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Artificial intelligence for clinical decision support in sepsis
M Wu, X Du, R Gu, J Wei - Frontiers in Medicine, 2021 - frontiersin.org
Sepsis is one of the main causes of death in critically ill patients. Despite the continuous
development of medical technology in recent years, its morbidity and mortality are still high …
development of medical technology in recent years, its morbidity and mortality are still high …
[HTML][HTML] Artificial intelligence in perioperative medicine: a narrative review
Recent advancements in artificial intelligence (AI) techniques have enabled the
development of accurate prediction models using clinical big data. AI models for …
development of accurate prediction models using clinical big data. AI models for …
Machine learning model to predict mental health crises from electronic health records
R Garriga, J Mas, S Abraha, J Nolan, O Harrison… - Nature medicine, 2022 - nature.com
The timely identification of patients who are at risk of a mental health crisis can lead to
improved outcomes and to the mitigation of burdens and costs. However, the high …
improved outcomes and to the mitigation of burdens and costs. However, the high …
Sepsis prediction in intensive care unit based on genetic feature optimization and stacked deep ensemble learning
Sepsis is a life-threatening disease that is associated with organ dysfunction. It occurs due to
the body's dysregulated response to infection. It is difficult to identify sepsis in its early …
the body's dysregulated response to infection. It is difficult to identify sepsis in its early …
RadioLOGIC, a healthcare model for processing electronic health records and decision-making in breast disease
Digital health data used in diagnostics, patient care, and oncology research continue to
accumulate exponentially. Most medical information, and particularly radiology results, are …
accumulate exponentially. Most medical information, and particularly radiology results, are …
Interpretable machine-learning model for real-time, clustered risk factor analysis of sepsis and septic death in critical care
Z Jiang, L Bo, L Wang, Y ** review of machine learning for sepsis prediction-feature engineering strategies and model performance: a step towards explainability
Background Sepsis, an acute and potentially fatal systemic response to infection,
significantly impacts global health by affecting millions annually. Prompt identification of …
significantly impacts global health by affecting millions annually. Prompt identification of …
An adaptive federated learning framework for clinical risk prediction with electronic health records from multiple hospitals
Clinical risk prediction with electronic health records (EHR) using machine learning has
attracted lots of attentions in recent years, where one of the key challenges is how to protect …
attracted lots of attentions in recent years, where one of the key challenges is how to protect …
Interpretable Machine Learning to Optimize Early In‐Hospital Mortality Prediction for Elderly Patients with Sepsis: A Discovery Study
X Ke, F Zhang, G Huang, A Wang - … and Mathematical Methods …, 2022 - Wiley Online Library
Sepsis‐related mortality rates are high among elderly patients, especially those in intensive
care units (ICUs). Early prediction of the prognosis of sepsis is critical, as prompt and …
care units (ICUs). Early prediction of the prognosis of sepsis is critical, as prompt and …
Transatlantic transferability and replicability of machine-learning algorithms to predict mental health crises
Transferring and replicating predictive algorithms across healthcare systems constitutes a
unique yet crucial challenge that needs to be addressed to enable the widespread adoption …
unique yet crucial challenge that needs to be addressed to enable the widespread adoption …