Tootfinder

This week's #ThursDeath is a recent find I've been listening a lot to this week, Belgium's DISCARNATION have re-released their 2023 demo as 'Mournful Incantations of Mortality'. This is a fantastic piece of gloomy, death-doom that's super growly and cavernous, just how I like it-- much like this year's Cave, Ovenhead or Annihilation Cult. Definitely one to try out, for fans o…

ClinBench-HPB: A Clinical Benchmark for Evaluating LLMs in Hepato-Pancreato-Biliary Diseases
Yuchong Li, Xiaojun Zeng, Chihua Fang, Jian Yang, Lei Zhang
https://arxiv.org/abs/2506.00095

ClinBench-HPB: A Clinical Benchmark for Evaluating LLMs in Hepato-Pancreato-Biliary Diseases
Hepato-pancreato-biliary (HPB) disorders represent a global public health challenge due to their high morbidity and mortality. Although large language models (LLMs) have shown promising performance in general medical question-answering tasks, the current evaluation benchmarks are mostly derived from standardized examinations or manually designed questions, lacking HPB coverage and clinical cases. To address these issues, we systematically eatablish an HPB disease evaluation benchmark comprising…

Joint Modeling for Learning Decision-Making Dynamics in Behavioral Experiments
Yuan Bian, Xingche Guo, Yuanjia Wang
https://arxiv.org/abs/2506.02394 https:…

Joint Modeling for Learning Decision-Making Dynamics in Behavioral Experiments
Major depressive disorder (MDD), a leading cause of disability and mortality, is associated with reward-processing abnormalities and concentration issues. Motivated by the probabilistic reward task from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, we propose a novel framework that integrates the reinforcement learning (RL) model and drift-diffusion model (DDM) to jointly analyze reward-based decision-making with response times. To acc…

Accurate and Efficient Fetal Birth Weight Estimation from 3D Ultrasound
Jian Wang, Qiongying Ni, Hongkui Yu, Ruixuan Yao, Jinqiao Ying, Bin Zhang, Xingyi Yang, Jin Peng, Jiongquan Chen, Junxuan Yu, Wenlong Shi, Chaoyu Chen, Zhongnuo Yan, Mingyuan Luo, Gaocheng Cai, Dong Ni, Jing Lu, Xin Yang
https://arxiv.org/abs/2507.00398

Accurate and Efficient Fetal Birth Weight Estimation from 3D Ultrasound
Accurate fetal birth weight (FBW) estimation is essential for optimizing delivery decisions and reducing perinatal mortality. However, clinical methods for FBW estimation are inefficient, operator-dependent, and challenging to apply in cases of complex fetal anatomy. Existing deep learning methods are based on 2D standard ultrasound (US) images or videos that lack spatial information, limiting their prediction accuracy. In this study, we propose the first method for directly estimating FBW from…

Dear men.
Please stop shaming our gender by being this kind of idiot.
Thank you.
https://beige.party/@RickiTarr/114597143638854656

Ensemble-Based Estimation of Alzheimer's Disease Incidence from Dynamic Population Reconstructions
Giulia Bertaglia, Elisa Iacomini, Alex Viguerie
https://arxiv.org/abs/2507.20179

Ensemble-Based Estimation of Alzheimer's Disease Incidence from Dynamic Population Reconstructions
We present a two-stage methodology for reconstructing Alzheimer's disease (AD) incidence over time using ensemble Kalman inversion (EKI) applied to mortality data. In the first stage, we use EKI to infer temporal trends in all-cause and Alzheimer's-specific mortality by fitting an age-structured demographic model to observed death counts. This yields posterior estimates of evolving population structure and age-specific AD mortality rates. In the second stage, we apply a back-calculation procedu…

Never forget what the world's richest man did to the world's poorest people! (Source: https://www.bu.edu/sph/news/articles/2025/tracking-anticipated-deaths-from-usaid-funding-cuts/)

Tracking Anticipated Deaths from USAID Funding Cuts
Brooke Nichols has launched online tracking tools that capture estimated increases in mortality and disease spread for HIV/AIDS, tuberculosis, malaria, and other diseases as a result of the near-total freeze in US foreign aid funding and programming.

