Sepsis

A Deep Learning Approach to Antibiotic Discovery.

Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub-halicin-that is structurally divergent from conventio...

Too Many Definitions of Sepsis: Can Machine Learning Leverage the Electronic Health Record to Increase Accuracy and Bring Consensus?

Postimplementation Evaluation of a Machine Learning-Based Deterioration Risk Alert to Enhance Sepsis Outcome Improvements.

Machine learning-based early warning systems (EWSs) can detect clinical deterioration more accurately than point-score tools. In patients with sepsis,...

A Machine Learning-Based Model to Predict Acute Traumatic Coagulopathy in Trauma Patients Upon Emergency Hospitalization.

Acute traumatic coagulopathy (ATC) is an extremely common but silent murderer; this condition presents early after trauma and impacts approximately 30...

High-accuracy Automated Diagnosis of Parkinson's Disease.

PURPOSE: Parkinson's disease (PD), which is the second most common neurodegenerative disease following Alzheimer's disease, can be diagnosed clinicall...

Evaluating the Effect of Dexmedetomidine on Hemodynamic Status of Patients with Septic Shock Admitted to Intensive Care Unit: A Single-Blind Randomized Controlled Trial.

Septic shock, known as the most severe complication of sepsis, is a serious medical condition that can lead to death. Clinical symptoms of sepsis incl...

Protective Effects of Mill. Fruit on Carbon Tetrachloride-induced Hepatotoxicity Mediated through Mitochondria and Restoration of Cellular Energy Content.

Quince ( Mill.) is one of the medicinal plant with a broad range of pharmacological activities such as hepatoprotective effect. The present study was ...

Natural Language Processing for the Identification of Surgical Site Infections in Orthopaedics.

BACKGROUND: The identification of surgical site infections for infection surveillance in hospitals depends on the manual abstraction of medical record...

Artificial Neural Network-Based Prediction of Outcome in Parkinson's Disease Patients Using DaTscan SPECT Imaging Features.

PURPOSE: Quantitative analysis of dopamine transporter (DAT) single-photon emission computed tomography (SPECT) images can enhance diagnostic confiden...

Assessing clinical heterogeneity in sepsis through treatment patterns and machine learning.

OBJECTIVE: To use unsupervised topic modeling to evaluate heterogeneity in sepsis treatment patterns contained within granular data of electronic heal...

A guide to machine learning for bacterial host attribution using genome sequence data.

With the ever-expanding number of available sequences from bacterial genomes, and the expectation that this data type will be the primary one generate...

High Accuracy of Convolutional Neural Network for Evaluation of Helicobacter pylori Infection Based on Endoscopic Images: Preliminary Experience.

OBJECTIVES: Application of artificial intelligence in gastrointestinal endoscopy is increasing. The aim of the study was to examine the accuracy of co...

[Severe influenza A (H1N1) in late pregnancy: a case report].

Pregnancy has increased susceptibility to H1N1 influenza virus infection. Maternal influenza infection is associated with increased risk of morbidity ...

Clinician Perception of a Machine Learning-Based Early Warning System Designed to Predict Severe Sepsis and Septic Shock.

OBJECTIVE: To assess clinician perceptions of a machine learning-based early warning system to predict severe sepsis and septic shock (Early Warning S...

A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice.

OBJECTIVES: Develop and implement a machine learning algorithm to predict severe sepsis and septic shock and evaluate the impact on clinical practice ...

In vitro cytotoxic, antioxidant, antibacterial and antifungal activity of Saussurea heteromalla indigenous to Pakistan.

Medicinal plants are proven to reveal vast promising potential providing novel drug candidates to combat health-related problems. The aim of current s...

Risk stratification of cervical lesions using capture sequencing and machine learning method based on HPV and human integrated genomic profiles.

From initial human papillomavirus (HPV) infection and precursor stages, the development of cervical cancer takes decades. High-sensitivity HPV DNA tes...

A new and efficient numerical method for the fractional modeling and optimal control of diabetes and tuberculosis co-existence.

The main objective of this research is to investigate a new fractional mathematical model involving a nonsingular derivative operator to discuss the c...

A Double-Blind, Randomized Control Trial of Rapidly Infused High Strong Ion Difference (SID) Fluid Versus Hartmann's Solution on Acid-Base Status in Sepsis Patients in the Emergency Department.

BACKGROUND: Balanced fluids are preferred in initial resuscitation of septic patients based on several recent studies. The Stewart's concept on acid-b...

Prospect of using deep learning for predicting differentiation of myeloid progenitor cells after sepsis.