[HTML][HTML] Surgical data science–from concepts toward clinical translation
Recent developments in data science in general and machine learning in particular have
transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a …
transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a …
Mobile health in remote patient monitoring for chronic diseases: Principles, trends, and challenges
Chronic diseases are becoming more widespread. Treatment and monitoring of these
diseases require going to hospitals frequently, which increases the burdens of hospitals and …
diseases require going to hospitals frequently, which increases the burdens of hospitals and …
Early prediction of circulatory failure in the intensive care unit using machine learning
Intensive-care clinicians are presented with large quantities of measurements from multiple
monitoring systems. The limited ability of humans to process complex information hinders …
monitoring systems. The limited ability of humans to process complex information hinders …
Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic …
HC Thorsen-Meyer, AB Nielsen, AP Nielsen… - The Lancet Digital …, 2020 - thelancet.com
Background Many mortality prediction models have been developed for patients in intensive
care units (ICUs); most are based on data available at ICU admission. We investigated …
care units (ICUs); most are based on data available at ICU admission. We investigated …
[HTML][HTML] Real-world integration of a sepsis deep learning technology into routine clinical care: implementation study
MP Sendak, W Ratliff, D Sarro, E Alderton… - JMIR medical …, 2020 - medinform.jmir.org
Background: Successful integrations of machine learning into routine clinical care are
exceedingly rare, and barriers to its adoption are poorly characterized in the literature …
exceedingly rare, and barriers to its adoption are poorly characterized in the literature …
Operationalising AI ethics through the agile software development lifecycle: a case study of AI-enabled mobile health applications
Although numerous ethical principles and guidelines have been proposed to guide the
development of artificial intelligence (AI) systems, it has proven difficult to translate these …
development of artificial intelligence (AI) systems, it has proven difficult to translate these …
Deep-learning-based real-time prediction of acute kidney injury outperforms human predictive performance
N Rank, B Pfahringer, J Kempfert, C Stamm… - NPJ digital …, 2020 - nature.com
Acute kidney injury (AKI) is a major complication after cardiothoracic surgery. Early
prediction of AKI could prompt preventive measures, but is challenging in the clinical routine …
prediction of AKI could prompt preventive measures, but is challenging in the clinical routine …
Explainable artificial intelligence to detect atrial fibrillation using electrocardiogram
Introduction Early detection and intervention of atrial fibrillation (AF) is a cornerstone for
effective treatment and prevention of mortality. Diverse deep learning models (DLMs) have …
effective treatment and prevention of mortality. Diverse deep learning models (DLMs) have …
Application of artificial intelligence in surgery
Artificial intelligence (AI) is gradually changing the practice of surgery with technological
advancements in imaging, navigation, and robotic intervention. In this article, we review the …
advancements in imaging, navigation, and robotic intervention. In this article, we review the …
Use of machine learning to develop and evaluate models using preoperative and intraoperative data to identify risks of postoperative complications
Importance Postoperative complications can significantly impact perioperative care
management and planning. Objectives To assess machine learning (ML) models for …
management and planning. Objectives To assess machine learning (ML) models for …