Modeling Excess Mortality and Interest Rates using Mixed Fractional Brownian Motions
Kenneth Q. Zhou, Hongjuan Zhou
https://arxiv.org/abs/2507.19445 https://

Modeling Excess Mortality and Interest Rates using Mixed Fractional Brownian Motions
Recent studies have identified long-range dependence as a key feature in the dynamics of both mortality and interest rates. Building on this insight, we develop a novel bi-variate stochastic framework based on mixed fractional Brownian motions to jointly model their long-memory behavior and instantaneous correlation. Analytical solutions are derived under the risk-neutral measure for explicitly pricing zero-coupon bonds and extreme mortality bonds, while capturing the impact of persistent and c…

Characterization Of Diseases In Temporal Comorbidity Networks
Yuri Gardinazzi, Roger Gonzal\'ez March, Suprabhath Kalahasti, Andrea Monta\~no Ramirez, Matteo Neri, Cicely Nguyen, Giovanni Palermo, Erik Weis, Katharina Ledebur, Elma Dervi\'c
https://arxiv.org/abs/2506.22136

Characterization Of Diseases In Temporal Comorbidity Networks
Comorbidity networks, which capture disease-disease co-occurrence usually based on electronic health records, reveal structured patterns in how diseases cluster and progress across individuals. However, how these networks evolve across different age groups and how this evolution relates to properties like disease prevalence and mortality remains understudied. To address these issues, we used publicly available comorbidity networks extracted from a comprehensive dataset of 45 million Austrian ho…

Long-term impact of PM2.5 on mortality is exacerbated when wildfire events occur
Federica Spoto, Francesca Dominici, Danielle Braun, Joan A. Casey
https://arxiv.org/abs/2505.16613

Long-term impact of PM2.5 on mortality is exacerbated when wildfire events occur
There is extensive evidence that long-term exposure to all-source PM2.5 increases mortality. However, to date, no study has evaluated whether this effect is exacerbated in the presence of wildfire events. Here, we study 60+ million older US adults and find that wildfire events increase the harmful effects of long-term all-source PM2.5 exposure on mortality, providing a new and realistic conceptualization of wildfire health risks.

Characterizing Online Activities Contributing to Suicide Mortality among Youth
Aparna Ananthasubramaniam, Elyse J. Thulin, Viktoryia Kalesnikava, Silas Falde, Jonathan Kertawidjaja, Lily Johns, Alejandro Rodr\'iguez-Putnam, Emma Spring, Kara Zivin, Briana Mezuk
https://arxiv.org/abs/2507.16185 …

Characterizing Online Activities Contributing to Suicide Mortality among Youth
The recent rise in youth suicide highlights the urgent need to understand how online experiences contribute to this public health issue. Our mixed-methods approach responds to this challenge by developing a set of themes focused on risk factors for suicide mortality in online spaces among youth ages 10-24, and a framework to model these themes at scale. Using 29,124 open text summaries of death investigations between 2013-2022, we conducted a thematic analysis to identify 12 types of online act…

An Immune Infiltration-Based Risk Scoring System for Prognostic Stratification in Colorectal Adenocarcinoma
Oluwafemi Ogundare
https://arxiv.org/abs/2507.18832 https://

An Immune Infiltration-Based Risk Scoring System for Prognostic Stratification in Colorectal Adenocarcinoma
Background: Colorectal adenocarcinoma (CRC) remains a leading cause of cancer-related mortality worldwide, with variable patient outcomes despite treatment advances. Traditional prognostic methods based on clinicopathological variables alone do not fully capture the biological complexity of the disease. This study aims to develop a risk scoring system based on genes associated with tumor-infiltrating immune cells (TIIC-associated genes) to improve prognostic assessment in CRC. Methods: RNA-se…

SpiroLLM: Finetuning Pretrained LLMs to Understand Spirogram Time Series with Clinical Validation in COPD Reporting
Shuhao Mei, Yongchao Long, Shan Cao, Xiaobo Han, Shijia Geng, Jinbo Sun, Yuxi Zhou, Shenda Hong
https://arxiv.org/abs/2507.16145

