scholarly journals An Exploration and Forecast of COVID-19 in Mexico with Machine Learning

2020 ◽  
Author(s):  
Daniela A. Gomez-Cravioto ◽  
Ramon E. Diaz-Ramos ◽  
Francisco J. Cantu-Ortiz ◽  
Hector G. Ceballos
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Health Care Providers ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Logistic Growth ◽  
Care Providers ◽  
Modern Approach ◽  
Logistic Regression Models ◽  
Feature Importance

Abstract Background: To understand and approach the COVID-19 spread, Machine Learning offers fundamental tools. This study presents the use of machine learning techniques for the projection of COVID-19 infections and deaths in Mexico. The research has three main objectives: first, to identify which function adjusts the best to the infected population growth in Mexico; second, to determine the feature importance of climate and mobility; third, to compare the results of a traditional time series statistical model with a modern approach in machine learning. The motivation for this work is to support health care providers in their preparation and planning. Methods: The methods used are linear, polynomial, and generalized logistic regression models to evaluate the growth of the COVID-19 incidents in the country. Additionally, machine learning and time-series techniques are used to identify feature importance and perform forecasting for daily cases and fatalities. The study uses the publicly available data sets from the John Hopkins University of Medicine in conjunction with mobility rates obtained from Google’s Mobility Reports and climate variables acquired from Weather Online. Results: The results suggest that the logistic growth model fits best the behavior of the pandemic in Mexico, that there is a significant correlation of climate and mobility variables with the disease numbers, and that LSTM is a more suitable approach for the prediction of daily cases. Conclusion: We hope that this study can make some contributions to the world’s response to this epidemic as well as give some references for future research.

scholarly journals Data Analysis and Forecasting of the COVID-19 Spread: A Comparison of Recurrent Neural Networks and Time Series Models

2021 ◽  
Author(s):  
Daniela A. Gomez-Cravioto ◽  
Ramon E. Diaz-Ramos ◽  
Francisco J. Cantu-Ortiz ◽  
Hector G. Ceballos
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Health Care Providers ◽  
Time Series Data ◽  
Machine Learning Techniques ◽  
Series Data ◽  
Logistic Growth ◽  
Care Providers ◽  
Modern Approach ◽  
Feature Importance

AbstractTo understand and approach the spread of the SARS-CoV-2 epidemic, machine learning offers fundamental tools. This study presents the use of machine learning techniques for projecting COVID-19 infections and deaths in Mexico. The research has three main objectives: first, to identify which function adjusts the best to the infected population growth in Mexico; second, to determine the feature importance of climate and mobility; third, to compare the results of a traditional time series statistical model with a modern approach in machine learning. The motivation for this work is to support health care providers in their preparation and planning. The methods compared are linear, polynomial, and generalized logistic regression models to describe the growth of COVID-19 incidents in Mexico. Additionally, machine learning and time series techniques are used to identify feature importance and perform forecasting for daily cases and fatalities. The study uses the publicly available data sets from the John Hopkins University of Medicine in conjunction with the mobility rates obtained from Google’s Mobility Reports and climate variables acquired from the Weather Online API. The results suggest that the logistic growth model fits best the pandemic’s behavior, that there is enough correlation of climate and mobility variables with the disease numbers, and that the Long short-term memory network can be exploited for predicting daily cases. Given this, we propose a model to predict daily cases and fatalities for SARS-CoV-2 using time series data, mobility, and weather variables.

scholarly journals What is morally at stake when using algorithms to make medical diagnoses? Expanding the discussion beyond risks and harms

2022 ◽  
Author(s):  
Bas de Boer ◽  
Olya Kudina
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Health Care Providers ◽  
Medical Diagnosis ◽  
Clinical Decision Support Systems ◽  
Active Role ◽  
Machine Learning Techniques ◽  
Care Providers ◽  
Technological Mediation ◽  
Medical Diagnoses

AbstractIn this paper, we examine the qualitative moral impact of machine learning-based clinical decision support systems in the process of medical diagnosis. To date, discussions about machine learning in this context have focused on problems that can be measured and assessed quantitatively, such as by estimating the extent of potential harm or calculating incurred risks. We maintain that such discussions neglect the qualitative moral impact of these technologies. Drawing on the philosophical approaches of technomoral change and technological mediation theory, which explore the interplay between technologies and morality, we present an analysis of concerns related to the adoption of machine learning-aided medical diagnosis. We analyze anticipated moral issues that machine learning systems pose for different stakeholders, such as bias and opacity in the way that models are trained to produce diagnoses, changes to how health care providers, patients, and developers understand their roles and professions, and challenges to existing forms of medical legislation. Albeit preliminary in nature, the insights offered by the technomoral change and the technological mediation approaches expand and enrich the current discussion about machine learning in diagnostic practices, bringing distinct and currently underexplored areas of concern to the forefront. These insights can contribute to a more encompassing and better informed decision-making process when adapting machine learning techniques to medical diagnosis, while acknowledging the interests of multiple stakeholders and the active role that technologies play in generating, perpetuating, and modifying ethical concerns in health care.

