Sepsis

Virus-host interactions predictor (VHIP): Machine learning approach to resolve microbial virus-host interaction networks.

Viruses of microbes are ubiquitous biological entities that reprogram their hosts' metabolisms during infection in order to produce viral progeny, impacting the ecology and evolution of microbiomes with broad implications for human and environmental health. Advances in genome sequencing have led to the discovery of millions of novel viruses and an appreciation for the great diversity of viruses on...

Salivary detection of Chikungunya virus infection using a portable and sustainable biophotonic platform coupled with artificial intelligence algorithms.

The current detection method for Chikungunya Virus (CHIKV) involves an invasive and costly molecular biology procedure as the gold standard diagnostic method. Consequently, the search for a non-invasive, more cost-effective, reagent-free, and sustainable method for the detection of CHIKV infection is imperative for public health. The portable Fourier-transform infrared coupled with Attenuated Tota...

Screening of genes co-associated with osteoporosis and chronic HBV infection based on bioinformatics analysis and machine learning.

OBJECTIVE: To identify HBV-related genes (HRGs) implicated in osteoporosis (OP) pathogenesis and develop a diagnostic model for early OP detection in ...

Unveiling the role of artificial intelligence in tetracycline antibiotics removal using UV/sulfite/phenol advanced reduction process.

UV/sulfite-based advanced reduction processes (ARP) have attracted increasing attention due to their high capability for removing a wide range of poll...

Geospatial and Temporal Analysis of Avian Influenza Risk in Thailand: A GIS-Based Multi-Criteria Decision Analysis Approach for Enhanced Surveillance and Control.

Avian influenza (AI) is a viral infection that profoundly affects global poultry production. This study aimed to identify the spatial and temporal fac...

Detection of Verticillium infection in cotton leaves using ATR-FTIR spectroscopy coupled with machine learning algorithms.

Verticillium wilt (VW) is a soil-borne vascular disease that affects upland cotton and is caused by Verticillium dahliae Kleb. A rapid and user-friend...

Machine learning to guide the use of plasma technology for antibiotic degradation.

Antibiotics are misused and discharged into environmental water, posing a constant potential threat to the ecosystem. Utilising plasma's physical and ...

Integrating respiratory microbiome and host immune response through machine learning for respiratory tract infection diagnosis.

At present, the diagnosis of lower respiratory tract infections (LRTIs) is difficult, and there is an urgent need for better diagnostic methods. This ...

Harnessing computational tools of the digital era for enhanced infection control.

This paper explores the potential of artificial intelligence, machine learning, and big data analytics in revolutionizing infection control. It addres...

Development and evaluation of a model for predicting the risk of healthcare-associated infections in patients admitted to intensive care units.

This retrospective study used 10 machine learning algorithms to predict the risk of healthcare-associated infections (HAIs) in patients admitted to in...

Advanced Prediction of Hepatic Oncogenic Transformation in HBV Patients via RNA-Seq Data Analysis and Deep Learning Techniques.

Liver cancer, recognized as a significant global health issue, is increasingly correlated with Hepatitis B virus (HBV) infection, as evidenced by nume...

Antibiotic profile classification of Proteus mirabilis using machine learning: An investigation into multidimensional radiomics features.

Antimicrobial resistance (AMR) presents a significant threat to global healthcare. Proteus mirabilis causes catheter-associated urinary tract infectio...

An efficient ranking-based ensembled multiclassifier for neurodegenerative diseases classification using deep learning.

Neurodegenerative diseases are group of debilitating and progressive disorders that primarily affect the structure and functions of nervous system, le...

Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3.

BACKGROUND: Sepsis poses a critical threat to hospitalized patients, particularly those in the Intensive Care Unit (ICU). Rapid identification of Seps...

Implementing Autonomous Control in the Digital-Twins-Based Internet of Robotic Things for Remote Patient Monitoring.

Conventional patient monitoring methods require skin-to-skin contact, continuous observation, and long working shifts, causing physical and mental str...

Preparation of Multistage Pore TS-1 with Enhanced Photocatalytic Activity, Including Process Studies and Artificial Neural Network Modeling for Synergy Assessment.

Antibiotic residues have been found in several aquatic ecosystems as a result of the widespread use of antibiotics in recent years, which poses a majo...

Mesocorticolimbic and Cardiometabolic Diseases-Two Faces of the Same Coin?

The risk behaviors underlying the most prevalent chronic noncommunicable diseases (NCDs) encompass alcohol misuse, unhealthy diets, smoking and sedent...

Predicting Mortality in Sepsis-Associated Acute Respiratory Distress Syndrome: A Machine Learning Approach Using the MIMIC-III Database.

BackgroundTo develop and validate a mortality prediction model for patients with sepsis-associated Acute Respiratory Distress Syndrome (ARDS).MethodsT...

Assessing the Performance of Machine Learning Methods Trained on Public Health Observational Data: A Case Study From COVID-19.

From early in the coronavirus disease 2019 (COVID-19) pandemic, there was interest in using machine learning methods to predict COVID-19 infection sta...

Enhancing Patient Selection in Sepsis Clinical Trials Design Through an AI Enrichment Strategy: Algorithm Development and Validation.

BACKGROUND: Sepsis is a heterogeneous syndrome, and enrollment of more homogeneous patients is essential to improve the efficiency of clinical trials....