SpiroLLM: Finetuning Pretrained LLMs to Understand Spirogram Time Series with Clinical Validation in COPD Reporting
Chronic Obstructive Pulmonary Disease (COPD), a major chronic respiratory disease with persistent airflow limitation, is a leading global cause of disability and mortality. Respiratory spirogram time series, routinely collected during pulmonary function tests (PFTs), play a critical role in the early detection of repsiratory diseases and in monitoring lung function over time. However, most current AI models for COPD diagnosis are limited to outputting classification results without providing a …

Estimating Average Causal Effects with Incomplete Exposure and Confounders
Lan Wen, Glen McGee
https://arxiv.org/abs/2506.21786 https://

Estimating Average Causal Effects with Incomplete Exposure and Confounders
Standard methods for estimating average causal effects require complete observations of the exposure and confounders. In observational studies, however, missing data are ubiquitous. Motivated by a study on the effect of prescription opioids on mortality, we propose methods for estimating average causal effects when exposures and potential confounders may be missing. We consider missingness at random and additionally propose several specific missing not at random (MNAR) assumptions. Under our pr…

EndoFinder: Online Lesion Retrieval for Explainable Colorectal Polyp Diagnosis Leveraging Latent Scene Representations
Ruijie Yang, Yan Zhu, Peiyao Fu, Yizhe Zhang, Zhihua Wang, Quanlin Li, Pinghong Zhou, Xian Yang, Shuo Wang
https://arxiv.org/abs/2507.17323

EndoFinder: Online Lesion Retrieval for Explainable Colorectal Polyp Diagnosis Leveraging Latent Scene Representations
Colorectal cancer (CRC) remains a leading cause of cancer-related mortality, underscoring the importance of timely polyp detection and diagnosis. While deep learning models have improved optical-assisted diagnostics, they often demand extensive labeled datasets and yield "black-box" outputs with limited interpretability. In this paper, we propose EndoFinder, an online polyp retrieval framework that leverages multi-view scene representations for explainable and scalable CRC diagnosis. First, we …

Learning Structured Population Models from Data with WSINDy
Rainey Lyons, Vanja Dukic, David M. Bortz
https://arxiv.org/abs/2506.24101 https://

Learning Structured Population Models from Data with WSINDy
In the context of population dynamics, identifying effective model features, such as fecundity and mortality rates, is generally a complex and computationally intensive process, especially when the dynamics are heterogeneous across the population. In this work, we propose a Weak form Scientific Machine Learning-based method for selecting appropriate model ingredients from a library of scientifically feasible functions used to model structured populations. This method uses extensions of the Weak…

BreastDCEDL: Curating a Comprehensive DCE-MRI Dataset and developing a Transformer Implementation for Breast Cancer Treatment Response Prediction
Naomi Fridman, Bubby Solway, Tomer Fridman, Itamar Barnea, Anat Goldshtein
https://arxiv.org/abs/2506.12190

BreastDCEDL: Curating a Comprehensive DCE-MRI Dataset and developing a Transformer Implementation for Breast Cancer Treatment Response Prediction
Breast cancer remains a leading cause of cancer-related mortality worldwide, making early detection and accurate treatment response monitoring critical priorities. We present BreastDCEDL, a curated, deep learning-ready dataset comprising pre-treatment 3D Dynamic Contrast-Enhanced MRI (DCE-MRI) scans from 2,070 breast cancer patients drawn from the I-SPY1, I-SPY2, and Duke cohorts, all sourced from The Cancer Imaging Archive. The raw DICOM imaging data were rigorously converted into standardized…

Predicting Short-Term Mortality in Elderly ICU Patients with Diabetes and Heart Failure: A Distributional Inference Framework
Junyi Fan, Shuheng Chen, Li Sun, Yong Si, Elham Pishgar, Kamiar Alaei, Greg Placencia, Maryam Pishgar
https://arxiv.org/abs/2506.15058

Predicting Short-Term Mortality in Elderly ICU Patients with Diabetes and Heart Failure: A Distributional Inference Framework
Elderly ICU patients with coexisting diabetes mellitus and heart failure experience markedly elevated short-term mortality, yet few predictive models are tailored to this high-risk group. Diabetes mellitus affects nearly 30% of U.S. adults over 65, and significantly increases the risk of heart failure. When combined, these conditions worsen frailty, renal dysfunction, and hospitalization risk, leading to one-year mortality rates of up to 40%. Despite their clinical burden and complexity, no est…

