Bioterrorism

Causal Machine Learning Analysis of All-Cause Mortality in Japanese Atomic-Bomb Survivors

The health consequences of ionizing radiation have long been studied, yet significant uncertainties remain, particularly at low doses. In particular, traditional dose-response models such as linear, linear-quadratic, threshold, or hormesis models, all impose specific assumptions about low-dose effects. In addition, while the goal of radiation epidemiological studies is ideally to uncover causal re...

Bayesian hybrid statistical and machine learning models for dengue forecasting in Bangladesh: Temporal and spatial analysis for an early warning system

Dengue remains a major public health concern in Bangladesh, yet reliable forecasting models that integrate climatic and demographic drivers are limited. Developing an early warning system (EWS) capable of anticipating outbreaks is critical for effective prevention and control. We analysed hospital-based dengue surveillance data covering admissions from January 2000 to August 2025 alongside climati...

HGACL-DRP: Heterogeneous Graph Attention Dual-Perturbation Contrastive Learning Network for Drug Response Prediction

The marked heterogeneity of cancer poses a substantial challenge to precision drug therapy, resulting in considerable variability in patient responses...

Socioeconomic and Behavioral Drivers of Geographic Disparities in U.S. Cardiovascular Mortality: A Machine Learning Analysis

Substantial geographic disparities in cardiovascular disease (CVD) mortality persist across the United States. The extent to which “place” reflects un...

Machine learning predicts treatment response to nusinersen in non-sitter Spinal Muscular Atrophy (SMA)

Nusinersen has substantially increased survival and improved disease progression in Spinal Muscular Atrophy (SMA) patients. However, treatment respons...

Machine Learning and Probabilistic Approaches for Forecasting Infectious Disease Transmission and Cases

Forecasting the effective reproductive number (Rt) and infection case counts is critical for guiding public health responses. We developed a machine l...

Machine learning to phenotype pain and predict response to pain interventions among young adults with irritable bowel syndrome

Irritable bowel syndrome (IBS) is a prevalent disorder whose most debilitating symptom is pain. The complex, multifactorial nature of IBS pain leads t...

Multiregional CT Features Improve Prediction of Immunotherapy Response in Advanced Melanoma

Immunotherapy has improved outcomes for advanced-stage melanoma, however, predictive biomarkers remain limited. We evaluated whether computed tomograp...

A Prospective Real-time Early Warning System to Anticipate Onsets and Peaks of Respiratory Diseases Outbreaks at the State Level in the U.S. A Transfer Learning Approach Leveraging Digital Traces

Respiratory disease outbreaks burden U.S. healthcare systems with over one million hospitalizations annually, yet current surveillance systems lag 1-2...

Understanding the Relationship Between Germ Layer Origin and Cancer Therapy Response: A Systematic Review

Cancer therapeutic response patterns may be fundamentally influenced by embryonic germ layer origin. Emerging evidence suggests mesoderm-derived malig...

EEG-Based Prediction of rTMS Treatment Response in Depression: Nonlinear Features and Machine Learning with Minimal Electrode

Repetitive transcranial magnetic stimulation (rTMS) is an established intervention for treatment-resistant depression, but response rates remain highl...

Radiologic, Pathologic, and Deep Learning Predictors of Response to Immune Checkpoint Blockade in Renal Cell Carcinoma Patients Undergoing Post-Treatment Nephrectomy

Response assessment of primary kidney tumors in the consolidation cytoreductive and neoadjuvant settings offers a unique opportunity to inform postope...

A three-dose MVA-BN mpox vaccination series improves the quality of anti-monkeypox virus immunity

The 2022 global outbreak of clade IIb mpox represented a turning point in public health’s handling of poxviruses. The primary vaccine available for pr...

Generalizable AI predicts immunotherapy outcomes across cancers and treatments

Immune checkpoint inhibitors have become standard care across many cancers, but most patients do not respond. Predicting response remains challenging ...

Fairness in infectious disease modeling

The concept of fairness has been extensively examined within the domains of Machine Learning and Artificial Intelligence more broadly. It remains, how...

Pandemic-Potential Viruses are a Blind Spot for Frontier Open-Source LLMs

We study large language models (LLMs) for front-line, pre-diagnostic infectious-disease triage, a critically understudied stage in clinical interventi...

Evaluation of large language model chatbot responses to psychotic prompts

The large language model (LLM) chatbot product ChatGPT has accumulated 800 million weekly users since its 2022 launch. In 2025, several media outlets ...

Data Extraction from Oncology Imaging Reports by Large Language Models: A Comparative Accuracy Study

Manual data extraction from clinical text is resource intensive. Locally hosted large language models (LLMs) may offer a privacy-preserving solution, ...

Machine learning-driven prediction of opioid and stimulant-related drug overdose fatalities: Analysis of the potential fourth wave

Between 2010 and 2021, fentanyl and stimulants co-involved deaths increased from 0.6% to 32.3% of all overdose deaths in the U.S. The Centers for Dise...

Operational Survival Deficit of Neoadjuvant Chemotherapy in Early-Stage Breast Cancer: A Target Trial Emulation and Causal Machine Learning Study

Neoadjuvant chemotherapy (NAC) is the standard of care for locally advanced breast cancer. However, the disconnect between efficacy in randomized tria...