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…
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…
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…
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…
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….
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…
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…
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…
This paper is devoted to the modeling of compressible hyperelastic materials whose response functions exhibit uncertainties at some scale of interest. The construction of parametric probabilistic representations for the Ogden class of stored energy functions is specifically considered and formulated within the framework of Information Theory. The overall methodology relies on the principle of maximum…