Sepsis

In-Sensor Artificial Intelligence and Fusion With Electronic Medical Records for At-Home Monitoring.

This work presents an artificial intelligence (AI) framework for real-time, personalized sepsis prediction four hours before onset through fusion of electrocardiogram (ECG) and patient electronic medical record. An on-chip classifier combines analog reservoir-computer and artificial neural network to perform prediction without front-end data converter or feature extraction which reduces energy by ...

Is ultrasound-guided hip aspiration more successful than fluoroscopic-guided aspiration in diagnosing prosthetic joint infection?

: aspiration of total hip arthroplasty (THA) is commonly performed to assist in the diagnosis of prosthetic joint infection (PJI). This study aimed to determine whether fluoroscopic- or ultrasound- guided hip aspiration differs in the ability to acquire synovial fluid and in the accuracy of diagnosing infection. all THA aspirations performed between 2014 and 2021 at our institution were retrospec...

A dosing strategy model of deep deterministic policy gradient algorithm for sepsis patients.

BACKGROUND: A growing body of research suggests that the use of computerized decision support systems can better guide disease treatment and reduce th...

Robot-assisted vs. manually guided stereoelectroencephalography for refractory epilepsy: a systematic review and meta-analysis.

Robotic assistance has improved electrode implantation precision in stereoelectroencephalography (SEEG) for refractory epilepsy patients. We sought to...

Bringing the Promise of Artificial Intelligence to Critical Care: What the Experience With Sepsis Analytics Can Teach Us.

Using artificial intelligence to reduce orthopedic surgical site infection surveillance workload: Algorithm design, validation, and implementation in 4 Spanish hospitals.

BACKGROUND: Surgical site infection (SSI) surveillance is a labor-intensive endeavor. We present the design and validation of an algorithm for SSI det...

Developing DELPHI expert consensus rules for a digital twin model of acute stroke care in the neuro critical care unit.

INTRODUCTION: Digital twins, a form of artificial intelligence, are virtual representations of the physical world. In the past 20 years, digital twins...

Machine Learning Model for Assessment of Risk Factors and Postoperative Day for Superficial vs Deep/Organ-Space Surgical Site Infections.

Deep and organ space surgical site infections (SSI) require more intensive treatment, may result in more severe clinical disease and may have differe...

Data-driven prediction models for forecasting multistep ahead profiles of mammalian cell culture toward bioprocess digital twins.

Recently, the advancement in process analytical technology and artificial intelligence (AI) has enabled the generation of enormous culture data sets f...

Anti-Biofilm: Machine Learning Assisted Prediction of IC Activity of Chemicals Against Biofilms of Microbes Causing Antimicrobial Resistance and Implications in Drug Repurposing.

Biofilms are one of the leading causes of antibiotic resistance. It acts as a physical barrier against the human immune system and drugs. The use of a...

Addressing antibiotic resistance: computational answers to a biological problem?

The increasing prevalence of infections caused by antibiotic-resistant bacteria is a global healthcare crisis. Understanding the spread of resistance ...

Constructing metabolism-protein interaction relationship to identify glioma prognosis using deep learning.

Glioma is heterogeneous disease that requires classification into subtypes with similar clinical phenotypes, prognosis or treatment responses. Metabol...

Clinically explainable machine learning models for early identification of patients at risk of hospital-acquired urinary tract infection.

BACKGROUND: Machine learning (ML) models for early identification of patients at risk of hospital-acquired urinary tract infection (HA-UTI) may enable...

Applying T-classifier, binary classifiers, upon high-throughput TCR sequencing output to identify cytomegalovirus exposure history.

With the continuous development of information technology and the running speed of computers, the development of informatization has led to the genera...

Plasma-Derived Fibrin Hydrogels Containing Graphene Oxide for Infections Treatment.

Wound infection is inevitable in most patients suffering from extensive burns or chronic ulcers, and there is an urgent demand for the production of b...

Multi-modal body part segmentation of infants using deep learning.

BACKGROUND: Monitoring the body temperature of premature infants is vital, as it allows optimal temperature control and may provide early warning sign...

The use of smart environments and robots for infection prevention control: A systematic literature review.

BACKGROUND: Infection prevention and control (IPC) is essential to prevent nosocomial infections. This manuscript aims at investigating the current us...

Assessment of Helicobacter pylori infection by deep learning based on endoscopic videos in real time.

BACKGROUND AND AIMS: Endoscopic assessment of Helicobacter pylori infection is a simple and effective method. Here, we aimed to develop a deep learnin...

A deep-learning algorithm to classify skin lesions from mpox virus infection.

Undetected infection and delayed isolation of infected individuals are key factors driving the monkeypox virus (now termed mpox virus or MPXV) outbrea...

Exploring a global interpretation mechanism for deep learning networks when predicting sepsis.

The purpose of this study is to identify additional clinical features for sepsis detection through the use of a novel mechanism for interpreting black...