Information Technology

Anomaly detection of cybersecurity behavior using cross-sequence aligned transformer-A dynamic recognition approach for high-frequency interaction patterns.

In high-frequency interaction network environments, network traffic features and user behavior sequences often exhibit pronounced temporal asynchrony and information redundancy, which can substantially weaken the capability of anomaly detection models to identify dynamic attack patterns. Based on this observation, this study proposes and empirically validates a core hypothesis: explicitly modeling...

Differentiation between psychotic and non-psychotic major depression by the tabular prior-data fitted network.

BACKGROUND: Misdiagnosing psychotic major depression (PMD) as non-psychotic major depression (NPMD) can lead to poor treatment outcomes. This study aims to develop and validate a machine learning-based model using electronic medical record (EMR) data and the Tabular Prior-data Fitted Network (TabPFN) model to distinguish between PMD and NPMD. METHODS: A total of 666 patients with PMD and 808 patie...

Utilization and prospects of artificial intelligence in the diagnosis, prediction, and treatment of erectile dysfunction.

INTRODUCTION: Erectile dysfunction (ED) represents a significant global public health challenge in men's health, with its prevalence exacerbated by po...

Improving Fairness and Mitigating Bias in Multicenter Electronic Health Records Models to Predict Glaucoma Outcomes.

PURPOSE: To evaluate the effectiveness and generalizability of bias mitigation methods in glaucoma progression prediction models across a multicenter ...

Primary Care Physician Perspectives on Artificial Intelligence for Dementia Screening: A Qualitative Study.

BACKGROUND: Automated approaches to cognitive impairment screening may soon achieve sufficient levels of accuracy for clinical implementation but they...

Identification of clinical phenotypes and disease trajectories in SLE using AI through a natural language processing framework.

OBJECTIVES: Electronic Health Records (EHRs) contain a wealth of unstructured patient data that can be leveraged using Artificial Intelligence (AI). T...

Direct Integration of Ionic Liquid Gel Sensors onto Microfibrous Face Mask Substrates for Wearable Respiratory Health Monitoring.

Mask-integrated respiratory sensors are promising for noninvasive monitoring of respiratory health outside of clinical settings, for example, to suppo...

Secure electronic health record access control via blockchain, dual-attribute encryption, and large language model-based attribute extraction.

Access control and data privacy are two of the main necessities in managing electronic health records (EHRs) across distributed domain. There are priv...

Predictive Modeling of Childhood Disease Severity at a Telemedicine Service: Evidence from the Improving Nighttime Access to Care and Treatment Studies in Haiti.

OBJECTIVE: To develop models within pediatric telemedicine that identify potentially "sick" cases for additional safety checks and integrate those mod...

Machine learning and artificial intelligence for delirium prediction with Electronic Health Records (EHR): a scoping review.

Artificial Intelligence-Driven Three-Dimensional Reconstruction in Lung Cancer Surgery: Current Status and Future Perspectives.

BACKGROUND: Three-dimensional (3D) reconstruction is an important adjunct in lung cancer surgery for visualizing pulmonary anatomy, supporting preoper...

Multimodal SERS Biosensing Platforms: Emerging Opportunities for Ultrasensitive Biomarker Detection and Intelligent Diagnostics.

Surface-enhanced Raman scattering (SERS) has emerged as a powerful analytical technique for biosensing owing to its ultrahigh sensitivity and molecula...

Small-bowel capsule endoscopy - from clinic to couch: time to go green?

PURPOSE OF REVIEW: Small-bowel capsule endoscopy (SBCE) has transformed small-intestine diagnostics by enabling direct, noninvasive mucosal visualizat...

Pioneering efficient deep learning architectures for enhanced hepatocellular carcinoma prediction and clinical translation.

BACKGROUND: Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality worldwide and is often diagnosed at advanced stages, reducing ...

Artificial intelligence in combating challenges in antimicrobial resistance: a narrative review.

Antimicrobial resistance (AMR) is a major global health challenge that threatens the effective prevention and treatment of infections. It arises from ...

Interpretable four-factor day-1 nomogram for predicting sepsis-associated encephalopathy in septic ICU patients with AKI: Development and internal validation in MIMIC-IV.

Sepsis-associated encephalopathy (SAE) is common in the intensive care unit (ICU) and portends worse short- and long-term outcomes. To enable real-tim...

Overview of a User-Centered, Mixed-Methods Process for Designing Interconnected and Focused Mobile Applications on Patient Care Environment (InterFACE): Augmented-Reality Decision Support System for Pediatric Resuscitation.

BACKGROUND: Pediatric cardiopulmonary resuscitation (CPR) is a highly complex and time-critical process that demands precise team coordination and str...

An update on the developments and challenges with the diagnosis and classification of autoimmune optic neuritis.

INTRODUCTION: Autoimmune optic neuritis (ON) is a heterogeneous spectrum that includes multiple sclerosis (MS), neuromyelitis optica spectrum disorder...

Predicting the need for electroconvulsive therapy via machine learning trained on electronic health record data.

OBJECTIVES: Electroconvulsive therapy (ECT) is an effective treatment of severe manifestations of mental illness. Since delay in initiation of ECT can...

Identifying the 'gray zone': Developing scalable methods to detect opioid misuse in veterans on long-term opioid therapy for pain.

BACKGROUND: Patients prescribed opioids who are at high risk for misuse but don't meet diagnostic criteria for opioid use disorder (OUD) fall into a c...