Medicare

Introduction to secure data sharing in primary care using the federated causal learning models.

OBJECTIVES: In primary healthcare research, there are core challenges such as data silos and missing data. Furthermore, the current high technical barriers severely limit effective cross-regional data analysis. METHODS: This work was the first to apply the federated causal learning framework to primary healthcare. Through two case studies, we demonstrated how to estimate cross-regional causal effe...

Uncertainty aware and explainable construction cost prediction using a hybrid probabilistic learning model.

This paper presents a unified probabilistic framework for construction cost forecasting, NGBoost-ETR (Natural Gradient Boosting with Extra Trees base learners) that delivers predictive accuracy, calibrated uncertainty, and SHAP-based interpretability. Rather than positioning novelty in algorithmic integration alone, the contribution lies in a systems-level design that jointly addresses three criti...

Identifying systemic lupus erythematosus from serum proteomic profiles using machine learning and genetic risk stratification.

OBJECTIVES: Proteome-wide risk models for lupus remain underexplored. We developed classification models to identify lupus from serum proteomic profil...

Large Language Model-Powered Diagnostic Co-Pilot ("CapyEngine") for Mental Disorders: Development, Evaluation, and Future Optimization Study.

BACKGROUND: Despite the growing potential of large language models (LLMs) in mental health services, evidence on its capabilities in diagnostic proces...

Differential Factors Associated With the Presence of Persistent Symptoms in Individuals Diagnosed With Long COVID: Protocol for a Longitudinal Matched Case-Control Study.

BACKGROUND: Long COVID (postacute sequelae of SARS-CoV-2 infection) is a heterogeneous condition with persistent multisystem symptoms and substantial ...

Integration of alternative fragmentation techniques into standard LC-MS workflows using a single deep learning model enhances proteome coverage.

Bottom-up proteomics relies predominantly on collision-induced dissociation (CID) for peptide sequencing, which has achieved remarkable sensitivity an...

From Guidelines to Clicklists: GPT-5-Generated ERAS Checklists Improve Guideline Coverage for Bariatric and Gastrointestinal Cancer Surgery-A STROBE-Compatible Cross-Sectional Evaluation.

BACKGROUND: Enhanced Recovery After Surgery (ERAS) pathways improve outcomes after bariatric and gastrointestinal (GI) cancer surgery, yet real-world ...

Artificial Intelligence as a Catalyst for Value-Based Health Insurance in the United States: Narrative Review and Policy Perspective.

The United States health insurance system is at a critical crossroads. Inflating costs, fragmented care, and administrative inefficiencies have reveal...

Comparison of Written and Visual Patient Education Methods Before Urodynamic Studies: A Randomized Controlled Trial.

PURPOSE: This study compared the effects of written versus visual patient education methods on patient anxiety, procedural comprehension, hemodynamic ...

Predicting progression of Mycobacterium avium complex pulmonary disease with treatment using explainable artificial intelligence techniques.

OBJECTIVES: The incidence of nontuberculous mycobacterial pulmonary disease (NTM-PD) is rising; however, only certain patients experience disease prog...

Managing maternity: Moving care, not patients, using artificial intelligence (AI), internet-of-things (IOT) and point-of-care testing (POCT) devices.

The integration of artificial intelligence (AI) into healthcare is accelerating and maternity care is at a pivotal moment for the strategic implementa...

Automated daily adaptation for breast cancer radiotherapy: benefits of deep learning-driven dose prediction workflow for CBCT-based adaptation.

Objective Daily anatomical variations can jeopardize the quality of modern radiotherapy (RT). Adaptive radiotherapy (ART) aims to address this by ...

Evidence-based AI clinical decision support system for acute burn care and complex reconstruction.

BACKGROUND: Burn surgery requires rapid, evidence-based decision-making across acute resuscitation, operative management, and reconstruction. Despite ...

Recurrent spiking neural networks with bimodal neuronal time scales for learning performance enhancement.

Recurrent Spiking Neural Networks (RSNNs) represent a crucial paradigm in neuromorphic computing, with their performance heavily dependent on the desi...

Design of an optimal planning framework for cryosurgical treatment of brain tumor using CNN segmentation of MRI images.

This study presents an integrated methodology for pre-operative cryosurgical planning of irregularly shaped brain tumors using two-dimensional MRI dat...

Deep learning-based 4D robust optimization of intensity-modulated proton therapy for lung cancer radiotherapy.

Intensity-modulated proton therapy (IMPT) provides steep dose gradients but is vulnerable to range uncertainties and respiratory motion, leading to in...

Non-destructive monitoring of root biomass in hydroponically grown leafy vegetables: comparison between machine learning-based RGB and hyperspectral imaging.

BACKGROUND: Root biomass serves as a critical indicator of plant eco-physiological status and crop productivity, yet its non-destructive monitoring re...

Prescription‑dose stratification improves deep learning‑based VMAT dose prediction in locally advanced NSCLC.

Volumetric-modulated arc therapy (VMAT) planning for locally advanced non-small cell lung cancer (NSCLC) is an iterative and planner-dependent process...

[Standardizing breast cancer digital pathology databases for artificial intelligence: practice and reflection].

The traditional paradigm of pathology diagnosis faces challenges like data fragmentation and inefficiency in the era of big data and artificial intell...

DiNovo enables high-coverage and high-confidence de novo peptide sequencing via mirror proteases and deep learning.

Despite the recent advancements driven by deep learning, de novo peptide sequencing is still constrained by incomplete peptide fragmentation and insuf...