[HTML][HTML] Shifting machine learning for healthcare from development to deployment and from models to data
In the past decade, the application of machine learning (ML) to healthcare has helped drive
the automation of physician tasks as well as enhancements in clinical capabilities and …
the automation of physician tasks as well as enhancements in clinical capabilities and …
Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine
Precision medicine is one of the recent and powerful developments in medical care, which
has the potential to improve the traditional symptom-driven practice of medicine, allowing …
has the potential to improve the traditional symptom-driven practice of medicine, allowing …
Capabilities of gpt-4 on medical challenge problems
H Nori, N King, SM McKinney, D Carignan… - arXiv preprint arXiv …, 2023 - arxiv.org
Large language models (LLMs) have demonstrated remarkable capabilities in natural
language understanding and generation across various domains, including medicine. We …
language understanding and generation across various domains, including medicine. We …
[HTML][HTML] Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis
Early recognition and treatment of sepsis are linked to improved patient outcomes. Machine
learning-based early warning systems may reduce the time to recognition, but few systems …
learning-based early warning systems may reduce the time to recognition, but few systems …
Toward causal representation learning
The two fields of machine learning and graphical causality arose and are developed
separately. However, there is, now, cross-pollination and increasing interest in both fields to …
separately. However, there is, now, cross-pollination and increasing interest in both fields to …
[HTML][HTML] Human–machine teaming is key to AI adoption: clinicians' experiences with a deployed machine learning system
KE Henry, R Kornfield, A Sridharan, RC Linton… - NPJ digital …, 2022 - nature.com
While a growing number of machine learning (ML) systems have been deployed in clinical
settings with the promise of improving patient care, many have struggled to gain adoption …
settings with the promise of improving patient care, many have struggled to gain adoption …
Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist
Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist |
Nature Medicine Skip to main content Thank you for visiting nature.com. You are using a …
Nature Medicine Skip to main content Thank you for visiting nature.com. You are using a …
[HTML][HTML] Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare
Sepsis is a leading cause of death in hospitals. Early prediction and diagnosis of sepsis,
which is critical in reducing mortality, is challenging as many of its signs and symptoms are …
which is critical in reducing mortality, is challenging as many of its signs and symptoms are …
Big data and machine learning algorithms for health-care delivery
KY Ngiam, W Khor - The Lancet Oncology, 2019 - thelancet.com
Analysis of big data by machine learning offers considerable advantages for assimilation
and evaluation of large amounts of complex health-care data. However, to effectively use …
and evaluation of large amounts of complex health-care data. However, to effectively use …
A clinically applicable approach to continuous prediction of future acute kidney injury
The early prediction of deterioration could have an important role in supporting healthcare
professionals, as an estimated 11% of deaths in hospital follow a failure to promptly …
professionals, as an estimated 11% of deaths in hospital follow a failure to promptly …