An evidence-grounded LLM ensemble filters pathology from clinical radiology reports to create large healthy cohorts and build age-, sex-, and contrast-adjusted reference charts for 106 CT structures using generalized additive models.
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A zero-inflated Poisson tensor model integrating low-rank CP structure, latent cluster embeddings, and smoothness is introduced for sparse single-cell Hi-C count tensors, with a Bayes-optimal zero distinction procedure, identifiability results, and consistency rates.
The paper introduces a penalized distributed lag non-linear Lee-Carter framework that adds temperature and influenza effects, negative binomial overdispersion, SARIMA dynamics, and copula dependence for improved regional weekly mortality forecasts on French data 1990-2019.
Implicit neural representations enable stable, resolution-independent reconstruction of continuous environmental fields from sparse and irregular ecological data.
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Whole-body CT attenuation and volume charts from routine clinical scans via evidence-grounded LLM report filtering
An evidence-grounded LLM ensemble filters pathology from clinical radiology reports to create large healthy cohorts and build age-, sex-, and contrast-adjusted reference charts for 106 CT structures using generalized additive models.
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Zero-inflated modeling with smoothing on counting tensors
A zero-inflated Poisson tensor model integrating low-rank CP structure, latent cluster embeddings, and smoothness is introduced for sparse single-cell Hi-C count tensors, with a Bayes-optimal zero distinction procedure, identifiability results, and consistency rates.
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A penalized distributed lag non-linear Lee-Carter framework for regional weekly mortality forecasting
The paper introduces a penalized distributed lag non-linear Lee-Carter framework that adds temperature and influenza effects, negative binomial overdispersion, SARIMA dynamics, and copula dependence for improved regional weekly mortality forecasts on French data 1990-2019.
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Implicit neural representations as a coordinate-based framework for continuous environmental field reconstruction from sparse ecological observations
Implicit neural representations enable stable, resolution-independent reconstruction of continuous environmental fields from sparse and irregular ecological data.