Acceptance of the HPV Vaccine in a Multiethnic Sample of Latinx Mothers

2021 ◽  
Vol 31 (3) ◽  
pp. 472-483
Author(s):  
Ana Cristina Lindsay ◽  
Madelyne J. Valdez ◽  
Denisse Delgado ◽  
Emily Restrepo ◽  
Yessica M. Guzmán ◽  
...  
Keyword(s):  
Health Care Providers ◽  
Thematic Analysis ◽  
Hpv Vaccine ◽  
Female Adolescents ◽  
Future Research ◽  
Care Providers ◽  
Foreign Born ◽  
Structured Interviews ◽  
Direct Benefits ◽  
Multiethnic Sample

This descriptive qualitative study explored Latinx mothers’ acceptance of the human papillomavirus (HPV) vaccine for their adolescent children. Data were collected through individual, semi-structured interviews and analyzed using a hybrid method of thematic analysis that incorporated deductive and inductive approaches. Twenty-two ( n = 22), mostly foreign-born, Latinx mothers of male and female adolescents participated in the study. Three main themes and nine subthemes emerged from the analyses. Findings identified the need for increased efforts to raise awareness and knowledge among Latinx mothers of the direct benefits of the HPV vaccine for sons, including stressing prevention of HPV-associated cancers in males. Findings also underscore the need for improved health care providers’ communication and recommendation of the HPV vaccine for Latinx adolescent males. Future research should intervene upon the study’s findings to address barriers that remain and affect Latinx mothers’ acceptance and uptake of the HPV vaccine for their children, in particular their sons.

Ways that nurse practitioner students self-explain during diagnostic reasoning

Diagnosis ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Leah Burt ◽  
Susan Corbridge ◽  
Colleen Corte ◽  
Laurie Quinn ◽  
Lorna Finnegan ◽  
...  
Keyword(s):  
Problem Solving ◽  
Health Care Providers ◽  
Nurse Practitioner ◽  
Descriptive Analysis ◽  
Diagnostic Errors ◽  
Diagnostic Reasoning ◽  
Knowledge Structures ◽  
Future Research ◽  
Biological Knowledge ◽  
Care Providers

Abstract Objectives An important step in mitigating the burden of diagnostic errors is strengthening diagnostic reasoning among health care providers. A promising way forward is through self-explanation, the purposeful technique of generating self-directed explanations to process novel information while problem-solving. Self-explanation actively improves knowledge structures within learners’ memories, facilitating problem-solving accuracy and acquisition of knowledge. When students self-explain, they make sense of information in a variety of unique ways, ranging from simple restatements to multidimensional thoughts. Successful problem-solvers frequently use specific, high-quality self-explanation types. The unique types of self-explanation present among nurse practitioner (NP) student diagnosticians have yet to be explored. This study explores the question: How do NP students self-explain during diagnostic reasoning? Methods Thirty-seven Family NP students enrolled in the Doctor of Nursing Practice program at a large, Midwestern U.S. university diagnosed three written case studies while self-explaining. Dual methodology content analyses facilitated both deductive and qualitative descriptive analysis. Results Categories emerged describing the unique ways that NP student diagnosticians self-explain. Nine categories of inference self-explanations included clinical and biological foci. Eight categories of non-inference self-explanations monitored students’ understanding of clinical data and reflect shallow information processing. Conclusions Findings extend the understanding of self-explanation use during diagnostic reasoning by affording a glimpse into fine-grained knowledge structures of NP students. NP students apply both clinical and biological knowledge, actively improving immature knowledge structures. Future research should examine relationships between categories of self-explanation and markers of diagnostic success, a step in developing prompted self-explanation learning interventions.

scholarly journals Accuracy of State-Level Surveillance during Emerging Outbreaks of Respiratory Viruses: A Model-Based Assessment

2021 ◽  
pp. 0272989X2110222
Author(s):  
Yuwen Gu ◽  
Elise DeDoncker ◽  
Richard VanEnk ◽  
Rajib Paul ◽  
Susan Peters ◽  
...  
Keyword(s):  
Growth Rate ◽  
Data Collection ◽  
Disease Progression ◽  
Health Care Providers ◽  
Growth Rates ◽  
State Level ◽  
Future Research ◽  
Care Providers ◽  
State Surveillance ◽  
First Come First Serve