Replaced article(s) found for physics.soc-ph. https://arxiv.org/list/physics.soc-ph/new
[1/1]:
- Projecting Multimorbidity and Mortality under Demographic Change and Preventive Interventions
Katharina Ledebur, Alexandra Kautzky-Willer, Stefan Thurner, Peter Klimek

This https://arxiv.org/abs/2505.12485 has been replaced.
initial toot: https://mastoxiv.page/@arX…

A Reduced-Order CFD Approach for Intermediate grade Coronary Arterial Clinical Parameter Assessment A Reduced-Order CFD Approach for Intermediate grade Coronary Arterial Clinical Parameter Assessment
Coronary heart disease (CHD) remains a top reason of mortality worldwide. This study introduces a novel approach by integrating patient-specific Multi-slice CT scans into CAD models and employing a one-dimensional numerical framework to assess varying degrees of stenosis. The computational analysis encompasses the entire arterial tree, with a particular focus on stenosed coronary arteries modelled using an analytical equation. One-dimensional characteristic equations, utilizing forward and back…

Viele Menschen verstehen #Warnungen wie „#Hitzeindex“ oder „#Hitzewarnung“ falsch. Das zeigt eine US-Studie.
Fast die Hälfte hält den Hitzeindex für die reine

Bridging the Gap between National Weather Service Heat Terminology and Public Understanding
Abstract Clearly communicating heat warning information to the public is an important way to reduce heat mortality and morbidity. However, heat communication interventions from the National Weather Service commonly include technical and scientific terms, otherwise known as jargon. These terms, such as heat advisory or heat index, may not be understood by the public. Given the importance of message understanding in protective action decision-making, the purpose of this study is to assess how the…

Multivariate Spatio-temporal Modelling for Completing Cancer Registries and Forecasting Incidence
Garazi Retegui, Jaione Etxeberria, Mar\'ia Dolores Ugarte
https://arxiv.org/abs/2507.21714

Multivariate Spatio-temporal Modelling for Completing Cancer Registries and Forecasting Incidence
Cancer data, particularly cancer incidence and mortality, are fundamental to understand the cancer burden, to set targets for cancer control and to evaluate the evolution of the implementation of a cancer control policy. However, the complexity of data collection, classification, validation and processing result in cancer incidence figures often lagging two to three years behind the calendar year. In response, national or regional population-based cancer registries (PBCRs) are increasingly inte…

From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification
Comte Valentin, Gemma Piella, Mario Ceresa, Miguel A. Gonzalez Ballester
https://arxiv.org/abs/2507.05783

From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification
Cardiac diseases are among the leading causes of morbidity and mortality worldwide, which requires accurate and timely diagnostic strategies. In this study, we introduce an innovative approach that combines deep learning image registration with physics-informed regularization to predict the biomechanical properties of moving cardiac tissues and extract features for disease classification. We utilize the energy strain formulation of Neo-Hookean material to model cardiac tissue deformations, opti…

Replaced article(s) found for cs.DM. https://arxiv.org/list/cs.DM/new
[1/1]:
On shortest products for nonnegative matrix mortality
https://

Global, Regional, and National Burden of Chronic Kidney Disease Attributable to High Body Mass Index (BMI) among Individuals Aged 20-54 Years from 1990 to 2021: An Analysis of the Global Burden of Disease Study
Yu Chen, Guangxi Wu
https://arxiv.org/abs/2507.23537

Global, Regional, and National Burden of Chronic Kidney Disease Attributable to High Body Mass Index (BMI) among Individuals Aged 20-54 Years from 1990 to 2021: An Analysis of the Global Burden of Disease Study
Background:Chronic kidney disease is one of the most prevalent non-communicable health issues globally, and high body mass index plays a significant role in the onset and progression of chronic kidney disease. Methods: Data on the disease burden attributable to high body mass index were retrieved from the 2021 Global Burden of Disease, Injuries, and Risk Factors Study . The global cases, age-standardized mortality rate , and age-standardized disability-adjusted life years attributable to high b…

