Catalogue Search | MBRL
Search Results Heading
Explore the vast range of titles available.
MBRLSearchResults
-
DisciplineDiscipline
-
Is Peer ReviewedIs Peer Reviewed
-
Series TitleSeries Title
-
Reading LevelReading Level
-
YearFrom:-To:
-
More FiltersMore FiltersContent TypeItem TypeIs Full-Text AvailableSubjectCountry Of PublicationPublisherSourceDonorLanguagePlace of PublicationContributorsLocation
Done
Filters
Reset
9,449
result(s) for
"Anderson, Nick"
Sort by:
Clockwork lives
by
Anderson, Kevin J., 1962- author
,
Peart, Neil, author
,
Robles, Nick, illustrator
in
Fathers and daughters Fiction.
,
Inventors Fiction.
,
Alchemy Fiction.
2015
Marinda Peake is a woman with a quiet, perfect life in a small village; she long ago gave up on her dreams and ambitions to take care of her ailing father, an alchemist and an inventor. When he dies, he gives Marinda a mysterious inheritance: a blank book that she must fill with other people's stories-- and ultimately her own.
Exploring the deep learning of artificial intelligence in nursing: a concept analysis with Walker and Avant’s approach
by
Lininger, Jiraporn
,
Wangpitipanit, Supichaya
,
Anderson, Nick
in
Algorithms
,
Analysis
,
Artificial intelligence
2024
Background
In recent years, increased attention has been given to using deep learning (DL) of artificial intelligence (AI) in healthcare to address nursing challenges. The adoption of new technologies in nursing needs to be improved, and AI in nursing is still in its early stages. However, the current literature needs more clarity, which affects clinical practice, research, and theory development. This study aimed to clarify the meaning of deep learning and identify the defining attributes of artificial intelligence within nursing.
Methods
We conducted a concept analysis of the deep learning of AI in nursing care using Walker and Avant’s 8-step approach. Our search strategy employed Boolean techniques and MeSH terms across databases, including BMC, CINAHL, ClinicalKey for Nursing, Embase, Ovid, Scopus, SpringerLink and Spinger Nature, ProQuest, PubMed, and Web of Science. By focusing on relevant keywords in titles and abstracts from articles published between 2018 and 2024, we initially found 571 sources.
Results
Thirty-seven articles that met the inclusion criteria were analyzed in this study. The attributes of evidence included four themes: focus and immersion, coding and understanding, arranging layers and algorithms, and implementing within the process of use cases to modify recommendations. Antecedents, unclear systems and communication, insufficient data management knowledge and support, and compound challenges can lead to suffering and risky caregiving tasks. Applying deep learning techniques enables nurses to simulate scenarios, predict outcomes, and plan care more precisely. Embracing deep learning equipment allows nurses to make better decisions. It empowers them with enhanced knowledge while ensuring adequate support and resources essential for caregiver and patient well-being. Access to necessary equipment is vital for high-quality home healthcare.
Conclusion
This study provides a clearer understanding of the use of deep learning in nursing and its implications for nursing practice. Future research should focus on exploring the impact of deep learning on healthcare operations management through quantitative and qualitative studies. Additionally, developing a framework to guide the integration of deep learning into nursing practice is recommended to facilitate its adoption and implementation.
Journal Article
Personalized Telehealth in the Future: A Global Research Agenda
by
Young, Heather M
,
Dinesen, Birthe
,
Jethwani, Kamal
in
Adoption of innovations
,
Agenda
,
Best practice
2016
As telehealth plays an even greater role in global health care delivery, it will be increasingly important to develop a strong evidence base of successful, innovative telehealth solutions that can lead to scalable and sustainable telehealth programs. This paper has two aims: (1) to describe the challenges of promoting telehealth implementation to advance adoption and (2) to present a global research agenda for personalized telehealth within chronic disease management. Using evidence from the United States and the European Union, this paper provides a global overview of the current state of telehealth services and benefits, presents fundamental principles that must be addressed to advance the status quo, and provides a framework for current and future research initiatives within telehealth for personalized care, treatment, and prevention. A broad, multinational research agenda can provide a uniform framework for identifying and rapidly replicating best practices, while concurrently fostering global collaboration in the development and rigorous testing of new and emerging telehealth technologies. In this paper, the members of the Transatlantic Telehealth Research Network offer a 12-point research agenda for future telehealth applications within chronic disease management.
