scholarly journals Using automation to produce a ‘living map’ of the COVID-19 research literature

2021 ◽  
Vol 17 (2) ◽  
pp. 11-15
Author(s):  
Ian Shemilt ◽  
Anneliese Arno ◽  
James Thomas ◽  
Theo Lorenc ◽  
Claire C Khouja ◽  
...  
Keyword(s):  
Machine Learning ◽  
Research Evidence ◽  
Research Literature ◽  
Relevant Research ◽  
Automation Systems

The COVID-19 pandemic has disrupted life worldwide and presented unique challenges in the health evidencesynthesis space. The urgent nature of the pandemic required extreme rapidity for keeping track of research, andthis presented a unique opportunity for long-proposed automation systems to be deployed and evaluated. Wecompared the use of novel automation technologies with conventional manual screening; and Microsoft AcademicGraph (MAG) with the MEDLINE and Embase databases locating the emerging research evidence. We foundthat a new workflow involving machine learning to identify relevant research in MAG achieved a much higherrecall with lower manual effort than using conventional approaches.

Using Research Evidence in Planning Treatment for the Individual Client

1998 ◽  
Vol 65 (3) ◽  
pp. 152-159 ◽  
Author(s):  
Linda Tickle-Degnen
Keyword(s):  
Treatment Planning ◽  
Research Evidence ◽  
Research Literature ◽  
Relevant Research ◽  
Timely Manner ◽  
Planning Treatment ◽  
Individual Client ◽  
Clinical Questions ◽  
The Individual ◽  
Research Studies

The purpose of this paper is to describe strategies for retrieving and using evidence published in the research literature for application to treatment planning with a particular client. Suggestions are given for how to develop clinical questions about a particular client for use in guiding literature retrieval, how to retrieve the most relevant research studies in a timely manner, how to interpret results of these studies for use with a single client, and how to communicate the results of these studies with clients and families in order to engage in collaborative treatment planning.

Physical Therapists’ Experiences Updating the Clinical Management of Walking Rehabilitation After Stroke: A Qualitative Study

2009 ◽  
Vol 89 (6) ◽  
pp. 556-568 ◽  
Author(s):  
Nancy M. Salbach ◽  
Paula Veinot ◽  
Susan Rappolt ◽  
Mark Bayley ◽  
Dawn Burnett ◽  
...  
Keyword(s):  
Clinical Management ◽  
Research Evidence ◽  
Physical Therapists ◽  
Research Literature ◽  
Comparative Approach ◽  
Sources Of Information ◽  
Efficient Access ◽  
Research Findings ◽  
Walking Rehabilitation ◽  
Ease Of Access

Background: Little is known about physical therapists’ experiences using research evidence to improve the delivery of stroke rehabilitation. Objectives: The purpose of this study was to explore how physical therapists use research evidence to update the clinical management of walking rehabilitation after stroke. Specific objectives were to identify physical therapists’ clinical questions related to walking rehabilitation, sources of information sought to address these questions, and factors influencing the incorporation of research evidence into practice. Design and Methods: Two authors conducted in-depth telephone interviews with 23 physical therapists who treat people with stroke and who had participated in a previous survey on evidence-based practice. Data were analyzed with a constant comparative approach to identify emerging themes. Results: Therapists commonly raised questions about the selection of treatments or outcome measures. Therapists relied foremost on peers for information because of their availability, ease of access, and minimal cost. Participants sought information from research literature themselves or with the help of librarians or students. Research syntheses (eg, systematic reviews) enabled access to a body of research. Older therapists described insufficient computer and search skills. Most participants considered appraisal and application of research findings challenging and identified insufficient time and peer isolation as organizational barriers to the use of research. Conclusions: Physical therapists require efficient access to research syntheses primarily to inform the measurement and treatment of walking limitation after stroke. Continuing education is needed to enhance skills in appraising research findings and applying them to practice. Older therapists require additional training to develop computer and search skills. Peer networks and student internships may optimize the exchange of new knowledge for therapists working in isolation.

scholarly journals User involvement in adolescents’ mental healthcare: protocol for a systematic review

BMJ Open ◽  
2017 ◽  
Vol 7 (12) ◽  
pp. e018800
Author(s):  
Petter Viksveen ◽  
Stig Erlend Bjønness ◽  
Siv Hilde Berg ◽  
Nicole Elizabeth Cardenas ◽  
Julia Rose Game ◽  
...  
Keyword(s):  
Systematic Review ◽  
Critical Appraisal ◽  
Research Evidence ◽  
Data Extraction ◽  
Mental Healthcare ◽  
User Involvement ◽  
Cochrane Collaboration ◽  
Research Literature ◽  
Primary Data ◽  
Evaluation Approach

