Infectious Disease

Machine learning models predict long COVID outcomes based on baseline clinical and immunologic factors

The post-acute sequelae of SARS-CoV-2 (PASC), also known as long COVID, remain a significant health issue that is incompletely understood. Predicting which acutely infected individuals will go on to develop long COVID is challenging due to the lack of established biomarkers, clear disease mechanisms, or well-defined sub-phenotypes. Machine learning (ML) models offer the potential to address this b...

Sentiment analysis of employees and COVID-19 vaccine hesitancy at workplace

Vaccination is a potent means to combat the spread of infectious disease epidemics or pandemics, such as the COVID-19 pandemic. However, getting sufficient people to accept the vaccine and achieve herd immunity remains a significant challenge. This study evaluates preschool workers’ sentiments and challenges during in-person schooling amidst the COVID-19 pandemic and attitudes toward COVID-19 vacc...

The impact of evaluation strategy on sepsis prediction model performance metrics in intensive care data

The prediction of the onset of sepsis, a life-threatening condition resulting from a dysregulated response to an infection, is one of the most common ...

InfEHR: Resolving Clinical Uncertainty through Deep Geometric Learning on Electronic Health Records

Electronic health records (EHRs) contain multimodal data that can inform diagnostic and prognostic clinical decisions but are often unsuited for advan...

H-MpoxNet: A Hybrid Deep Learning Framework for Mpox Detection from Image Data

Infectious diseases pose significant global threats to public health and economic stability by causing pandemics. Early detection of infectious diseas...

Automated Antibiogram Extraction from Unstructured Microbiology Reports: A Comparative Performance and Efficiency Analysis of Domain-Specific Named Entity Recognition (NER) Pipelines

Antibiograms are essential tools in antimicrobial stewardship programs (ASPs), guiding empirical antibiotic therapy and tracking antimicrobial resista...

Plasma proteomics for novel biomarker discovery in childhood tuberculosis

Failure to rapidly diagnose tuberculosis disease (TB) and initiate treatment is a driving factor of TB as a leading cause of death in children. Curren...

Development and Validation of an Artificial Intelligence Predictive Model to Accelerate Antibiotic Therapy for Critical Ill Children with Sepsis in the Pediatric ED with Pediatric ICU Disposition

Pediatric sepsis accounts for over 72,000 US hospitalizations annually with significant mortality and morbidity. Many pediatric hospitals struggle to ...

Expanding cholera serosurveillance to vaccinated populations

Mass oral cholera vaccination campaigns targeted at subnational areas with high incidence are central to global cholera elimination efforts. Serologic...

Extracting Pulmonary Embolism Diagnoses from Radiology Impressions Using GPT-4o: A Large Language Model Evaluation Study

Pulmonary embolism (PE) is a critical condition requiring rapid diagnosis to reduce mortality. Extracting PE diagnoses from radiology reports manually...

Predicting Suicidality in people living with HIV in Uganda: A Machine Learning Approach

People living with HIV (PLWH) are more likely to experience suicidal thoughts and exhibit suicidal behavior than the general population. However, ther...

Unravelling the Complex Inflammatory Landscape of COVID-19 infection: A Pathway to Biomarkers Identification in Infection-Associated Delirium in the ICU

Delirium is a serious complication in patients with COVID-19-related acute respiratory distress syndrome (ARDS) admitted to the intensive care unit (I...

irAE-GPT: Leveraging large language models to identify immune-related adverse events in electronic health records and clinical trial datasets

Large language models (LLMs) have emerged as transformative technologies, revolutionizing natural language understanding and generation across various...

Concurrent prediction of in-hospital mortality and length of stay using single-task, multi-class, and multi-task machine learning

Accurate predictions of discharge timing and in-hospital mortality could improve hospital efficiency, but clinician estimates are often inconsistent a...

Evaluating biomedical feature fusion on machine learning’s predictability and interpretability of COVID-19 severity types

Accurately differentiating severe from non-severe COVID-19 clinical types is critical for the healthcare system to optimize workflow. Current techniqu...

Unraveling the drivers of leptospirosis risk in Thailand using machine learning

Leptospirosis poses a significant public health challenge in Thailand, driven by a complex mix of environmental and socioeconomic factors. This study ...

Mining Social Media Data for Influenza Vaccine Effectiveness Using a Large Language Model and Chain-of-Thought Prompting

Influenza vaccine effectiveness (VE) estimation plays a critical role in public health decision-making by quantifying the real-world impact of vaccina...

Tuberculosis disease severity assessment using clinical variables and radiology enabled by artificial intelligence

Radiology can define tuberculosis (TB) severity and may guide duration of treatment, however the optimal radiological metric to use and which clinical...

A mechanistic neural network model predicts both potency and toxicity of antimicrobial combination therapies

Antimicrobial resistance poses a major global threat due to the diminishing efficacy of current treatments and limited new therapies. Combination ther...

Artificial Intelligence algorithm for real-time detection and counting of Trypanosoma cruzi parasites using smartphone microscopy

Chagas disease affects 6–7 million people worldwide and causes approximately 12,000 deaths annually. Diagnostic methods vary by disease stage, with se...