural racism is a critical body of knowledge needed for generalizability in almost all domains of health research.

Health research capacity in Nepal: Analysis of the trend and the role of local researchers

2021 ◽  
pp. 004947552098277
Author(s):  
Madhu Kharel ◽  
Alpha Pokharel ◽  
Krishna P Sapkota ◽  
Prasant V Shahi ◽  
Pratisha Shakya ◽  
...  
Keyword(s):  
Health Research ◽  
Fold Increase ◽  
Research Capacity ◽  
Rapid Review ◽  
Evidence Based ◽  
Middle Income ◽  
Middle Income Countries ◽  
Health Related ◽  
Evidence Based Decision Making

Evidence-based decision-making is less common in low- and middle-income countries where the research capacity remains low. Nepal, a lower-middle-income country in Asia, is not an exception. We conducted a rapid review to identify the trend of health research in Nepal and found more than seven-fold increase in the number of published health-related articles between 2000 and 2018. The proportion of articles with Nepalese researchers as the first authors has also risen over the years, though they are still only in two-thirds of the articles in 2018.

scholarly journals Effect of Integrated Neuromuscular Exercise in Physical Education Class on Health-Related Fitness in Female Children

Healthcare ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 312
Author(s):  
Marijana Sinđić ◽  
Draženka Mačak ◽  
Nikola Todorović ◽  
Bianka Purda ◽  
Maja Batez
Keyword(s):  
Physical Education ◽  
Muscular Fitness ◽  
Female Children ◽  
Before And After ◽  
Health Related ◽  
The Republic ◽  
Almost All ◽  
And Control ◽  
Cardiorespiratory Endurance ◽  
Health Related Fitness

Integrated neuromuscular training (INT) showed benefits for improving fundamental movement skills (FMS). However, the INT health-related fitness (HRF) effects are lacking. The current study aimed to determine the effects of INT implemented during physical education (PE) in a primary school in the Republic of Serbia on HRF in female children. The sample consisted of 72 healthy girls who were divided into the intervention (EG: n = 37; mean ± SD: age = 8.17 ± 0.31) and control (CG: n = 35; age = 8.11 ± 0.31) groups. The EG and CG performed the INT program and traditional PE activities two times per week within the first ~15 min of PE class, respectively. The Fitnessgram battery tests assessed the HRF (body composition, cardiorespiratory endurance, muscular fitness, and flexibility) before and after the program. After eight weeks, the EG significantly reduced all fat measures, while the CG decreased only triceps skinfold but to a smaller extent (F = 5.92, p < 0.02, ŋ2 = 0.09). Both groups significantly improved the performance of almost all muscular fitness tests (curl-ups, trunk lift, push-ups); however, the EG increased the push-ups more than the CG (F = 9.01, p < 0.01, ŋ2 = 0.14). The EG additionally improved the modified pull-ups (F = 14.09, p < 0.01, ŋ2 = 0.19) and flexed arm hang (F = 28.82, p < 0.01, ŋ2 = 0.33) tests. The flexibility and cardiorespiratory endurance of both groups did not significantly change after eight weeks. This approach of exercise showed positive acceptance and relatively good results after only eight weeks.

scholarly journals Blockchain Disrupting Fintech and the Banking System

Proceedings ◽  
2021 ◽  
Vol 74 (1) ◽  
pp. 24
Author(s):  
Eduard Alexandru Stoica ◽  
Daria Maria Sitea
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Financial Institutions ◽  
New World ◽  
Great Part ◽  
Banking System ◽  
Daily Activities ◽  
Blockchain Technology ◽  
Almost All

Nowadays society is profoundly changed by technology, velocity and productivity. While individuals are not yet prepared for holographic connection with banks or financial institutions, other innovative technologies have been adopted. Lately, a new world has been launched, personalized and adapted to reality. It has emerged and started to govern almost all daily activities due to the five key elements that are foundations of the technology: machine to machine (M2M), internet of things (IoT), big data, machine learning and artificial intelligence (AI). Competitive innovations are now on the market, helping with the connection between investors and borrowers—notably crowdfunding and peer-to-peer lending. Blockchain technology is now enjoying great popularity. Thus, a great part of the focus of this research paper is on Elrond. The outcomes highlight the relevance of technology in digital finance.

