Critical Care

Are we ready to adapt artificial intelligence for early sepsis detection?

An interpretable machine learning model for real-time sepsis prediction based on basic physiological indicators.

OBJECTIVE: In view of the important role of risk prediction models in the clinical diagnosis and treatment of sepsis, and the limitations of existing models in terms of timeliness and interpretability, we intend to develop a real-time prediction model of sepsis with high timeliness and clinical interpretability.

Artificial Intelligence for Early Sepsis Detection: A Word of Caution.

The surgical anatomy of a (robot-assisted) minimally invasive transcervical esophagectomy.

BACKGROUND: Transcervical esophagectomy allows for esophagectomy through transcervical access and bypasses the thoracic cavity, thereby eliminating si...

Collaborative deep learning improves disease-related circRNA prediction based on multi-source functional information.

Emerging studies have shown that circular RNAs (circRNAs) are involved in a variety of biological processes and play a key role in disease diagnosing,...

Attention-guided multi-scale deep object detection framework for lymphocyte analysis in IHC histological images.

Tumor-infiltrating lymphocytes are specialized lymphocytes that can detect and kill cancerous cells. Their detection poses many challenges due to sign...

Integrated mRNA sequence optimization using deep learning.

The coronavirus disease of 2019 pandemic has catalyzed the rapid development of mRNA vaccines, whereas, how to optimize the mRNA sequence of exogenous...

A novel method for conformity assessment testing of dialysis machines for post-market surveillance purposes.

BACKGROUND: Dialysis machines are used regularly in healthcare practice. They are classified as a type of medical device with moderate and high risk t...

A novel method for conformity assessment testing of mechanical ventilators for post-market surveillance purposes.

BACKGROUND: Mechanical ventilators are medical devices used in intensive care units when patients are in need of mechanical aid to facilitate the proc...

Review of Artificial Intelligence-Based Signal Processing in Dialysis: Challenges for Machine-Embedded and Complementary Applications.

Artificial intelligence technology is trending in nearly every medical area. It offers the possibility for improving analytics, therapy outcome, and u...

Deep Learning Using Electrocardiograms in Patients on Maintenance Dialysis.

Cardiovascular morbidity and mortality occur with an extraordinarily high incidence in the hemodialysis-dependent end-stage kidney disease population....

The possible role of artificial intelligence in deciding postnatal steroid management in extremely premature ventilated infants.

Clinical decision support (CDS) has shown a positive effect on physicians. There is variability among physicians about using postnatal steroids (PNS) ...

Developments in respiratory self-management interventions over the last two decades.

This paper describes developments in the fields of asthma and COPD self-management interventions (SMIs) over the last two decades and discusses future...

Continuous Renal Replacement Therapy: What Have We Learned And What Are Key Milestones For The Years To Come?

Continuous renal replacement therapy (CRRT) is the main extracorporeal kidney support therapy used in critical ill patients in the intensive care unit...

[Effect of EIT-guided individualized PEEP setting on the incidence of hypoxemia in elderly patients undergoing robot-assisted radical prostatectomy].

To investigate the effect of individualized positive end expiratory pressure (PEEP) setting guided by chest electrical impedance tomography (EIT) on ...

[Prediction of respiratory motion based on sequential embedding combined with relational embedding].

OBJECTIVE: To propose a deep learning model for modeling and prediction of the integration of respiratory motion in all directions.

Artificial intelligence in peritoneal dialysis: general overview.

OBJECTIVE: This article is a general overview about artificial intelligence/machine learning (AI/ML) algorithms in the domain of peritoneal dialysis (...

Development and deployment of interpretable machine-learning model for predicting in-hospital mortality in elderly patients with acute kidney disease.

BACKGROUND: Acute kidney injury (AKI) is more likely to develop in the elderly admitted to the intensive care unit (ICU). Acute kidney disease (AKD) a...

Clinical utility of automatic phenotype annotation in unstructured clinical notes: intensive care unit use.

OBJECTIVE: Clinical notes contain information that has not been documented elsewhere, including responses to treatment and clinical findings, which ar...

Comparison of Mortality Predictive Models of Sepsis Patients Based on Machine Learning.

Objective To compare the performance of five machine learning models and SAPS II score in predicting the 30-day mortality amongst patients with sepsis...