Patterns

Data scarcity and stylistic heterogeneity pose major challenges for emotion intensity classification. This paper presents a cross-dataset augmentation framework that leverages prompt-conditioned generative models alongside deterministic and heuristic transformations to synthesize target-style examples for improved transfer learning. We introduce a unified taxonomy of augmentation strategies--Heuristic Lexical Perturbation (HLA), Prompt-Conditioned Generative Augmentation (CGA), Sequential Hybrid...

Malaria affects almost 263 million people worldwide, most of whom live in sub-Saharan countries. In a strategy to reduce malaria-related mortality and limit transmission, diagnosis in endemic areas needs to be immediately available on the field, easy to perform and cheap. Therefore, it currently heavily relies on microscopic examination of blood smears. However, several studies comparing the sensitivity of this approach with qPCR, considered as the most sensitive method albeit not available on t...

As of early 2026, over 115 million US adults (more than 1 in 3) have prediabetes, a condition with an annual conversion rate of 5%-10% to type 2 diabetes. Total diabetes (diagnosed and undiagnosed) affects approximately 40.1 million Americans, or 12% of the population, with roughly 1.5 million new cases diagnosed annually. Continuous Glucose Monitoring (CGM) provides real-time, 24/7 insights into glycemic variability, detecting dangerous highs, lows, and trends that HbA1c (a 3-month average) mis...

Medical errors are one of the leading causes of death in the United States. Several public databases have been built to record patient safety events across healthcare systems to better understand and improve safety hazards. These reports typically include both structured fields (e.g., event type, device, manufacturer) and unstructured data elements (free text narrative of what happened). The structured fields are usually restricted to a limited number of categories, whereas the unstructured fiel...

Large language models (LLMs) increasingly operate in high-stakes settings including healthcare and medicine, where demographic attributes such as race and ethnicity may be explicitly stated or implicitly inferred from text. However, existing studies primarily document outcome-level disparities, offering limited insight into internal mechanisms underlying these effects. We present a mechanistic study of how race and ethnicity are represented and operationalized within LLMs. Using two publicly ava...

BackgroundTuberculosis (TB) remains a major public health challenge in Nepal, with incidence rates substantially higher than global estimates. Accurate forecasting of TB incidence is essential for early warning systems, resource allocation, and targeted interventions. This study aimed to develop and validate a hybrid Seasonal Autoregressive Integrated Moving Average (SARIMA) and Convolutional Neural Network Auto-Regressive (CNNAR) model for TB incidence forecasting in Nepal. MethodsMonthly TB i...

Medical Multimodal Large Language Models (Medical MLLMs) have achieved remarkable progress in specialized medical tasks; however, research into their safety has lagged, posing potential risks for real-world deployment. In this paper, we first establish a multidimensional evaluation framework to systematically benchmark the safety of current SOTA Medical MLLMs. Our empirical analysis reveals pervasive vulnerabilities across both general and medical-specific safety dimensions in existing models, p...

Citation screening in systematic review is time-consuming. Machine learning can help semi-automate it but faces obstacles. Each systematic review is a new dataset without initial annotations. Extreme class imbalance against irrelevant studies makes it difficult to select a good subset of samples to train a classifier. The rigid requirement of a (near) total recall of relevant studies demands a careful trade-off between accuracy and recall. This paper pilots a weak classifier ensemble approach to...

BackgroundDifferentiating parathyroid adenoma from hyperplasia is critical for surgical planning, but conventional imaging often cannot reliably distinguish these lesions. Ultrasound elastography offers quantitative assessment of tissue stiffness and may improve preoperative characterization. PurposeTo evaluate the diagnostic accuracy of ultrasound elastography in differentiating parathyroid adenoma from hyperplasia. MethodsA systematic review and meta-analysis was conducted in accordance with...

Ambient AI documentation tools generate draft notes that clinicians can review and edit before signing off in electronic health records. Scalable computational approaches to characterize how clinicians modify drafts remain limited, yet are essential for evaluating and improving AI effectiveness. We examined the feasibility of a few-shot prompted large language model (LLM) for categorizing sentence-level edits between AI drafts and final documentation. We developed five label-specific binary mode...