IntroductionUser involvement has become a growing importance in healthcare. The United Nations state that adolescents have a right to be heard, and user involvement in healthcare is a legal right in many countries. Some research provides an insight into the field of user involvement in somatic and mental healthcare for adults, but little is known about user involvement in adolescents’ mental healthcare, and no overview of the existing research evidence exists.Methods and analysisThe aim of this systematic review is to provide an overview of existing research reporting on experiences with and the effectiveness and safety issues associated with user involvement for adolescents’ mental healthcare at the individual and organisational level. A systematic literature search and assessment of published research in the field of user involvement in adolescents’ mental healthcare will be carried out. Established guidelines will be used for data extraction (Cochrane Collaboration guidelines, Strengthening the Reporting of Observational studies in Epidemiology and Critical Appraisal Skills Programme (CASP)), critical appraisal (Cochrane Collaboration guidelines and Pragmatic-Explanatory Continuum Indicator Summary) and reporting of results (Preferred Reporting Items for Systematic reviews and Meta-Analyses, Consolidated Standards of Reporting Trials and CASP). Confidence in the research evidence will be assessed using the Grading of Recommendations Assessment, Development and Evaluation approach. Adolescents are included as coresearchers for the planning and carrying out of this systematic review. This systematic review will provide an overview of the existing research literature and thereby fill a knowledge gap. It may provide various stakeholders, including decision-makers, professionals, individuals and their families, with an overview of existing knowledge in an underexplored field of research.Ethics and disseminationEthics approval is not required for this systematic review as we are not collecting primary data. The results will be published in a peer-reviewed journal and at conference presentations and will be shared with stakeholder groups.

scholarly journals Machine Learning for Biometrics

2012 ◽  
pp. 704-723
Author(s):  
Albert Ali Salah
Keyword(s):  
Machine Learning ◽  
Learning Approaches ◽  
Relevant Research ◽  
Robust Identification ◽  
Research Issues ◽  
Personal Traits ◽  
Machine Learning Methods ◽  
Biometric Template ◽  
Fusion Methods ◽  
Areas Of Interest

Biometrics aims at reliable and robust identification of humans from their personal traits, mainly for security and authentication purposes, but also for identifying and tracking the users of smarter applications. Frequently considered modalities are fingerprint, face, iris, palmprint and voice, but there are many other possible biometrics, including gait, ear image, retina, DNA, and even behaviours. This chapter presents a survey of machine learning methods used for biometrics applications, and identifies relevant research issues. The author focuses on three areas of interest: offline methods for biometric template construction and recognition, information fusion methods for integrating multiple biometrics to obtain robust results, and methods for dealing with temporal information. By introducing exemplary and influential machine learning approaches in the context of specific biometrics applications, the author hopes to provide the reader with the means to create novel machine learning solutions to challenging biometrics problems.

scholarly journals Machine Learning for Landslides Prevention: A Survey

2020 ◽  
Author(s):  
Zhengjing Ma ◽  
Gang Mei
Keyword(s):  
Machine Learning ◽  
State Of The Art ◽  
Human Life ◽  
Machine Learning Algorithms ◽  
Warning Systems ◽  
Negative Effects ◽  
Relevant Research ◽  
Related Information ◽  
Urgent Task ◽  
Comprehensive Survey

Landslides are one of the most critical categories of natural disasters worldwide and induce severely destructive outcomes to human life and the overall economic system. To reduce its negative effects, landslides prevention has become an urgent task, which includes investigating landslide-related information and predicting potential landslides. Machine learning is a state-of-the-art analytics tool that has been widely used in landslides prevention. This paper presents a comprehensive survey of relevant research on machine learning applied in landslides prevention, mainly focusing on (1) landslides detection based on images, (2) landslides susceptibility assessment, and (3) the development of landslide warning systems. Moreover, this paper discusses the current challenges and potential opportunities in the application of machine learning algorithms for landslides prevention.

A Literature Review on Thyroid Hormonal Problems in Women Using Data Science and Analytics

2021 ◽  
pp. 416-428
Author(s):  
R. Suganya ◽  
Rajaram S. ◽  
Kameswari M.
Keyword(s):  
Machine Learning ◽  
Literature Review ◽  
Data Science ◽  
Learning Algorithms ◽  
Research Literature ◽  
Machine Learning Algorithms ◽  
Thyroid Disorder ◽  
Classification Models ◽  
Indian Women ◽  
Using Data

Currently, thyroid disorders are more common and widespread among women worldwide. In India, seven out of ten women are suffering from thyroid problems. Various research literature studies predict that about 35% of Indian women are examined with prevalent goiter. It is very necessary to take preventive measures at its early stages, otherwise it causes infertility problem among women. The recent review discusses various analytics models that are used to handle different types of thyroid problems in women. This chapter is planned to analyze and compare different classification models, both machine learning algorithms and deep leaning algorithms, to classify different thyroid problems. Literature from both machine learning and deep learning algorithms is considered. This literature review on thyroid problems will help to analyze the reason and characteristics of thyroid disorder. The dataset used to build and to validate the algorithms was provided by UCI machine learning repository.

