Sepsis is the leading cause of mortality in burn patients, yet early identification remains difficult due to persistent hyperinflammatory responses and altered baseline physiology. We developed a streamlined machine learning model for early sepsis…
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Surgical site infections (SSIs), among the most frequent healthcare-associated infections, require surveillance, but traditional methods are labour-intensive. We developed machine learning (ML) and rule-based models for the semi-automated detection of deep and organ/space SSIs…
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What You Should Know:
– Caravel Bio, the first joint platform for protein discovery and delivery that enables new applications of synthetic biology, today announces its selection for a $7.8M award from the U.S. National Science…
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This study evaluates the CT-based volumetric sarcopenia index (SI) as a baseline prognostic factor for overall survival (OS) in 10,340 solid tumor patients (40% female). Automated body composition analysis was applied to internal baseline…
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Autism spectrum disorder (ASD) is a prevalent childhood-onset neurodevelopmental condition. Early diagnosis remains challenging by the time, cost, and expertise required for traditional assessments, creating barriers to timely identification. We developed an AI-based screening…
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Recent advances in unbiased metagenomic next-generation sequencing (mNGS) enable simultaneous examination of microbial and host genetic material. We developed a multimodal machine learning-based diagnostic approach to differentiate lung cancer and pulmonary infections by analyzing…
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Preoperative differentiation between xanthogranulomatous cholecystitis (XGC) and gallbladder cancer (GBC) remains challenging due to overlapping clinical and imaging features. This multicenter retrospective study developed a machine learning (ML) model, LIDGAX, using preoperative clinical, imaging,…
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Generative artificial intelligence is revolutionizing digital twin development, enabling virtual patient representations that predict health trajectories, with large language models (LLMs) showcasing untapped clinical forecasting potential. We developed the Digital Twin—Generative Pretrained Transformer (DT-GPT),…
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Globally, drug discovery and development programs are complex, multi-decade long and prohibitively expensive. Artificial intelligence (AI) and other digital health technologies have the potential to enhance and accelerate each stage of drug discovery and…
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Hospital readmission is a key quality metric, yet post-discharge interventions often yield variable results. In the first large-scale randomized evaluation of causal machine learning in a health system, we assessed whether a novel model…
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