AGE-US: automated gestational age estimation based on fetal ultrasound images
C\'esar D\'iaz-Parga, Marta Nu\~nez-Garcia, Maria J. Carreira, Gabriel Bernardino, Nicol\'as Vila-Blanco
https://arxiv.org/abs/2506.16256

AGE-US: automated gestational age estimation based on fetal ultrasound images
Being born small carries significant health risks, including increased neonatal mortality and a higher likelihood of future cardiac diseases. Accurate estimation of gestational age is critical for monitoring fetal growth, but traditional methods, such as estimation based on the last menstrual period, are in some situations difficult to obtain. While ultrasound-based approaches offer greater reliability, they rely on manual measurements that introduce variability. This study presents an interpre…

This https://arxiv.org/abs/2505.13185 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qfi…

Filtering in a hazard rate change-point model with financial and life-insurance applications
This paper develops a continuous-time filtering framework for estimating a hazard rate subject to an unobservable change-point. This framework arises naturally in both financial and insurance applications, where the default intensity of a firm or the mortality rate of an individual may experience a sudden jump at an unobservable time, representing, for instance, a shift in the firm's risk profile or a deterioration in an individual's health status. By employing a progressive enlargement of filt…

Interpretable Machine Learning Model for Early Prediction of 30-Day Mortality in ICU Patients With Coexisting Hypertension and Atrial Fibrillation: A Retrospective Cohort Study
Shuheng Chen, Yong Si, Junyi Fan, Li Sun, Greg Placencia, Elham Pishgar, Kamiar Alaei, Maryam Pishgar
https://arxiv.org/abs/2506.15036

Interpretable Machine Learning Model for Early Prediction of 30-Day Mortality in ICU Patients With Coexisting Hypertension and Atrial Fibrillation: A Retrospective Cohort Study
Hypertension and atrial fibrillation (AF) often coexist in critically ill patients, significantly increasing mortality rates in the ICU. Early identification of high-risk individuals is crucial for targeted interventions. However, limited research has focused on short-term mortality prediction for this subgroup. This study analyzed 1,301 adult ICU patients with hypertension and AF from the MIMIC-IV database. Data including chart events, laboratory results, procedures, medications, and demogra…

Improving Surgical Risk Prediction Through Integrating Automated Body Composition Analysis: a Retrospective Trial on Colectomy Surgery
Hanxue Gu, Yaqian Chen, isoo Lee, Diego Schaps, Regina Woody, Roy Colglazier, Maciej A. Mazurowski, Christopher Mantyh
https://arxiv.org/abs/2506.11996

Improving Surgical Risk Prediction Through Integrating Automated Body Composition Analysis: a Retrospective Trial on Colectomy Surgery
Objective: To evaluate whether preoperative body composition metrics automatically extracted from CT scans can predict postoperative outcomes after colectomy, either alone or combined with clinical variables or existing risk predictors. Main outcomes and measures: The primary outcome was the predictive performance for 1-year all-cause mortality following colectomy. A Cox proportional hazards model with 1-year follow-up was used, and performance was evaluated using the concordance index (C-index…

Adding a fecundity-survival trade-off to a discrete population model with maturation delay
Christopher J. Greyson-Gaito, Sabrina H. Streipert, Gail S. K. Wolkowicz
https://arxiv.org/abs/2507.18765

Adding a fecundity-survival trade-off to a discrete population model with maturation delay
Although maturation delays of new individuals are frequently added in population models, researchers rarely account for the mortality of individuals between birth and maturity. Previous studies have added mortality of immature individuals to the maturation delay in discrete population models, finding that increasing the delay results in a decreased population level at equilibrium. Furthermore, increasing the maturation delay beyond a critical threshold ultimately leads to the extinction of the …

Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography
Farnoush Bayatmakou, Reza Taleei, Nicole Simone, Arash Mohammadi
https://arxiv.org/abs/2507.17662