It is long perceived that the more data collection, the more knowledge emerges about the real disease progression. During emergencies like the H1N1 and the severe acute respiratory syndrome coronavirus 2 pandemics, public health surveillance requested increased testing to address the exacerbated demand. However, it is currently unknown how accurately surveillance portrays disease progression through incidence and confirmed case trends. State surveillance, unlike commercial testing, can process specimens based on the upcoming demand (e.g., with testing restrictions). Hence, proper assessment of accuracy may lead to improvements for a robust infrastructure. Using the H1N1 pandemic experience, we developed a simulation that models the true unobserved influenza incidence trend in the State of Michigan, as well as trends observed at different data collection points of the surveillance system. We calculated the growth rate, or speed at which each trend increases during the pandemic growth phase, and we performed statistical experiments to assess the biases (or differences) between growth rates of unobserved and observed trends. We highlight the following results: 1) emergency-driven high-risk perception increases reporting, which leads to reduction of biases in the growth rates; 2) the best predicted growth rates are those estimated from the trend of specimens submitted to the surveillance point that receives reports from a variety of health care providers; and 3) under several criteria to queue specimens for viral subtyping with limited capacity, the best-performing criterion was to queue first-come, first-serve restricted to specimens with higher hospitalization risk. Under this criterion, the lab released capacity to subtype specimens for each day in the trend, which reduced the growth rate bias the most compared to other queuing criteria. Future research should investigate additional restrictions to the queue.

scholarly journals Combining Virtual Reality and Organizational Neuroscience for Leadership Assessment

2021 ◽  
Vol 11 (13) ◽  
pp. 5956
Author(s):  
Elena Parra ◽  
Irene Alice Chicchi Giglioli ◽  
Jestine Philip ◽  
Lucia Amalia Carrasco-Ribelles ◽  
Javier Marín-Morales ◽  
...  
Keyword(s):  
Machine Learning ◽  
Eye Tracking ◽  
Ecological Validity ◽  
Objective Assessment ◽  
Leadership Skills ◽  
Three Dimensional ◽  
Machine Learning Techniques ◽  
Future Research ◽  
Promising Tool ◽  
Future Research Agenda

In this article, we introduce three-dimensional Serious Games (3DSGs) under an evidence-centered design (ECD) framework and use an organizational neuroscience-based eye-tracking measure to capture implicit behavioral signals associated with leadership skills. While ECD is a well-established framework used in the design and development of assessments, it has rarely been utilized in organizational research. The study proposes a novel 3DSG combined with organizational neuroscience methods as a promising tool to assess and recognize leadership-related behavioral patterns that manifest during complex and realistic social situations. We offer a research protocol for assessing task- and relationship-oriented leadership skills that uses ECD, eye-tracking measures, and machine learning. Seamlessly embedding biological measures into 3DSGs enables objective assessment methods that are based on machine learning techniques to achieve high ecological validity. We conclude by describing a future research agenda for the combined use of 3DSGs and organizational neuroscience methods for leadership and human resources.

scholarly journals Exploring the Social Networks’ Use in the Health-Care Industry: A Multi-Level Analysis

2021 ◽  
Vol 18 (14) ◽  
pp. 7295
Author(s):  
Tommasina Pianese ◽  
Patrizia Belfiore
Keyword(s):  
Health Care ◽  
Social Networks ◽  
Health Care Providers ◽  
Health Care Industry ◽  
Future Research ◽  
Great Level ◽  
Care Providers ◽  
Care Industry ◽  
Multi Level ◽  
Video And Audio

The application of social networks in the health domain has become increasingly prevalent. They are web-based technologies which bring together a group of people and health-care providers having in common health-related interests, who share text, image, video and audio contents and interact with each other. This explains the increasing amount of attention paid to this topic by researchers who have investigated a variety of issues dealing with the specific applications in the health-care industry. The aim of this study is to systematize this fragmented body of literature, and provide a comprehensive and multi-level overview of the studies that has been carried out to date on social network uses in healthcare, taking into account the great level of diversity that characterizes this industry. To this end, we conduct a scoping review enabling to identify the major research streams, whose aggregate knowledge are discussed according to three levels of analysis that reflect the viewpoints of the major actors using social networks for health-care purposes, i.e., governments, health-care providers (including health-care organizations and professionals) and social networks’ users (including ill patients and general public). We conclude by proposing directions for future research.

