Hospital-Based Medicine

Surveillance

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

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PH-LLM: Public Health Large Language Models for Infoveillance

The effectiveness of public health intervention, such as vaccination and social distancing, relies on public support and adherence. Social media has emerged as a critical platform for understanding and fostering public engagement with health interventions. However, the lack of real-time surveillance on public health issues leveraging social media data, particularly during public health emergencies...

Cutaneous leishmaniasis in Casablanca-Settat region (Morocco): spatio-temporal analysis of disease dynamic and machine learning based case prediction

Cutaneous leishmaniasis (CL) caused by Leishmania protozoa and transmitted through infected sandfly bites, poses a significant public health burden in Morocco. Our research aims to retrospectively assess spatio-temporal patterns of CL in the most densely populated region of the country, Casablanca-Settat, during a period of 14 years (2009–2022). We investigate epidemiological trends, seasonal fluc...

A Claims-Based Machine Learning Classifier of Modified Rankin Scale in Acute Ischemic Stroke

We developed a classifier to infer acute ischemic stroke (AIS) severity from Medicare claims using the Modified Rankin Scale (mRS) at discharge. The c...

The Rise of the Large Language Models (LLMs): Can They Truly Match Clinical and Data Science Experts in Clinical Trial Data Analysis?

Clinical trials provide evidence of the efficacy and safety of experimental treatment regimens. Analysis of data from these trials is a time-intensive...

CONORM: Context-Aware Entity Normalization for Adverse Drug Event Detection

Adverse drug events (ADEs) are a critical aspect of patient safety and pharmacovigilance, with significant implications for patient outcomes and publi...

Detecting papilloedema as a marker of raised intracranial pressure using artificial intelligence: a systematic review

Automated detection of papilloedema using artificial intelligence (AI) and retinal images acquired through an ophthalmoscope for triage of patients wi...

Exploring multidrug resistance patterns in community-acquired E. coli urinary tract infections with machine learning

While associations of antibiotic resistance traits are not random in multidrug-resistant (MDR) bacteria, clinically relevant resistance patterns remai...

Neuromodulation with Ultrasound: Hypotheses on the Directionality of Effects and Community Resource

Low-intensity Transcranial Ultrasound Stimulation is a promising non-invasive technique for brain stimulation and focal neuromodulation. Research with...

Projecting climate change impacts on inter-epidemic risk of Rift Valley fever across East Africa

Rift Valley fever (RVF) is a zoonotic disease that causes sporadic, multi-country epidemics. However, RVF virus (RVFV) also circulates during inter-ep...

Finding the Clinical Traces of Cognitive Impairment in Patients with Heart Failure: A Natural Language Processing Study of Clinical Letters from Routine Care

Cognitive impairment is common in patients with heart failure, but to which extent cognitive complaints are evaluated and listed in clinical practice ...

Development and Application of Natural Language Processing on Unstructured Data in Hypertension: A Scoping Review

Hypertension is a global health concern with a vast body of unstructured data, such as clinical notes, diagnosis reports, and discharge summaries, tha...

Towards automated fetal brain biometry reporting for 3-dimensional T2-weighted 0.55-3T magnetic resonance imaging at 20-40 weeks gestational age range

The detailed assessment of fetal brain maturation and development involves morphological evaluation, gyration analysis, and reliable biometric measure...

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...

OphthUS-GPT: Multimodal AI for Automated Reporting in Ophthalmic B-Scan Ultrasound

The rapid advancement of AI in ophthalmology is transforming diagnostics, especially in resource-limited settings. The shortage of ophthalmologists an...

Protocol for developing the reporting guideline for the use of chatbots and other Generative Artificial intelligence tools in MEdical Research (GAMER)

The integration of artificial intelligence (AI) has revolutionized medical research, offering innovative solutions for data collection, patient engage...

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...

The epidemiology of pathogens with pandemic potential: A review of key parameters and clustering analysis

In the light of the COVID-19 pandemic many countries are trying to widen their pandemic planning from its traditional focus on influenza. However, it ...

Evaluating the Reporting Quality of 21,041 Randomized Controlled Trial Articles

Incomplete reporting of a study’s methods and results hinders efforts to evaluate and reproduce research findings in randomized controlled trials (RCT...

Large Language Models in Radiology Reporting—A Systematic Review of Performance, Limitations, and Clinical Implications

Large language models (LLMs) have emerged as potential tools for automated radiology reporting. However, concerns regarding their fidelity, reliabilit...

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