Mammo-Mamba: A Hybrid State-Space and Transformer Architecture with Sequential Mixture of Experts for Multi-View Mammography
Breast cancer (BC) remains one of the leading causes of cancer-related mortality among women, despite recent advances in Computer-Aided Diagnosis (CAD) systems. Accurate and efficient interpretation of multi-view mammograms is essential for early detection, driving a surge of interest in Artificial Intelligence (AI)-powered CAD models. While state-of-the-art multi-view mammogram classification models are largely based on Transformer architectures, their computational complexity scales quadratic…

Unmasking inequility: socio-economic determinants and gender disparities in Maharashtra and India's health outcomes -- Insights from NFHS-5
Sharmishtha Raghuvanshi, Supriya Sanjay Nikam, Manisha Karne, Satyanarayan Kishan Kothe
https://arxiv.org/abs/2506.08206

Unmasking inequility: socio-economic determinants and gender disparities in Maharashtra and India's health outcomes -- Insights from NFHS-5
This research examines the persistent challenge of health inequalities in India, departing from the conventional focus on aggregate improvements in mortality rates. While India has achieved progress in overall health indicators since independence, the distribution of health outcomes remains uneven, a fact starkly highlighted by the COVID-19 pandemic. This study investigates the socio-economic determinants of health disparities using the National Family and Health Survey (NFHS)-5 data from 2019-…

A Bayesian Spatio-Temporal Model of Temperature- and Humidity-Related Mortality Using High-Resolution Climate Data
Corinna Perchtold, Julia Eisenberg, Philipp Otto
https://arxiv.org/abs/2507.12643

A Bayesian Spatio-Temporal Model of Temperature- and Humidity-Related Mortality Using High-Resolution Climate Data
In this study, we introduce a novel and comprehensive extension of a Bayesian spatio-temporal disease mapping model that explicitly accounts for gender-specific effects of meteorological exposures. Leveraging fine-scale weekly mortality and high-resolution climate data from Austria (2002 to 2019), we assess how interactions between temperature, humidity, age, and gender influence mortality patterns. Our approach goes beyond conventional modelling by capturing complex dependencies through struct…

Filtering in a hazard rate change-point model with financial and life-insurance applications
Matteo Buttarazzi, Claudia Ceci
https://arxiv.org/abs/2505.13185

Filtering in a hazard rate change-point model with financial and life-insurance applications
This paper develops a continuous-time filtering framework for estimating a hazard rate subject to an unobservable change-point. This framework arises naturally in both financial and insurance applications, where the default intensity of a firm or the mortality rate of an individual may experience a sudden jump at an unobservable time, representing, for instance, a shift in the firm's risk profile or a deterioration in an individual's health status. By employing a progressive enlargement of filt…

Integrated Open-Source Framework for Simulation of Transcatheter Pulmonary Valves in Native Right Ventricular Outflow Tracts
Christopher N. Zelonis, Jalaj Maheshwari, Wensi Wu, Steve A. Maas, Seda Aslan, Kyle Sunderland, Stephen Ching, Ashley Koluda, Yuval Barak-Corren, Nicolas Mangine, Patricia M. Sabin, Andras Lasso, Devin W. Laurence, Christian Herz, Matthew J. Gillespie, Jeffrey A. Weiss, Matthew A. Jolley

Integrated Open-Source Framework for Simulation of Transcatheter Pulmonary Valves in Native Right Ventricular Outflow Tracts
Background - Pulmonary insufficiency is a consequence of transannular patch repair in Tetralogy of Fallot (ToF), leading to late morbidity and mortality. Transcatheter native outflow tract pulmonary valve replacement (TPVR) has become common, but assessment of patient candidacy and selection of the optimal device remains challenging. We demonstrate an integrated open-source workflow for simulation of TPVR in image-derived models to inform device selection. Methods - Machine learning-based seg…

Improving Diagnostic Accuracy of Pigmented Skin Lesions With CNNs: an Application on the DermaMNIST Dataset
Nerma Kadric, Amila Akagic, Medina Kapo
https://arxiv.org/abs/2507.12961

Improving Diagnostic Accuracy of Pigmented Skin Lesions With CNNs: an Application on the DermaMNIST Dataset
Pigmented skin lesions represent localized areas of increased melanin and can indicate serious conditions like melanoma, a major contributor to skin cancer mortality. The MedMNIST v2 dataset, inspired by MNIST, was recently introduced to advance research in biomedical imaging and includes DermaMNIST, a dataset for classifying pigmented lesions based on the HAM10000 dataset. This study assesses ResNet-50 and EfficientNetV2L models for multi-class classification using DermaMNIST, employing transf…

