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Anti-Bacterial Agents

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Antibiotic discovery in the artificial intelligence era.

Annals of the New York Academy of Sciences
As the global burden of antibiotic resistance continues to grow, creative approaches to antibiotic discovery are needed to accelerate the development of novel medicines. A rapidly progressing computational revolution-artificial intelligence-offers an...

Analysis of Characteristic Factors of Nursing Safety Incidents in ENT Surgery by Deep Learning-Based Medical Data Association Rules Method.

Computational and mathematical methods in medicine
It is of great significance to explore the characteristic factors of postoperative nursing safety events in patients with otolaryngology surgery and to understand the characteristics of postoperative nursing safety events in otolaryngology surgery pa...

Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study.

International journal of environmental research and public health
Surgical site infections (SSIs) have a major role in the evolution of medical care. Despite centuries of medical progress, the management of surgical infection remains a pressing concern. Nowadays, the SSIs continue to be an important factor able to...

DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches.

Communications biology
This work demonstrates and guides how to use a range of state-of-the-art artificial neural-networks to analyse bacterial microscopy images using the recently developed ZeroCostDL4Mic platform. We generated a database of image datasets used to train n...

Machine Learning Study of Metabolic Networks ChEMBL Data of Antibacterial Compounds.

Molecular pharmaceutics
Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations an...

Using machine learning techniques to predict antimicrobial resistance in stone disease patients.

World journal of urology
PURPOSE: Artificial intelligence is part of our daily life and machine learning techniques offer possibilities unknown until now in medicine. This study aims to offer an evaluation of the performance of machine learning (ML) techniques, for predictin...

Parental Perceptions on Use of Artificial Intelligence in Pediatric Acute Care.

Academic pediatrics
BACKGROUND: Family engagement is critical in the implementation of artificial intelligence (AI)-based clinical decision support tools, which will play an increasing role in health care in the future. We sought to understand parental perceptions of co...

Antilogic, a new supervised machine learning software for the automatic interpretation of antibiotic susceptibility testing in clinical microbiology: proof-of-concept on three frequently isolated bacterial species.

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases
OBJECTIVE: Antibiotic susceptibility testing (AST) is necessary in order to adjust empirical antibiotic treatment, but the interpretation of results requires experience and knowledge. We have developed a machine learning software that is capable of r...

Machine learning to design antimicrobial combination therapies: Promises and pitfalls.

Drug discovery today
Combination therapies can overcome antimicrobial resistance (AMR) and repurpose existing drugs. However, the large combinatorial space to explore presents a daunting challenge. In response, machine learning (ML) algorithms are being applied to identi...