Latest AI and machine learning research in hospital-based medicine for healthcare professionals.
Recent advances in deep learning have enabled the integration of heterogeneous data modalities for clinical prediction, allowing models to exploit complex information embedded within electronic health records (EHRs). Among these modalities, chest radiographs (CXRs) provide a rich source of visual information that can enhance patient outcome prediction for patients in the intensive care unit (ICU)....
MAX-EVAL-11 is constructed by converting MIMIC-III discharge summaries from ICD-9 to ICD-11 codes through systematic mapping, creating a synthetic diagnosis dataset of 10,000 clinical notes with comprehensive ICD-11 annotations spanning the complete taxonomy. Unlike existing partial-taxonomy benchmarks that rely on traditional precision-recall metrics, MAX-EVAL-11 introduces a clinically-informed ...
The scientific literature on artificial intelligence-enabled electrocardiography (AI-ECG) has defined a robust performance of AI models in detecting a...
High-quality clinical documentation is essential for safe, effective care, yet producing it is time consuming and error prone. Large language models (...
UK ambulance services face record demand, resourcing challenges and rising clinical documentation burden. Ambient voice technology (AVT) coupled with ...
Bias in machine learning is a persistent challenge because it can create unfair outcomes, limit generalization, and reduce trust in real-world applica...
Questionnaires that capture patient-reported symptomatology provide low-cost but potentially high-value data for the de novo discovery of disease phen...
Heart failure (HF), including heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF), remains...
Automated machine learning (AutoML) promises to democratize predictive modeling in healthcare by automating algorithm selection and hyperparameter opt...
To identify clusters of high-cost patients in England based on diagnoses and sociodemographic characteristics to inform targeted population health man...
Preoperative cardiovascular (CV) risk stratification is essential in non-cardiac surgery, but conventional testing is frequently overused, increasing ...
Use of coercive measures in psychiatric hospitals is clinically and ethically challenging. Aiming to support prevention, we developed and evaluated ma...
Differentiating between motor functional dissociative seizures (FDS) and motor epileptic seizures (ES) is a common diagnostic challenge, requiring vid...
The increasing availability of electronic health records (EHRs) provides opportunities to apply machine learning (ML) methods in support of clinical d...
For large language models (LLMs) to reach their potential as information technology tools that make medication use safer, clinically relevant benchmar...
Early prediction of in-hospital death remains a significant challenge due to the limited availability of structured data during initial admission. Uns...
The mortality rate is very high in patients with severe COVID-19. Nearly 32% of COVID-19 patients are critically ill, with mortality rates ranging fr...
The molecular complexity of cancer presents significant challenges to traditional therapeutic approaches, necessitating the development of innovative ...
OBJECTIVE: The purpose of this study was to develop and validate machine learning models that can predict superaverage length of stay in hypercapnic-t...
Tuberculosis (TB) remains a global health challenge, with timely and accurate diagnosis being critical for effective disease management and control. R...