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data
Tinghuan Li, Shuheng Chen, Junyi Fan, Elham Pishgar, Kamiar Alaei, Greg Placencia, Maryam Pishgar
https://arxiv.org/abs/2506.03209

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data
Postoperative stroke remains a critical complication in elderly surgical intensive care unit (SICU) patients, contributing to prolonged hospitalization, elevated healthcare costs, and increased mortality. Accurate early risk stratification is essential to enable timely intervention and improve clinical outcomes. We constructed a combined cohort of 19,085 elderly SICU admissions from the MIMIC-III and MIMIC-IV databases and developed an interpretable machine learning (ML) framework to predict in…

Penalised spline estimation of covariate-specific time-dependent ROC curves
Mar\'ia Xos\'e Rodr\'iguez-\'Alvarez, Vanda In\'acio
https://arxiv.org/abs/2506.13604

Penalised spline estimation of covariate-specific time-dependent ROC curves
The identification of biomarkers with high predictive accuracy is a crucial task in medical research, as it can aid clinicians in making early decisions, thereby reducing morbidity and mortality in high-risk populations. Time-dependent receiver operating characteristic (ROC) curves are the main tool used to assess the accuracy of prognostic biomarkers for outcomes that evolve over time. Recognising the need to account for patient heterogeneity when evaluating the accuracy of a prognostic biomar…

Forecasting sub-population mortality using credibility theory
Mathias Lindholm, Gabriele Pittarello
https://arxiv.org/abs/2507.12330 https://

Forecasting sub-population mortality using credibility theory
The focus of the present paper is to forecast mortality rates for small sub-populations that are parts of a larger super-population. In this setting the assumption is that it is possible to produce reliable forecasts for the super-population, but the sub-populations may be too small or lack sufficient history to produce reliable forecasts if modelled separately. This setup is aligned with the ideas that underpin credibility theory, and in the present paper the classical credibility theory appro…

Beyond water limitation in vegetation-autotoxicity patterning: a cross-diffusion model
Francesco Giannino, Annalisa Iuorio, Cinzia Soresina
https://arxiv.org/abs/2506.03981

Beyond water limitation in vegetation-autotoxicity patterning: a cross-diffusion model
Many mathematical models describing vegetation patterns are based on biomass--water interactions, due to the impact of this limited resource in arid and semi-arid environments. However, in recent years, a novel biological factor called autotoxicity has proved to play a key role in vegetation spatiotemporal dynamics, particularly by inhibiting biomass growth and increasing its natural mortality rate. In a standard reaction-diffusion framework, biomass-toxicity dynamics alone are unable to suppor…

This https://arxiv.org/abs/2506.00095 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

ClinBench-HPB: A Clinical Benchmark for Evaluating LLMs in Hepato-Pancreato-Biliary Diseases
Hepato-pancreato-biliary (HPB) disorders represent a global public health challenge due to their high morbidity and mortality. Although large language models (LLMs) have shown promising performance in general medical question-answering tasks, the current evaluation benchmarks are mostly derived from standardized examinations or manually designed questions, lacking HPB coverage and clinical cases. To address these issues, we systematically eatablish an HPB disease evaluation benchmark comprising…

FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiography
Nan Mu, Ruiqi Song, Xiaoning Li, Zhihui Xu, Jingfeng Jiang, Chen Zhao
https://arxiv.org/abs/2506.11454

FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiography
Background: Coronary artery disease (CAD) remains one of the leading causes of mortality worldwide. Precise segmentation of coronary arteries from invasive coronary angiography (ICA) is critical for effective clinical decision-making. Objective: This study aims to propose a novel deep learning model based on frequency-domain analysis to enhance the accuracy of coronary artery segmentation and stenosis detection in ICA, thereby offering robust support for the stenosis detection and treatment of …

This https://arxiv.org/abs/2506.00095 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

