Hospital-Based Medicine

Human-Machine Interfaces for Subsea Telerobotics: From Soda-straw to Natural Language Interactions

This review explores the evolution of human-machine interfaces (HMIs) for subsea telerobotics, tracing back the transition from traditional first-person "soda-straw" consoles (narrow field-of-view camera feed) to advanced interfaces powered by gesture recognition, virtual reality, and natural language models. First, we discuss various forms of subsea telerobotics applications, current state-of-t...

Trading off performance and human oversight in algorithmic policy: evidence from Danish college admissions

Student dropout is a significant concern for educational institutions due to its social and economic impact, driving the need for risk prediction systems to identify at-risk students before enrollment. We explore the accuracy of such systems in the context of higher education by predicting degree completion before admission, with potential applications for prioritizing admissions decisions. Usin...

Integrating Clinical Data and Patient-Reported Outcomes for Analyzing Gender Differences and Progression in Multiple Sclerosis Using Machine Learning.

Multiple sclerosis (MS) is a complex neurodegenerative disease with a variable prognosis that complicates effective management and treatment. This stu...

BianCang: A Traditional Chinese Medicine Large Language Model

The rise of large language models (LLMs) has driven significant progress in medical applications, including traditional Chinese medicine (TCM). Howe...

Emotional Images: Assessing Emotions in Images and Potential Biases in Generative Models

This paper examines potential biases and inconsistencies in emotional evocation of images produced by generative artificial intelligence (AI) models...

Impact of wearable device data and multi-scale entropy analysis on improving hospital readmission prediction.

OBJECTIVE: Unplanned readmissions following a hospitalization remain common despite significant efforts to curtail these. Wearable devices may offer h...

Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models?

OBJECTIVE: To externally validate by revision and update the study on the efficacy of nosocomial infection control (SENIC) model of surgical site infe...

Machine learning-driven in-hospital mortality prediction in HIV/AIDS patients with infection: a single-centred retrospective study.

() is a widely disseminated betaherpesvirus that typically induces latant infections. In immunocompromised populations, especially transplant and HI...

Unlocking the Full Potential of High-Density Surface EMG: Novel Non-Invasive High-Yield Motor Unit Decomposition

The decomposition of high-density surface electromyography (HD-sEMG) signals into motor unit discharge patterns has become a powerful tool for inves...

Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval

Large language models (LLMs) have demonstrated significant potential in clinical decision support. Yet LLMs still suffer from hallucinations and lac...

INSIGHTBUDDY-AI: Medication Extraction and Entity Linking using Large Language Models and Ensemble Learning

Medication Extraction and Mining play an important role in healthcare NLP research due to its practical applications in hospital settings, such as t...

Retrospective Comparative Analysis of Prostate Cancer In-Basket Messages: Responses from Closed-Domain LLM vs. Clinical Teams

In-basket message interactions play a crucial role in physician-patient communication, occurring during all phases (pre-, during, and post) of a pat...

Predicting the Stay Length of Patients in Hospitals using Convolutional Gated Recurrent Deep Learning Model

Predicting hospital length of stay (LoS) stands as a critical factor in shaping public health strategies. This data serves as a cornerstone for gove...

Performance and Metacognition Disconnect when Reasoning in Human-AI Interaction

Optimizing human-AI interaction requires users to reflect on their own performance critically. Our paper examines whether people using AI to complet...

Automated detection of underdiagnosed medical conditions via opportunistic imaging

Abdominal computed tomography (CT) scans are frequently performed in clinical settings. Opportunistic CT involves repurposing routine CT images to e...

Pennsieve: A Collaborative Platform for Translational Neuroscience and Beyond

The exponential growth of neuroscientific data necessitates platforms that facilitate data management and multidisciplinary collaboration. In this p...

Human-Centered AI Applications for Canada's Immigration Settlement Sector

While AI has been frequently applied in the context of immigration, most of these applications focus on selection and screening, which primarily ser...

Predicting Patient No-Shows: Situated Machine Learning with Imperfect Data.

Patients who do not show up for scheduled appointments are a considerable cost and concern in healthcare. In this study, we predict patient no-shows f...

Evaluating the Predictive Features of Person-Centric Knowledge Graph Embeddings: Unfolding Ablation Studies.

Developing novel predictive models with complex biomedical information is challenging due to various idiosyncrasies related to heterogeneity, standard...

Automation of Trainable Datasets Generation for Medical-Specific Language Model: Using MIMIC-IV Discharge Notes.

This study introduces a novel approach for generating machine-generated instruction datasets for fine-tuning medical-specialized language models using...