Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review
S Lalmuanawma, J Hussain, L Chhakchhuak - Chaos, Solitons & Fractals, 2020 - Elsevier
Background and objective During the recent global urgency, scientists, clinicians, and
healthcare experts around the globe keep on searching for a new technology to support in …
healthcare experts around the globe keep on searching for a new technology to support in …
[HTML][HTML] Using machine learning for healthcare challenges and opportunities
A Alanazi - Informatics in Medicine Unlocked, 2022 - Elsevier
Abstract Machine learning (ML) and its applications in healthcare have gained a lot of
attention. When enhanced computational power is combined with big data, there is an …
attention. When enhanced computational power is combined with big data, there is an …
Artificial intelligence (AI)-enabled CRM capability in healthcare: The impact on service innovation
Although AI-enabled customer relationship management (CRM) systems have gained
momentum in healthcare to enhance performance, there is a striking dearth of knowledge on …
momentum in healthcare to enhance performance, there is a striking dearth of knowledge on …
[HTML][HTML] Artificial intelligence applications in health care practice: scoping review
Background Artificial intelligence (AI) is often heralded as a potential disruptor that will
transform the practice of medicine. The amount of data collected and available in health …
transform the practice of medicine. The amount of data collected and available in health …
Nosocomial infection
MH Kollef, A Torres, AF Shorr… - Critical care …, 2021 - journals.lww.com
Objective: The first 70 years of critical care can be considered a period of “industrial
revolution-like” advancement in terms of progressing the understanding and care of critical …
revolution-like” advancement in terms of progressing the understanding and care of critical …
A machine learning algorithm to analyse the effects of vaccination on COVID-19 mortality
The coronavirus disease 2019 (COVID-19), with new variants, continues to be a constant
pandemic threat that is generating socio-economic and health issues in manifold countries …
pandemic threat that is generating socio-economic and health issues in manifold countries …
[HTML][HTML] Optimising antimicrobial use in humans–review of current evidence and an interdisciplinary consensus on key priorities for research
Addressing the silent pandemic of antimicrobial resistance (AMR) is a focus of the 2021 G7
meeting. A major driver of AMR and poor clinical outcomes is suboptimal antimicrobial use …
meeting. A major driver of AMR and poor clinical outcomes is suboptimal antimicrobial use …
A review of artificial intelligence applications for antimicrobial resistance
J Lv, S Deng, L Zhang - Biosafety and Health, 2021 - mednexus.org
The wide use and abuse of antibiotics could make antimicrobial resistance (AMR) an
increasingly serious issue that threatens global health and imposes an enormous burden on …
increasingly serious issue that threatens global health and imposes an enormous burden on …
Sepsis prediction, early detection, and identification using clinical text for machine learning: a systematic review
Objective To determine the effects of using unstructured clinical text in machine learning
(ML) for prediction, early detection, and identification of sepsis. Materials and methods …
(ML) for prediction, early detection, and identification of sepsis. Materials and methods …
Applications of machine learning to the problem of antimicrobial resistance: an emerging model for translational research
Antimicrobial resistance (AMR) remains one of the most challenging phenomena of modern
medicine. Machine learning (ML) is a subfield of artificial intelligence that focuses on the …
medicine. Machine learning (ML) is a subfield of artificial intelligence that focuses on the …