Integrated marketing communications and social marketing

2015 ◽  
Vol 5 (3) ◽  
pp. 226-240 ◽  
Author(s):  
Stephan Dahl ◽  
Lynne Eagle ◽  
David Low
Keyword(s):  
Social Context ◽  
Social Marketing ◽  
Marketing Communications ◽  
Commercial Application ◽  
Integrated Marketing Communications ◽  
Integrated Marketing ◽  
Content Type ◽  
Body Of Knowledge ◽  
Health Related ◽  
Social Marketing Campaigns

Purpose – The purpose of this paper is to examine the view of integrated marketing communications (IMC) by social marketing practitioners. Specifically, the paper furthers the discussion how a symbiotic relationship between IMC and social marketing can lead to both practical improvements of health-related social marketing campaigns, as well as theoretical advancement of the IMC construct. Design/methodology/approach – Based on semi-structured, in-depth interviews with practitioners, the authors provide exploratory evidence for support for IMC within the social marketing community and highlight potential differences and similarities when transferring IMC from a commercial to a social context. Findings – Three main differences emerged when transferring IMC from a commercial to a social context. These include differences of customer-centric approaches between commercial and social marketing, the need to weigh out the application of IMC to the charity brand or the use of IMC at a behavioural level and, finally, different complexity levels of desired behaviour as a mediating factor. Research limitations/implications – As with all qualitative data, the findings may not be generalisable beyond the interview participants and organisations studied. Practical implications – Many practitioners expressed that they liked IMC as a concept, but they lacked guidance as to the application with a social marketing context. This paper contributes to providing this guidance and establishing a body of knowledge how IMC can be applied in a non-commercial setting. Originality/value – The paper contributes to the practical development of guidance how the largely commercially applied IMC construct can be modified to be used in a social marketing context, while correspondingly highlighting how IMC needs to evolve to grow beyond purely commercial application.

Reproducibility in machine learning for health research: Still a ways to go

2021 ◽  
Vol 13 (586) ◽  
pp. eabb1655
Author(s):  
Matthew B. A. McDermott ◽  
Shirly Wang ◽  
Nikki Marinsek ◽  
Rajesh Ranganath ◽  
Luca Foschini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Research ◽  
Clinical Use ◽  
Scientific Papers

Machine learning for health must be reproducible to ensure reliable clinical use. We evaluated 511 scientific papers across several machine learning subfields and found that machine learning for health compared poorly to other areas regarding reproducibility metrics, such as dataset and code accessibility. We propose recommendations to address this problem.

scholarly journals Increase Suicidal Mortality Rate Among Females in District Peshawar

2018 ◽  
Vol 5 (1) ◽  
pp. 18-21
Author(s):  
Farooq Ahmed Abro ◽  
Rizwan Zafar Ansari ◽  
Muhammad Yousaf ◽  
Riaz Gul ◽  
Anwar UI Haq ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Low Socioeconomic Status ◽  
Reproductive Age ◽  
Cultural Norms ◽  
Medicine Department ◽  
Low Socioeconomic ◽  
Medical College ◽  
Unwanted Pregnancies ◽  
Health Related ◽  
Almost All

OBJECTIVE: To study increased suicidal mortality rate among females in district Peshawar. METHODOLOGY: The bodies of women in the reproductive age who died because of fatal deliberate self-harming were examined at Forensic Medicine Department Khyber Medical College, Peshawar from January 2015 to September 2015. RESULTS: Forty-four bodies of females were autopsied. 13 (30%) were adolescents between the age of 10-18 years. 31 (70.4%) were aged between 19-48 years. 33 of the victims committed suicide due to health-related issues. 28 (64%) victims used poisons. In almost all cases (n=38) the incident happened when the victim was alone in house or left unattended. 30 (68%) victims were brought to hospitals for treatment. 13 were found to have recurrent attempt of self-harming. CONCLUSION: Females have increased tendency to commit suicide than males especially in their reproductive age. Low socioeconomic status, cultural norms, unwanted pregnancies and ill health are the major causes.

scholarly journals A Review on Linear Regression Comprehensive in Machine Learning

2020 ◽  
Vol 1 (4) ◽  
pp. 140-147
Author(s):  
Dastan Maulud ◽  
Adnan M. Abdulazeez
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Linear Relationship ◽  
Multiple Regression ◽  
Polynomial Regression ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Explanatory Variables ◽  
Almost All ◽  
Simple Regression

Perhaps one of the most common and comprehensive statistical and machine learning algorithms are linear regression. Linear regression is used to find a linear relationship between one or more predictors. The linear regression has two types: simple regression and multiple regression (MLR). This paper discusses various works by different researchers on linear regression and polynomial regression and compares their performance using the best approach to optimize prediction and precision. Almost all of the articles analyzed in this review is focused on datasets; in order to determine a model's efficiency, it must be correlated with the actual values obtained for the explanatory variables.

