Infectious Disease

Assessing AI-Based Decision Support in Early Sepsis and AKI Recognition.

Sepsis and acute kidney injury (AKI) remain among the most critical conditions in acute care, associated with high morbidity and mortality. Early risk recognition is essential but often hampered by nonspecific symptoms. Recent studies have demonstrated that AI- and ML-based Clinical Decision Support Systems (CDSS) can enhance the early detection of sepsis and AKI and support clinical decision-maki...

Biases in Healthcare AI and Their Impact on Patient Empowerment: A Scoping Review.

BACKGROUND: Artificial Intelligence (AI) is increasingly applied in healthcare and is often linked to patient empowerment. However, biases in data, algorithms, and design may hinder empowerment by reinforcing inequalities and limiting autonomy. OBJECTIVES: This scoping review examines how bias in healthcare AI impacts patient empowerment. METHODS: We searched PubMed and multiple databases via EBSC...

Interpretable Feature Extraction from Clinical Notes for Sepsis Prediction: Comparing Rule-Based, LLM, and Hybrid Approaches.

Embedding-based approaches integrate clinical notes into sepsis prediction models but produce uninterpretable representations, obscuring which clinica...

Kernel Density Estimation of Wearable Signals to Predict Preoperative Cancellation Risk.

Digital health approaches that leverage consumer wearables and machine learning offer scalable means to detect acute illness pre-symptomatically, enab...

Predicting COVID-19 Vaccination Decision-Making Profiles Among Dutch Adults: From Survey to National Administrative Data.

Individual's vaccination behaviors are influenced by factors such as values and beliefs. Applying latent class analysis (LCA) to such factors from the...

Emerging frontiers in genome editing: From CRISPR to next-generation technologies.

Genome editing has revolutionized molecular biology. It offers precise modification of genetic material across diverse organisms. This review outlines...

A methodological framework for conducting diagnostic meta-analyses using large literature corpora.

BACKGROUND: The exponential growth of biomedical literature challenges the feasibility, reproducibility, and bias control of diagnostic meta-analyses ...

Integrating network pharmacology, molecular docking, machine learning, and experimental validation: puerarin improves sepsis-induced acute kidney injury via the Sirt1-Nrf2-HO-1 pathway.

Acute kidney injury (AKI) associated with sepsis has a high clinical mortality rate, and there is a lack of effective therapeutic targets; uncontrolle...

Machine Learning-Assisted Molecularly Imprinted Polymer Sensor for Point-of-Care Vancomycin Monitoring in Serum.

Optimizing vancomycin dosage is critical for treating severe infections and combating antimicrobial resistance, yet it is hampered by slow, centralize...

Enhancing respiratory virus surveillance among hospitalised children: a machine learning-based predictive model.

BACKGROUND: Viral respiratory tract infections (vRTIs) are a leading cause of paediatric hospitalisation and healthcare utilisation. Existing syndromi...

Impact of COVID-19-related data drift on machine-learning prognostic models predicting 30-day opioid-related emergency department visits, hospitalisation or mortality: a population-level administrative data study in Alberta, Canada.

OBJECTIVE: To develop machine-learning (ML) models during the COVID-19 pandemic and adjacent time periods to evaluate the impact of data drift on mode...

Optimizing hepatitis C diagnosis through reinforcement learning feature selection and multi-model machine learning evaluation.

Hepatitis C virus (HCV) infection remains a leading cause of liver cirrhosis and hepatocellular carcinoma globally, affecting approximately 50 million...

Design and classify innovative antimicrobial and dual- and multi-functional peptides using generative artificial intelligence.

Antimicrobial resistance is a growing global crisis, where antimicrobial peptides (AMPs) have emerged as promising alternatives to conventional antibi...

Improved Prediction of Invasive Bacterial Infections in Febrile Infants Using Machine Learning.

BACKGROUND: Existing guidelines for febrile infants aged 8 to 60 days use clinical appearance, age, and laboratory test results to assess the risk of ...

Machine learning reveals microbiome differences by periodontitis severity.

Periodontitis is a chronic inflammatory disease driven by microbial dysbiosis, yet the microbial signatures associated with severity remain incomplete...

AI-designed OpenCRISPR-1 enables efficient targeted mutagenesis and prime editing in rice.

Recent advances in generative artificial intelligence (AI) have enabled the de novo design of genome-editing nucleases. For example, OpenCRISPR-1 offe...

Cooperative membrane association as a mechanistic origin of synergistic antimicrobial peptide activity.

Antimicrobial peptides (AMPs) are central components of the innate immunity system that can be found in almost all living organisms. They are promisin...

An externally validated machine learning algorithm for predicting mental and physical health outcomes three months post-hospitalization for severe viral infection with SARS-CoV-2.

Many individuals hospitalized due to severe viral infections develop mental and physical sequelae, which could potentially be prevented by targeted in...

Bridging ensemble model and public health practice: an approach for refining understanding of seasonal dengue transmission patterns in Bangladesh.

BACKGROUND: Dengue fever remains a persistent public health threat in Dhaka, Bangladesh, necessitating effective early warning systems to enable timel...

Antibiotic therapy in febrile neutropenia in haematological patients: current considerations and future challenges.

The management of febrile neutropenia (FN) in oncohematological patients is undergoing a paradigm shift driven by a deeper understanding of patients' ...