Davin Hill

Deep Learning of Suboptimal Spirometry to Predict Respiratory Outcomes and Mortality

Michael H. Cho, Davin Hill, Max Torop, Aria Masoomi et autres

us, mx (code pays fourni par la source)

Role of CT-quantified Local Histogram Emphysema Patterns in Disease Clustering and COPD Outcomes

M.G. Chipofya, Enguday Demeke Gebeyaw, Jean A. Connor, F. Tohidian et autres

Abstract Rationale: Emphysema, a hallmark of COPD, exhibits substantial heterogeneity in severity and anatomical distribution. The relationship between specific emphysema patterns and clinical outcomes remains incompletely characterized. Objectives: To determine if distinct emphysema patterns, identified through CT-based local histogram analysis and clustering …

us (code pays fourni par la source)

OrdShap: Feature Position Importance for Sequential Black-Box Models

Davin Hill, Brian L. Hill, Aria Masoomi, Vijay S. Nori et autres

Sequential deep learning models excel in domains with temporal or sequential dependencies, but their complexity necessitates post-hoc feature attribution methods for understanding their predictions. While existing techniques quantify feature importance, they inherently assume fixed feature ordering - conflating the effects of (1) …

es, mx, us (code pays fourni par la source)

Towards trustworthy explanations in machine learning with applications in health

Davin Hill

Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes in the COPDGene and ECLIPSE cohort studies

Matthew Moll, Julian Hecker, John Platig, Jingzhou Zhang et autres

Background Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear. We aimed to define high-risk COPD subtypes using genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, …

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Axiomatic Explainer Globalness via Optimal Transport

Davin Hill, Josh Bone, Aria Masoomi, Max Torop et autres

Explainability methods are often challenging to evaluate and compare. With a multitude of explainers available, practitioners must often compare and select explainers based on quantitative evaluation metrics. One particular differentiator between explainers is the diversity of explanations for a given dataset; i.e. …

Unsupervised representation learning on high-dimensional clinical data improves genomic discovery and prediction

Taedong Yun, Justin Cosentino, Babak Behsaz, Zachary R. McCaw et autres

Although high-dimensional clinical data (HDCD) are increasingly available in biobank-scale datasets, their use for genetic discovery remains challenging. Here we introduce an unsupervised deep learning model, Representation Learning for Genetic Discovery on Low-Dimensional Embeddings (REGLE), for discovering associations between genetic variants and …

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Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes

Matthew Moll, Julian Hecker, John Platig, Jingzhou Zhang et autres

ABSTRACT Rationale Genetic variants and gene expression predict risk of chronic obstructive pulmonary disease (COPD), but their effect on COPD heterogeneity is unclear. Objectives Define high-risk COPD subtypes using both genetics (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, …

us, ca, be, au (code pays fourni par la source)

Polygenic and Transcriptional Risk Scores Identify Chronic Obstructive Pulmonary Disease Subtypes

Matthew Moll, Julian Hecker, John Platig, Auyon Ghosh et autres

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SmoothHess: ReLU Network Feature Interactions via Stein's Lemma

Max Torop, Aria Masoomi, Davin Hill, Kıvanç Köse et autres

Several recent methods for interpretability model feature interactions by looking at the Hessian of a neural network. This poses a challenge for ReLU networks, which are piecewise-linear and thus have a zero Hessian almost everywhere. We propose SmoothHess, a method of estimating …

Polygenic and transcriptional risk scores identify chronic obstructive pulmonary disease subtypes

Matthew Moll, Julian Hecker, John Platig, Auyon Ghosh et autres

Rationale: Chronic obstructive pulmonary disease (COPD) is heterogeneous. We hypothesized that COPD subgroups defined by both genetic risk (polygenic risk score, PRS) and blood gene expression (transcriptional risk score, TRS) would differ in disease severities, activities, and risk for progression. Methods: We …

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Deep Learning Utilizing Suboptimal Spirometry Data to Improve Lung Function and Mortality Prediction in the UK Biobank

Davin Hill, Max Torop, Aria Masoomi, Peter J. Castaldi et autres

Abstract Background Spirometry measures lung function by selecting the best of multiple efforts meeting pre-specified quality control (QC), and reporting two key metrics: forced expiratory volume in 1 second (FEV 1 ) and forced vital capacity (FVC). We hypothesize that discarded submaximal …

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