Association between religious beliefs and discussions regarding advance care planning: A nationwide survey

2021 ◽  
pp. 026921632110295
Author(s):  
Jun Miyashita ◽  
Sayaka Shimizu ◽  
Shunichi Fukuhara ◽  
Yosuke Yamamoto
Keyword(s):  
Health Care Providers ◽  
Advance Care Planning ◽  
Religious Beliefs ◽  
Nationwide Survey ◽  
Care Planning ◽  
Care Providers ◽  
Logistic Regression Models ◽  
Japanese Buddhism ◽  
Advance Care ◽  
Quota Sampling

Background: The relationship between advance care planning and religious beliefs, which are important for palliative care, is controversial in Western countries and has not been verified in Asian countries. Aim: To investigate the association between advance care planning discussions and religious beliefs in Japan. Design: A nationwide survey conducted in 2016 using a quota sampling method to obtain a representative sample of Japan’s general population. Setting/participants: We analyzed responses from 3167 adults aged 20–84 years (mean age ± standard deviation, 50.9 ± 16.8 years). The outcome was measured by asking whether the respondents had ever discussed advance care planning, and the main exposure by whether they had any religious beliefs or affiliations, and if so, their degree of devoutness. We analyzed religious beliefs, affiliations, and devoutness in relation to the occurrence of discussions using multivariable logistic regression models adjusted for possible sociodemographic covariates. Results: Compared with respondents without, those with religious beliefs had significantly higher odds of having had discussions (adjusted odds ratio: 1.45, 95% confidence interval: 1.22–1.73). The devoutness of religious belief was proportional to the propensity of the occurrence of discussions ( p for trend < 0.001). In addition, Buddhists and Christians had higher odds of having had discussions than did nonbelievers. Conclusion: The results suggest that holding religious beliefs, especially in Japanese Buddhism and Christianity, facilitates advance care planning discussions among Japanese adults, and thus, may help health-care providers identify those prioritized for facilitating engagement in advance care planning, especially in palliative and spiritual care settings.

Commentary on “Diabetes and people with learning disabilities: issues for policy, practice and education”

2020 ◽  
Vol 25 (1) ◽  
pp. 35-39 ◽  
Author(s):  
Brianne Redquest ◽  
Yona Lunsky
Keyword(s):  
Health Care ◽  
Learning Disabilities ◽  
Health Care Providers ◽  
Diabetes Care ◽  
Future Research ◽  
Care Providers ◽  
Family Carers ◽  
Content Type ◽  
Policy Practice

Purpose There has been an increase in research exploring the area of intellectual and developmental disabilities (IDD) and diabetes. Despite being described as instrumental to diabetes care for people with IDD, the role and experiences of family carers, such as parents and siblings, are often neglected in this research. However, it is clear that family carers do not feel that they have sufficient knowledge about diabetes. The purpose of this commentary is to extend the content from “Diabetes and people with learning disabilities: Issues for policy, practice, and education (Maine et al., 2020)” and discuss how family carers can feel better supported when caring for someone with IDD and diabetes. Design/methodology/approach This commentary discusses specific efforts such as STOP diabetes, DESMOND-ID and OK-diabetes for people with IDD including family carers. Encouragement is given for health care providers to recommend such programmes to people with IDD and their family carers. It is also suggested that health care providers involve family carers in diabetes care planning and implementation for people with IDD. Findings It is hoped that if changes are made to current diabetes practices and more research with family carers is conducted, diabetes prevention and management for people with IDD will be more successful and family carers can feel more confident in providing support to their loved ones. Originality/value Research exploring the role of family carers in diabetes care for people with IDD and diabetes is very limited. This commentary makes recommendations to help family carers feel better supported in their role. It also provides areas for future research.

A Scoping Review and Content Analysis of Common Depressive Symptoms of Young People

2021 ◽  
pp. 105984052110126
Author(s):  
Jia-Wen Guo ◽  
Brooks R. Keeshin ◽  
Mike Conway ◽  
Wendy W. Chapman ◽  
Katherine A. Sward
Keyword(s):  
Content Analysis ◽  
Depressive Symptoms ◽  
Young People ◽  
Health Care Providers ◽  
Scoping Review ◽  
School Nurses ◽  
Future Research ◽  
Depression Symptom ◽  
Care Providers ◽  
Five Dimensions

School nurses are the most accessible health care providers for many young people including adolescents and young adults. Early identification of depression results in improved outcomes, but little information is available comprehensively describing depressive symptoms specific to this population. The aim of this study was to develop a taxonomy of depressive symptoms that were manifested and described by young people based on a scoping review and content analysis. Twenty-five journal articles that included narrative descriptions of depressive symptoms in young people were included. A total of 60 depressive symptoms were identified and categorized into five dimensions: behavioral ( n = 8), cognitive ( n = 14), emotional ( n = 15), interpersonal ( n = 13), and somatic ( n = 10). This comprehensive depression symptom taxonomy can help school nurses to identify young people who may experience depression and will support future research to better screen for depression.

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