Latest AI and machine learning research in hospital-based medicine for healthcare professionals.
Tuberculosis remains a major global health concern, particularly in high-burden countries where early detection is essential but often limited by insufficient radiological expertise. This study evaluated the diagnostic performance of a computer-aided detection system, CAD4TB, in interpreting chest X-ray images of suspected tuberculosis cases in a hospital setting in Indonesia. Using a retrospectiv...
Emergency department (ED) crowding strains patient care and drives up costs. Early decisions on the need for patient hospital admissions can allow for better planning and potentially improve throughput and alleviate crowding. We sought to prospectively compare nurse predictions with a machine learning (ML) model for hospital admissions and to evaluate whether adding the nurse prediction to ML outp...
Rapid innovation and new regulations increase the need for post-marketing surveillance of implantable devices. However, complex multi-level confoundin...
Lower respiratory tract infections (LRTIs) are a leading cause of mortality worldwide and can be difficult to diagnose in critically ill patients, as ...
During the COVID-19 pandemic, the field of infectious disease modeling advanced rapidly, with forecasting tools developed to track trends in transmiss...
Accurate identification of patients at high risk of in-hospital mortality in intensive care units (ICUs) is vital for enhancing clinical decision-maki...
Efficient patient monitoring on the medical-surgical wards is crucial to prevent significant in-hospital adverse events. Standard episodic inpatient a...
Despite increasingly widespread use of artificial intelligence-driven ambient scribes in medicine, the extent to which they may impact clinician pract...
An accurate, robust, clinically accessible, and explainable predictive model for post-stroke composite outcomes could identify high-risk patients for ...
Sub-phenotyping cardiogenic shock (CS) patients using non-traditional clustering methods represents a step toward precision medicine, potentially impr...
Accurately predicting clinical trajectories in critically ill patients remains challenging due to physiological instability and multisystem organ dysf...
Chest pain is among the most common chief complaints in Emergency Departments (EDs), and differentiating acute coronary syndrome from low-risk chest p...
The use of machine learning (ML) methods in medical prognostic modelling is gaining popularity, yet all currently available source models were designe...
The open-source release of DeepSeek-R1, a high-performing large language model (LLM), enables local deployment in Chinese hospitals. However, empirica...
Fulminant myocarditis (FM) is a rare but life-threatening pediatric condition that rapidly progresses to cardiogenic shock and fatal arrhythmias. Earl...
Acute pancreatitis (AP) is a common and potentially life-threatening gastrointestinal disease that places a substantial burden on healthcare systems w...
The modified Rankin scale (mRS) is an important metric in stroke research, often used as a primary outcome in clinical trials and observational studie...
Recently, automated Interictal Epileptiform Discharge (IED) detection has attracted significant attention as a challenging predictive data analysis ta...
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...
Perinatal depression (PND) affects 10-20% of pregnant women, with significant racial disparities in prevalence, screening, and treatment. Neighborhood...