Validating the Early Prediction Model for COPD Patients Care through a Federated Machine Learning Architecture on FAIR Data (Preprint)

2021 ◽  
Author(s):  
Celia ALVAREZ-ROMERO ◽  
Alicia MARTÍNEZ-GARCÍA ◽  
Jara Eloisa TERNERO-VEGA ◽  
Pablo DÍAZ-JIMÉNEZ ◽  
Carlos JIMÉNEZ-DE-JUAN ◽  
...  
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Prediction Model ◽  
Prospective Study ◽  
Health Research ◽  
Early Prediction ◽  
Learning Models ◽  
Copd Patients ◽  
Readmission Risk ◽  
Machine Learning Models

BACKGROUND Due to the nature of health data, its sharing and reuse for research are limited by legal, technical and ethical implications. In this sense, to address that challenge, and facilitate and promote the discovery of scientific knowledge, the FAIR (Findable, Accessible, Interoperable, and Reusable) principles help organizations to share research data in a secure, appropriate and useful way for other researchers. OBJECTIVE The objective of this study was the FAIRification of health research existing datasets and applying a federated machine learning architecture on top of the FAIRified datasets of different health research performing organizations. The whole FAIR4Health solution was validated through the assessment of the generated model for real-time prediction of 30-days readmission risk in patients with Chronic Obstructive Pulmonary Disease (COPD). METHODS The application of the FAIR principles in health research datasets in three different health care settings enabled a retrospective multicenter study for the generation of federated machine learning models, aiming to develop the early prediction model for 30-days readmission risk in COPD patients. This prediction model was implemented upon the FAIR4Health platform and, finally, an observational prospective study with 30-days follow-up was carried out in two health care centers from different countries. The same inclusion and exclusion criteria were used in both retrospective and prospective parts of the study. RESULTS The prediction model for the 30-days hospital readmission risk was trained using the retrospective data of 4.944 COPD patients. The assessment of the prediction model was performed using the data of 100 recruited (22 from Spain and 78 from Serbia) out of 2070 observed (records viewed) patients in total for the observational prospective study from April 2021 to September 2021. The significant accuracy (0.98) and precision (0.25) of the prediction model generated upon the FAIR4Health platform was observed and, as a result, the generated prediction of 30-day readmission risk was confirmed in 87% of the cases. CONCLUSIONS A clinical validation was demonstrated through the implementation of federated machine learning models on top of the FAIRified datasets from different health research performing organizations, providing an assessment for predicting 30-days readmission risk in COPD patients. This demonstration allowed to state the relevance and need of implementing a FAIR data policy to facilitate data sharing and reuse in health research.

Machinic Empathy and Mental Health: The Relational Ethics of Machine Empathy and Artificial Intelligence in Her

2019 ◽  
pp. 158-175
Author(s):  
Frances Shaw
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Health Research ◽  
Mental Health Treatment ◽  
Mental Health Research ◽  
Relational Ethics ◽  
Health Treatment

This paper situates a discussion of Her within contemporary developments in empathic machine learning for mental health treatment and therapy. Her simultaneously hooks into and critiques a particular imaginary about what artificial intelligence can do when combined with big data. Shaw threads the representation of empathy and artificial intelligence in the film into discussions of contemporary mental health research, in particular possibilities for the automation of treatment, whether through machine learning or guided interventions. Her provides some useful ways to think through utopian, dystopian, and ambivalent readings of such applications of technology in a broader sense, raising questions about sincerity and loss of human connectivity, relational ethics and automated empathy.

FinTech Adoption in China

2021 ◽  
pp. 166-173
Author(s):  
Gagan Kukreja
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Financial Services ◽  
New Technologies ◽  
Large Population ◽  
Financial Inclusion ◽  
High Penetration ◽  
Digital Payments ◽  
Almost All

Almost all financial services (especially digital payments) in China are affected by new innovations and technologies. New technologies such as blockchain, artificial intelligence, machine learning, deep learning, and data analytics have immensely influenced all most all aspects of financial services such as deposits, transactions, billings, remittances, credits (B2B and P2P), underwriting, insurance, and so on. Fintech companies are enabling larger financial inclusion, changing in lifestyle and expenditure behavior, better and fast financial services, and lots more. This chapter covers the development, opportunities, and challenges of financial sectors because of new technologies in China. This chapter throws the light on opportunities that emerged because of the large population of 1.4 billion people, high penetration, and access to the latest and affordable technology, affordable cost of smartphones, and government policies and regulations. Lastly, this chapter portrays the untapped potentials of Fintech in China.

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