Hospital-Based Medicine

Surveillance

Latest AI and machine learning research in surveillance for healthcare professionals.

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AutoReporter: Development of an artificial intelligence tool for automated assessment of research reporting guideline adherence

To develop AutoReporter, a large-language-model system that automates evaluation of adherence to research reporting guidelines. Eight prompt-engineering and retrieval strategies coupled with reasoning and general-purpose LLMs were benchmarked on the SPIRIT-CONSORT-TM corpus. The top-performing approach, AutoReporter, was validated on BenchReport, a novel benchmark dataset of expert-rated reporting...

Aligning computational pathology with clinical practice for colorectal cancer

Pathology reporting of colorectal cancer (CRC) follows the International Collaboration on Cancer Reporting (ICCR) guidelines which define a set of 25 diagnostic report elements. To further develop the CRC diagnostic routine, multiple computational tools have been proposed in the last years. Despite the excellent sensitivity and potential advantages, many tools do not reach clinical deployment, sug...

Wireless Colorimetric Multi-Biomarker Sensing to Enable Critical Neonatal Monitoring

Clinical monitoring in the most vulnerable patients such as newborns relies on invasive and costly procedures and/or wired sensor surveillance, increa...

Bayesian hybrid statistical and machine learning models for dengue forecasting in Bangladesh: Temporal and spatial analysis for an early warning system

Dengue remains a major public health concern in Bangladesh, yet reliable forecasting models that integrate climatic and demographic drivers are limite...

Rainfall, Mosquito Indices, and Dengue Outbreaks in Southern Taiwan: Reassessing Predictive Modeling with Machine Learning Approaches

Dengue remains a major public health challenge in southern Taiwan, where recurrent outbreaks are shaped by complex environmental and entomological dri...

Large language models for automatable real-world performance monitoring of diagnostic decision support systems: a comparison to manual doctor panel review in a prospective clinical study

Diagnostic decision support systems (DDSS) are increasingly deployed at scale, yet their diagnostic accuracy is insufficiently monitored once integrat...

Harnessing Machine Learning for Antimicrobial Resistance Surveillance in Zimbabwe

Antimicrobial resistance (AMR) poses a significant public health challenge, particularly in resource-limited settings such as Zimbabwe, where surveill...

Factors influencing the trustworthiness of non-randomized studies of interventions: a survey of international experts

Perceived trustworthiness of research may be influenced by factors beyond the risk of bias, including study-related characteristics, research context,...

Predicting Rejection Risk in Heart Transplantation: An Integrated Clinical–Histopathologic Framework for Personalized Post-Transplant Care

Cardiac allograft rejection (CAR) remains the leading cause of early graft failure after heart transplantation (HT). Current diagnostics, including hi...

Retrospective Validation of an Artificial Intelligence System for Diagnostic Assessment of Prostate Biopsies on the ProMort Cohort: Study Protocol

Prostate cancer diagnosis and treatment planning depend on accurate histopathological assessment of needle biopsies, particularly through the Gleason ...

A systematic review of early neuroimaging and neurophysiological biomarkers for post-stroke mobility prognostication

Accurate prognostication of mobility outcomes is essential to guide rehabilitation and manage patient expectations. The prognostic utility of neuroima...

A Systematic Process for Assessing Fitness-for-Purpose of Health Outcomes for Computable Phenotyping with Electronic Health Record Data

Information from electronic health records (EHRs) may be incorporated into computable phenotype algorithms in efforts to overcome inaccuracies of algo...

BeatAI: BiomEtrics for Atrial Arrhythmia Tracking Using Artificial Intelligence

Postoperative atrial fibrillation (POAF) affects 20 to 50% of patients undergoing cardiac surgery and is associated with longer hospital stays and adv...

Real-Time EEG-Based Epileptic Seizure Prediction Using Artificial Intelligence: A Systematic Review

Epilepsy affects approximately 50 million people worldwide, and seizures remain difficult to predict in onset, severity, and duration. Real-time seizu...

Machine Learning and Probabilistic Approaches for Forecasting Infectious Disease Transmission and Cases

Forecasting the effective reproductive number (Rt) and infection case counts is critical for guiding public health responses. We developed a machine l...

Costing Methods for Artificial Intelligence: Systematic Review and Recommended Cost Inventory for in Health Technology Assessment

Economic evaluations of artificial intelligence (AI) in healthcare are expanding rapidly, yet underlying costing methods remains heterogenous, and fre...

From claims to care: Machine learning algorithm to classify urinary tract infection cases using Swiss health insurance data

To evaluate whether machine learning (ML) applied to comprehensive claims data without diagnostic codes can distinguish a high proportion of antibioti...

Large Language Models for Detecting CONSORT Guideline Compliance in Published Randomized Clinical Trials: A Cross-Sectional Evaluation Study

Peer review processes may inadequately assess compliance with established reporting guidelines such as the Consolidated Standards of Reporting Trials ...

A Hybrid Deep Learning-Mechanistic Modeling Framework for Dengue Transmission Dynamics in Guangdong, China

Dengue fever is a mosquito-borne viral disease with strong seasonality, periodicity, and spatial heterogeneity, posing a persistent global public heal...

Urinary peptidomic signatures predict overall and progression-free survival in patients with bladder cancer

Clinicopathologic calculators for bladder cancer moderately predict survival and fail to depict the underlying molecular phenotype. We applied urinary...

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