Hypnosis and relaxation

2013 ◽  
pp. 560-568 ◽  
Author(s):  
Christina Liossi ◽  
Leora Kuttner ◽  
Chantal Wood ◽  
Lonnie K. Zeltzer
Keyword(s):  
Chronic Pain ◽  
Clinical Practice ◽  
Pain Management ◽  
Research Evidence ◽  
Research Literature ◽  
Current Understanding ◽  
Relaxation Techniques ◽  
Chronic Pain Management ◽  
Future Studies ◽  
Hypnotic Analgesia

This chapter discusses the current research literature and clinical practice regarding the use of hypnosis in paediatric pain management, first defining hypnosis and discussing theoretical conceptualizations. Next it presents our current understanding of the mechanisms of hypnotic analgesia, along with the research evidence for the efficacy of hypnosis in the control of acute and chronic paediatric pain; in both sections relevant clinical techniques are discussed. It also includes a description and discussion of different relaxation techniques and the evidence for their efficacy in acute and chronic pain management, and concludes with an attempt to summarize and evaluate the existing literature and make suggestions for future studies and clinical practice.

scholarly journals Identifying Social Care Research Literature: Case Studies From Guideline Development

2017 ◽  
Vol 12 (3) ◽  
pp. 114
Author(s):  
Claire Stansfield ◽  
Kristin Liabo
Keyword(s):  
Research Evidence ◽  
Social Care ◽  
Guideline Development ◽  
Research Literature ◽  
Database Searching ◽  
Bibliographic Database ◽  
Major Topic ◽  
Care Research ◽  
Service Environment ◽  
Search Terms

Abstract Objective – Systematic searching is central to guideline development, yet guidelines in social care present a challenge to systematic searching because they exist within a highly complex policy and service environment. The objective of this study was to highlight challenges and inform practice on identifying social care research literature, drawing on experiences from guideline development in social care. Methods – The researchers reflected on the approaches to searching for research evidence to inform three guidelines. They evaluated the utility of major topic-focused bibliographic database sources through a) determining the yield of citations from the search strategies for two guidelines and b) identifying which databases contain the citations for three guidelines. The researchers also considered the proportion of different study types and their presence in certain databases. Results – There were variations in the ability of the search terms to capture the studies from individual databases, even with low-precision searches. These were mitigated by searching a combination of databases and other resources that were specific to individual topics. A combination of eight databases was important for finding literature for the included topics. Multiple database searching also mitigates the currency of content, topic and study design focus, and consistency of indexing within individual databases. Conclusion – Systematic searching for research evidence in social care requires considerable thought and development so that the search is fit for the particular purpose of supporting guidelines. This study highlights key challenges and reveals trends when utilising some commonly used databases.

Apprenticeship and empowerment

2017 ◽  
Author(s):  
Helena Gaunt
Keyword(s):  
Communities Of Practice ◽  
Conceptual Framework ◽  
Peer Interactions ◽  
Research Literature ◽  
Creative Performance ◽  
Relevant Research ◽  
Collaborative Process ◽  
Socially Constructed ◽  
One To One ◽  
Classical Musicians

This chapter considers ways in which pathways to creative performance are supported through one-to-one lessons between a student and a specialist teacher. One-to-one interactions are generally considered central to the development of western classical musicians and traditionally have been conceived in terms of apprenticeship. More recently, however, understanding of the socially constructed nature of learning, including the essential parts played by peer interactions and engagement in communities of practice, has increased. In addition, the importance of collaboration in facilitating and channelling creativity in many fields has become apparent. Taken together, these suggest a need to develop a multifaceted and more nuanced conceptual framework for understanding one-to-one lessons and their relationship to performance. The chapter explores relevant research literature alongside perspectives provided by expert performer–teachers, and it concludes by setting out a provisional model for understanding both the collaborative process between student and teacher in one-to-one lessons and the potential for this context to underpin transformative processes of development for performers.

scholarly journals HEMS-IoT: A Big Data and Machine Learning-Based Smart Home System for Energy Saving

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1097 ◽  
Author(s):  
Isaac Machorro-Cano ◽  
Giner Alor-Hernández ◽  
Mario Andrés Paredes-Valverde ◽  
Lisbeth Rodríguez-Mazahua ◽  
José Luis Sánchez-Cervantes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
Big Data ◽  
Energy Consumption ◽  
Energy Saving ◽  
Smart Home ◽  
User Preferences ◽  
The Internet ◽  
Automation Systems ◽  
Home Energy Management

Energy efficiency has aroused great interest in research worldwide, because energy consumption has increased in recent years, especially in the residential sector. The advances in energy conversion, along with new forms of communication, and information technologies have paved the way for what is now known as smart homes. The Internet of Things (IoT) is the convergence of various heterogeneous technologies from different application domains that are used to interconnect things through the Internet, thus allowing for the detection, monitoring, and remote control of multiple devices. Home automation systems (HAS) combined with IoT, big data technologies, and machine learning are alternatives that promise to contribute to greater energy efficiency. This work presents HEMS-IoT, a big data and machine learning-based smart home energy management system for home comfort, safety, and energy saving. We used the J48 machine learning algorithm and Weka API to learn user behaviors and energy consumption patterns and classify houses with respect to energy consumption. Likewise, we relied on RuleML and Apache Mahout to generate energy-saving recommendations based on user preferences to preserve smart home comfort and safety. To validate our system, we present a case study where we monitor a smart home to ensure comfort and safety and reduce energy consumption.

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