Background/AimsClinical trials and observational studies support the synthesis and development of clinical guidelines, highlighting the need for strong data quality assurance measures. The Acute to Chronic Pain Signatures (A2CPS) program is a large-scale, multi-site observational study investigating chronic post-surgical pain and opioid dependence. Its primary goal is to identify biomarkers predictive of progression from acute to chronic pain following knee arthroplasty or thoracic surgery. The ...

Over 54 million Americans are aged 65+, with depression affecting 25-49% and anxiety exceeding 30% of assisted living residents. AI systems employing agentic orchestration exhibit 0.5-2% failure rates--unacceptable where a single missed crisis can be fatal. We designed and bench-evaluated Lilo Engine, a 5-layer deterministic therapeutic pipeline replacing a prior multi-agent orchestrator. Safety is enforced through structural invariants: a Guardian layer with 4-gate OR crisis detection runs unco...

Acquiring insights from electronic health records (EHRs) is slowed by manual analytical workflows that limit scalability and reproducibility. We present LATCH (LLM-Assisted Testing of Clinical Hypotheses), an agentic framework that converts natural language clinical hypotheses into fully auditable analyses on structured EHR data. LATCH integrates LLM-assisted semantic layers with deterministic execution pipelines to automate cohort construction, statistical analysis, and result reporting, while ...

ObjectiveTo identify optimal modeling parameters for dynamically predicting hospital readmission risk using post-discharge step-count data from remote monitoring devices. MethodsWe combined data from two clinical studies that collected wearable or smartphone-based activity data for up to 6 months after hospital discharge. Analyses were limited to older adults ([≥]55 years). We constructed a patient-day dataset incorporating static demographic and clinical variables and dynamic activity featu...

Cross-jurisdictional pharmaceutical compliance requires comparative analysis of regulatory requirements across jurisdictions such as the US FDA and Chinas NMPA. Although large language models (LLMs) are increasingly explored for healthcare-related applications, their performance in cross-jurisdictional regulatory comparison has not been systematically characterized using dedicated benchmarks. This study introduces Sino-US-DrugQA, a bilingual benchmark dataset designed to evaluate LLM performance...

Long-term adherence to prescribed therapies remains a persistent challenge in chronic and ultra-rare conditions where clinical outcomes depend on continuous medication use. Even brief gaps in therapy can compromise disease control, yet patients frequently encounter structural barriers including high out-of-pocket costs, prior-authorization (PA) delays, annual re-verification cycles, and refill logistics that disrupt persistence. This study evaluates a patient-centric Markov-chain framework for a...

Autism spectrum disorder (ASD) affects a substantial proportion of children worldwide, yet clinical assessment of symptom severity remains resource-intensive and unevenly accessible. Artificial intelligence (AI) has transformative potential to support scalable and timely severity assessment from behavioral data, but existing approaches largely treat autism as a monolithic prediction target and rely on opaque models that are difficult for clinicians to interpret or trust. Moreover, prior multimod...

AO_SCPLOWBSTRACTC_SCPLOWLongitudinal healthcare surveys frequently contain inconsistencies in self-reported onset ages, where participants report different ages for the same condition between enrollment and follow-up surveys. We propose two methods to handle this challenge. First, we introduce a procedure that aggregates inconsistency patterns to construct participant-level reliability scores, enabling researchers to stratify participants and prioritize analysis on high-reliability cohorts. Seco...

Burn injuries are a significant concern in developing countries due to limited infrastructure, and treating them remains a major challenge. The manual assessment of burn severity is subjective and depends, to a large extent, on individual expertise. Artificial intelligence can automate this task with greater accuracy and improved predictions, which can assist healthcare professionals in making more informed decisions while triaging burn injuries. This study established a model pipeline for detec...

Electronic health records (EHRs) provide a large source of data that can be used for research purposes. Extraction of information from unstructured clinical notes in EHRs can be automated by large language models (LLMs). Although LLMs are promising for this task, challenges remain in reliable application of LLMs to EHR, including the lack of development and validation for languages other than English. Here, we identified Dutch LLMs and compared their performance in a case study. We selected the ...