ClinBench-HPB: A Clinical Benchmark for Evaluating LLMs in Hepato-Pancreato-Biliary Diseases
Hepato-pancreato-biliary (HPB) disorders represent a global public health challenge due to their high morbidity and mortality. Although large language models (LLMs) have shown promising performance in general medical question-answering tasks, the current evaluation benchmarks are mostly derived from standardized examinations or manually designed questions, lacking HPB coverage and clinical cases. To address these issues, we systematically eatablish an HPB disease evaluation benchmark comprising…

Analyzing Pension Fund Mortality with Gaussian Processes in a Sub Population Framework
Eduardo F. L. de Melo, Michael Ludkovski, Rodrigo S. Targino
https://arxiv.org/abs/2506.03584

Analyzing Pension Fund Mortality with Gaussian Processes in a Sub Population Framework
Pension fund populations often have mortality experiences that are substantially different from the national benchmark. In a motivating case study of Brazilian corporate pension funds, pensioners are observed to have mortality that is 40-55% below the national average, due to the underlying socioeconomic disparities. Direct analysis of a pension fund population is challenging due to very sparse data, with age-specific annual death counts often in low single digits. We design and study a collect…

Hybrid-View Attention for csPCa Classification in TRUS
Zetian Feng, Juan Fu, Xuebin Zou, Hongsheng Ye, Hong Wu, Jianhua Zhou, Yi Wang
https://arxiv.org/abs/2507.03421

Hybrid-View Attention for csPCa Classification in TRUS
Prostate cancer (PCa) is a leading cause of cancer-related mortality in men, and accurate identification of clinically significant PCa (csPCa) is critical for timely intervention. Transrectal ultrasound (TRUS) is widely used for prostate biopsy; however, its low contrast and anisotropic spatial resolution pose diagnostic challenges. To address these limitations, we propose a novel hybrid-view attention (HVA) network for csPCa classification in 3D TRUS that leverages complementary information fr…

ViTaL: A Multimodality Dataset and Benchmark for Multi-pathological Ovarian Tumor Recognition
You Zhou, Lijiang Chen, Guangxia Cui, Wenpei Bai, Yu Guo, Shuchang Lyu, Guangliang Cheng, Qi Zhao
https://arxiv.org/abs/2507.04383

ViTaL: A Multimodality Dataset and Benchmark for Multi-pathological Ovarian Tumor Recognition
Ovarian tumor, as a common gynecological disease, can rapidly deteriorate into serious health crises when undetected early, thus posing significant threats to the health of women. Deep neural networks have the potential to identify ovarian tumors, thereby reducing mortality rates, but limited public datasets hinder its progress. To address this gap, we introduce a vital ovarian tumor pathological recognition dataset called \textbf{ViTaL} that contains \textbf{V}isual, \textbf{T}abular and \text…

Benchmarking Waitlist Mortality Prediction in Heart Transplantation Through Time-to-Event Modeling using New Longitudinal UNOS Dataset
Yingtao Luo, Reza Skandari, Carlos Martinez, Arman Kilic, Rema Padman
https://arxiv.org/abs/2507.07339

Benchmarking Waitlist Mortality Prediction in Heart Transplantation Through Time-to-Event Modeling using New Longitudinal UNOS Dataset
Decisions about managing patients on the heart transplant waitlist are currently made by committees of doctors who consider multiple factors, but the process remains largely ad-hoc. With the growing volume of longitudinal patient, donor, and organ data collected by the United Network for Organ Sharing (UNOS) since 2018, there is increasing interest in analytical approaches to support clinical decision-making at the time of organ availability. In this study, we benchmark machine learning models …

Multi-country forecasts of age distribution of deaths: Life expectancy and annuity valuation
Han Lin Shang, Steven Haberman
https://arxiv.org/abs/2507.04303

Multi-country forecasts of age distribution of deaths: Life expectancy and annuity valuation
We investigate two transformations within the framework of compositional data analysis for forecasting the age distribution of death counts. Drawing on age-specific period life-table death counts from 24 countries in the Human Mortality Database, we assess and compare the point and interval forecast accuracy of the two transformations. Enhancing the forecast accuracy of period life-table death counts holds significant value for demographers, who rely on such forecasts to estimate survival proba…