AIMC Topic: Aged, 80 and over

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Mortality risk stratification using artificial intelligence-augmented electrocardiogram in cardiac intensive care unit patients.

European heart journal. Acute cardiovascular care
AIMS: An artificial intelligence-augmented electrocardiogram (AI-ECG) algorithm can identify left ventricular systolic dysfunction (LVSD). We sought to determine whether this AI-ECG algorithm could stratify mortality risk in cardiac intensive care un...

Artificial Intelligence-Based Screening for Mycobacteria in Whole-Slide Images of Tissue Samples.

American journal of clinical pathology
OBJECTIVES: This study aimed to develop and validate a deep learning algorithm to screen digitized acid fast-stained (AFS) slides for mycobacteria within tissue sections.

Development and validation of a machine learning model predicting illness trajectory and hospital utilization of COVID-19 patients: A nationwide study.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: The spread of coronavirus disease 2019 (COVID-19) has led to severe strain on hospital capacity in many countries. We aim to develop a model helping planners assess expected COVID-19 hospital resource utilization based on individual patien...

Predicting ventilator-associated pneumonia with machine learning.

Medicine
Ventilator-associated pneumonia (VAP) is the most common and fatal nosocomial infection in intensive care units (ICUs). Existing methods for identifying VAP display low accuracy, and their use may delay antimicrobial therapy. VAP diagnostics derived ...

[Robot-assisted nephroureterectomy requiring no robot redocking or patient repositioning: experience from a single center with 62 cases].

Zhonghua wai ke za zhi [Chinese journal of surgery]
To examine a new technique of robot-assisted nephroureterectomy without robot reldocking or patient repositioning. Patients diagnosed as upper tract urothelial carcinoma treated with this modality between November 2015 and January 2019 at Departmen...

A Deep-Learning-Based, Fully Automated Program to Segment and Quantify Major Spinal Components on Axial Lumbar Spine Magnetic Resonance Images.

Physical therapy
OBJECTIVE: The paraspinal muscles have been extensively studied on axial lumbar magnetic resonance imaging (MRI) for better understanding of back pain; however, the acquisition of measurements mainly relies on manual segmentation, which is time consu...

Rib fracture detection in computed tomography images using deep convolutional neural networks.

Medicine
To evaluate the rib fracture detection performance in computed tomography (CT) images using a software based on a deep convolutional neural network (DCNN) and compare it with the rib fracture diagnostic performance of doctors.We included CT images fr...

The Value of Artificial Intelligence in Laboratory Medicine.

American journal of clinical pathology
OBJECTIVES: As laboratory medicine continues to undergo digitalization and automation, clinical laboratorians will likely be confronted with the challenges associated with artificial intelligence (AI). Understanding what AI is good for, how to evalua...

Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree.

Medicine
Sepsis is a leading cause of mortality in the intensive care unit. Early prediction of sepsis can reduce the overall mortality rate and cost of sepsis treatment. Some studies have predicted mortality and development of sepsis using machine learning m...

Four Biomarkers-Based Artificial Neural Network Model for Accurate Early Prediction of Bacteremia with Low-level Procalcitonin.

Annals of clinical and laboratory science
OBJECTIVE: Procalcitonin levels above 2.0 ng/mL are associated with a higher risk of severe sepsis. Bacteremia with procalcitonin levels lower than 2.0 ng/mL has not received much attention, and relevant prediction models are lacking. Herein, a panel...