Sepsis is a dysregulated host response to infection with high mortality and morbidity. Early detection and intervention have been shown to improve patient outcomes, but existing computational models relying on structured electronic health record…
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In treating malignant cerebral edema after a large middle cerebral artery stroke, clinicians need quantitative tools for real-time risk assessment. Existing predictive models typically estimate risk at one, early time point, failing to account…
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Running biomechanics studies the mechanical forces experienced during running to improve performance and prevent injuries. This study presents the development of a digital twin for predicting bone stress in runners. The digital twin leverages…
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Cardiotocography (CTG) is essential for monitoring high-risk pregnancies, yet perinatal asphyxia prediction accuracy remains limited to 50–55%. Regions of artifacts (missing valid signals)-including signal processing aberrations-possibly contribute to this limitation, highlighted by 40% of…
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Septic shock is one of the most lethal conditions in ICU, and early risk prediction may help reduce mortality. We developed a TOPSIS-based Classification Fusion (TCF) model to predict mortality risk in septic shock…
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Accurate assessment of progression and disease course in multiple sclerosis (MS) is vital for timely and appropriate clinical intervention. The gradual transition from relapsing-remitting MS (RRMS) to secondary progressive MS (SPMS) is often diagnosed…
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Valuable Public Health Use Cases for AI
AI can analyze massive, varied data sets — from sources such as electronic health records, demographic data, medical sensors, health journals and even social media — much faster…
We developed and validated models to predict 1- and 2-week risk of non-elective hospitalization (NEH) and sepsis hospitalization following outpatient clinic, emergency department treat and release (EDTR), or hospitalization encounters. We employed data from…
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Sepsis real-time prediction models (SRPMs) provide timely alerts and may improve patient outcomes but face limited clinical adoption due to inconsistent validation methods and potential biases. Comprehensive evaluation, including external full-window validation with model-…
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Preeclampsia (PE), a severe hypertensive disorder during pregnancy, significantly contributes to maternal and neonatal mortality. Existing prediction biomarkers are often invasive and expensive, hindering their widespread application. This study introduces PROMPT (Preeclampsia Risk factor…
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