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Borrowing From the Future: Enhancing Early Risk Assessment through Contrastive Learning
Risk assessments for a pediatric population are often conducted across multiple stages. For example, clinicians may evaluate risks prenatally, at birth, and during Well-Child visits. Although predictions made at later stages typically achieve higher precision, it is clinically desirable to make reliable risk assessments as early as possible. Therefore, this study focuses on improving prediction…
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Microfluidic Digital Twin for Enhanced Single-Cell Analysis
Advancing single-cell analysis requires tools that not only enable precise experimental measurements but also offer predictive capabilities to guide device optimization and expand experimental possibilities. This study addresses this need by developing a digital twin framework for mechano-node-pore sensing (mechano-NPS), a high-throughput microfluidic platform for single-cell analysis. By creating a virtual replica that integrates models…
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Identifying Antibiotic Effects of Investigational Drugs on Commensal Bacteria with Machine Learning
Many human-targeted medications have been found to impact the composition of patients’ gastrointestinal microbiomes, which has been proposed as an unrecognized source of drug side effects, comorbidities, and reduced treatment efficiencies. However, current methods to detect such drug effects on the microbiome largely rely on patient sample analysis or in vitro high-throughput screening – which…
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Diagnosing our datasets: How does my language model learn clinical information?
Large language models (LLMs) have performed well across various clinical natural language processing tasks, despite not being directly trained on electronic health record (EHR) data. In this work, we examine how popular open-source LLMs learn clinical information from large mined corpora through two crucial but understudied lenses: (1) their interpretation of clinical jargon, a foundational…
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Development of a Core Critical Care Data Dictionary With Common Data Elements to Characterize Critical Illness and Injuries Using a Modified Delphi Method
OBJECTIVES: To develop the first core Critical Care Data Dictionary (C2D2) with common data elements (CDEs) to characterize critical illness and injuries. DESIGN: Group consensus process using modified Delphi approach. SETTING: Electronic surveys and in-person meetings. SUBJECTS: A multidisciplinary workgroup of clinicians and researchers with expertise in the care of the critically ill and injured….
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Beneath the surface: revealing deep-tissue blood flow in human subjects with massively parallelized diffuse correlation spectroscopy
Diffuse correlation spectroscopy (DCS) allows label-free, non-invasive investigation of microvascular dynamics deep within tissue, such as cerebral blood flow (CBF). However, the signal-to-noise ratio (SNR) in DCS limits its effective cerebral sensitivity in adults, in which the depth to the brain, through the scalp and skull, is substantially larger than in infants.Therefore, we aim to…
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Impact of inlet velocity waveform shape on hemodynamics
Monitoring disease development in arteries, which supply oxygen and nutrients to the body, is crucial and can be assessed using hemodynamic metrics. Hemodynamic metrics can be calculated via computational fluid dynamic simulation of patient-specific geometries. These simulations are known to be heavily influenced by boundary conditions, such as time-dependent inlet flow. However, the effects of…
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Stochastic modeling of a class of stored energy functions for incompressible hyperelastic materials with uncertainties
In this Note, we address the construction of a class of stochastic Ogden’s stored energy functions associated with incompressible hyperelastic materials. The methodology relies on the maximum entropy principle, which is formulated under constraints arising in part from existence theorems in nonlinear elasticity. More specifically, constraints related to both polyconvexity and consistency with linearized elasticity…
