Latest AI and machine learning research in reimbursement for healthcare professionals.
Electronic Health Record (EHR) retrieval plays a pivotal role in various clinical tasks, but its development has been severely impeded by the lack of publicly available benchmarks. In this paper, we introduce a novel public EHR retrieval benchmark, CliniQ, to address this gap. We consider two retrieval settings: Single-Patient Retrieval and Multi-Patient Retrieval, reflecting various real-world ...
Electrocardiogram (ECG) analysis has emerged as a promising tool for identifying physiological changes associated with neuropsychiatric conditions. The relationship between cardiovascular health and neuropsychiatric disorders suggests that ECG abnormalities could serve as valuable biomarkers for more efficient detection, therapy monitoring, and risk stratification. However, the potential of the ...
BACKGROUND: International Classification of Diseases (ICD) codes utilized for congenital heart defect (CHD) case identification in datasets have subst...
Background: Healthcare has many manual processes that can benefit from automation and augmentation with Generative Artificial Intelligence (AI), the...
Extracting cell–cell communication (CCC) from biomedical literature is essential for supporting experimental and computational analyses of intercellul...
Rapid developments are occurring in artificial intelligence (AI) and machine learning (ML) applied to neuroimaging. To date, advances in this space ha...
Intracerebral hemorrhage (ICH) is a critical form of stroke resulting from bleeding within the brain, with a mortality rate of 40-50% within a few day...
The 24h behaviour profile, including physical activity, sedentary time, and sleep, is disrupted following a cancer diagnosis and contributes to cancer...
Implementing machine learning models to identify clinical deterioration on the wards is associated with improved outcomes. However, these models have ...
Anxiety, depression, and other mental health conditions are affecting millions of people worldwide each year. However, limited access to mental health...
Cerebrospinal fluid (CSF) culture is the diagnostic gold standard for neuroinfectious diseases such as bacterial meningitis, but its sensitivity is li...
To develop and evaluate an open-source machine learning (ML) models for predicting hospital short stays (length of stay [LOS] under 48 and 72 hours) e...
Impulse control disorders (ICD) in Parkinson’s disease (PD) patients mainly occur as adverse effects of dopamine replacement therapy. Despite several ...
While large language models (LLMs) have shown promise in medical text processing, their real-world application in self-hosted clinical settings remain...
Ambient artificial intelligence (AI) offers the potential to reduce documentation burden and improve efficiency through clinical note generation. Wide...
Artificial intelligence (AI) and statistical models designed to predict same-admission outcomes for hospitalized patients, such inpatient mortality, o...
Anophthalmia and microphthalmia (A/M) are rare congenital eye disorders with a low molecular diagnosis rate, which limits clinical management and gene...
Transparency and credibility of health-economic simulation models is essential to inform reimbursement decisions. Model replication can support model ...
Large language models (LLMs) now power clinical agents that can plan, call tools, and write into electronic health records (EHRs). They are becoming a...
Disease activity plays a central role in rheumatoid arthritis (RA) clinical studies. However, RA disease activity is inconsistently recorded in real-w...