AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Intensive Care Units

Showing 331 to 340 of 603 articles

Clear Filters

Machining learning predicts the need for escalated care and mortality in COVID-19 patients from clinical variables.

International journal of medical sciences
This study aimed to develop a machine learning algorithm to identify key clinical measures to triage patients more effectively to general admission versus intensive care unit (ICU) admission and to predict mortality in COVID-19 pandemic. This retro...

From predictions to prescriptions: A data-driven response to COVID-19.

Health care management science
The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to s...

DeepAISE - An interpretable and recurrent neural survival model for early prediction of sepsis.

Artificial intelligence in medicine
Sepsis, a dysregulated immune system response to infection, is among the leading causes of morbidity, mortality, and cost overruns in the Intensive Care Unit (ICU). Early prediction of sepsis can improve situational awareness among clinicians and fac...

A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil.

Scientific reports
The new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective allocation of healthcare resources. An accurate prognostic assessment is necessary to improve survival of patients, especially in developing countrie...

Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients.

Scientific reports
Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic m...

Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare.

Nature communications
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 similar to other less critical conditions. We develop an artifici...

EffiCare: Better Prognostic Models via Resource-Efficient Health Embeddings.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Recent medical prognostic models adapted from high data-resource fields like language processing have quickly grown in complexity and size. However, since medical data typically constitute low data-resource settings, performances on tasks like clinic...

Machine Learning Techniques for Personalized Detection of Epileptic Events in Clinical Video Recordings.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Continuous patient monitoring is essential to achieve an effective and optimal patient treatment in the intensive care unit. In the specific case of epilepsy it is the only way to achieve a correct diagnosis and a subsequent optimal medication plan i...

A Clinically Practical and Interpretable Deep Model for ICU Mortality Prediction with External Validation.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Deep learning models are increasingly studied in the field of critical care. However, due to the lack of external validation and interpretability, it is difficult to generalize deep learning models in critical care senarios. Few works have validated ...