Journal Article
Clinical validation of an AI-based pathology tool for scoring of metabolic dysfunction-associated steatohepatitis
by
Baxi, Vipul
,
Sejling, Anne-Sophie
,
Chung, Chuhan
in
631/114/1305
,
631/154/53/2421
,
692/308/153
2025
Metabolic dysfunction-associated steatohepatitis (MASH) is a major cause of liver-related morbidity and mortality, yet treatment options are limited. Manual scoring of liver biopsies, currently the gold standard for clinical trial enrollment and endpoint assessment, suffers from high reader variability. This study represents the most comprehensive multisite analytical and clinical validation of an artificial intelligence (AI)-based pathology system, AI-based measurement of metabolic dysfunction-associated steatohepatitis (AIM-MASH), to assist pathologists in MASH trial histology scoring. AIM-MASH demonstrated high repeatability and reproducibility compared to manual scoring. AIM-MASH-assisted reads by expert MASH pathologists were superior to unassisted reads in accurately assessing inflammation, ballooning, MAS ≥ 4 with ≥1 in each score category and MASH resolution, while maintaining non-inferiority in steatosis and fibrosis assessment. These findings suggest that AIM-MASH could mitigate reader variability, providing a more reliable assessment of therapeutics in MASH clinical trials.
In a prospective, regulatory-grade study of assistance to pathologists in MASH histology scoring, AIM-MASH-assisted reads by expert MASH pathologists were superior to unassisted reads and decreased inter-reader variability.
Journal Article
Superman Action Comics : the Oz effect
\"Shrouded in mystery for years, the puppetmaster known as Mr. Oz has finally shown his hand. His agents have begun to move as the Man of Steel works to stop the chaos they unleash in Metropolis and across the globe. But when Mr. Oz steps from the shadows, his identity rocks the Last Son of Krypton to his core. Who is he? The answer will change Superman forever. A mystery that has weaved through the pages of DC UNIVERSE: REBIRTH, DETECTIVE COMICS, ACTION COMICS and even Geoff Johns' SUPERMAN: THE MEN OF TOMORROW, is finally resolved here in SUPERMAN - ACTION COMICS: THE OZ EFFECT! Written by legendary scribe Dan Jurgens and illustrated by a team of superstar artists led by Ryan Sook and Viktor Bogdonavic, this graphic novel features a lenticular motion cover only available in the first print run!\"-- Provided by publisher.
From patients to partners: participant-centric initiatives in biomedical research
2012
Participant-centred initiatives use social media technologies to allow long-term interactive partnerships to be established between study participants and researchers. These varied initiatives improve research governance and quality and give participants greater knowledge of and control over how their data are used.
Advances in computing technology and bioinformatics mean that medical research is increasingly characterized by large international consortia of researchers that are reliant on large data sets and biobanks. These trends raise a number of challenges for obtaining consent, protecting participant privacy concerns and maintaining public trust. Participant-centred initiatives (PCIs) use social media technologies to address these immediate concerns, but they also provide the basis for long-term interactive partnerships. Here, we give an overview of this rapidly moving field by providing an analysis of the different PCI approaches, as well as the benefits and challenges of implementing PCIs.
Journal Article
My46: a Web-based tool for self-guided management of genomic test results in research and clinical settings
by
Yu, Joon-Ho
,
Crouch, Julia M.
,
Futral, Brett T.
in
631/114
,
631/1647/514
,
692/700/228/2050/1512
2017
A major challenge to implementing precision medicine is the need for an efficient and cost-effective strategy for returning individual genomic test results that is easily scalable and can be incorporated into multiple models of clinical practice. My46 is a Web-based tool for managing the return of genetic results that was designed and developed to support a wide range of approaches to disclosing results, ranging from traditional face-to-face disclosure to self-guided models. My46 has five key functions: set and modify results-return preferences, return results, educate, manage the return of results, and assess the return of results. These key functions are supported by six distinct modules and a suite of features that enhance the user experience, ease site navigation, facilitate knowledge sharing, and enable results-return tracking. My46 is a potentially effective solution for returning results and supports current trends toward shared decision making between patients and providers and patient-driven health management.
Genet Med19 4, 467–475.
Journal Article
AI-PACE: A Framework for Integrating AI into Medical Education
by
Wang, Haibo
,
Johl, Karnjit
,
McGrath, Scott P
in
Artificial intelligence
,
Education
,
Health care
2026
The integration of artificial intelligence (AI) into healthcare is accelerating, yet medical education has not kept pace with these technological advancements. This paper synthesizes current knowledge on AI in medical education through a comprehensive analysis of the literature, identifying key competencies, curricular approaches, and implementation strategies. The aim is highlighting the critical need for structured AI education across the medical learning continuum and offer a framework for curriculum development. The findings presented suggest that effective AI education requires longitudinal integration throughout medical training, interdisciplinary collaboration, and balanced attention to both technical fundamentals and clinical applications. This paper serves as a foundation for medical educators seeking to prepare future physicians for an AI-enhanced healthcare environment.