Hospital-Based Medicine

Bridging the gap between models and reality: development of a research environment for an object-oriented hospital information system to integrate artificial intelligence and robotics into clinical practice.

INTRODUCTION: Hospital information systems (HISs) are the main access opportunity for medical professionals to computer-based patient administration. However, current HISs are primarily designed to function as office applications rather than as comprehensive management and supporting tools. Due to their inflexible architecture, integrating modern technologies like artificial intelligence (AI) mode...

Predicting Unplanned Intubations in Rib Fracture Patients: An Interpretable Machine Learning Approach.

BackgroundTraumatic rib fractures can lead to respiratory complications necessitating unplanned intubation, but predictors have been inadequately delineated. We used interpretable machine learning to predict unplanned intubations in rib fracture patients while identifying predictors.MethodsTQIP 2017-2022 was queried for adult patients admitted to the hospital following a rib fracture injury. An XG...

Artificial Intelligence in Diagnosis and Prognosis of Cognitive Impairment in Parkinson's Disease.

Parkinson's disease (PD), a progressive neurodegenerative disorder, affects millions globally, with cognitive impairment as a significant non-motor co...

Artificial Intelligence-Assisted Conductive Hydrogel Dressings for Refractory Wounds Monitoring.

Refractory wounds cause significant harm to the health of patients and the most common treatments in clinical practice are surgical debridement and wo...

Machine learning for the prediction of urosepsis using electronic health record data.

Urosepsis, a medical condition resulting from the progression of urinary tract infection (UTI), is a leading cause of death in the US. Urosepsis occur...

Comparative analysis of artificial intelligence tools for the dissemination of colorectal cancer screening guidelines: a novel perspective on early screening education.

This study systematically evaluated the effectiveness of three artificial intelligence (AI) tools-ChatGPT-4o, Claude3.5, and DeepSeek-in disseminating...

Clinical decision support using pseudo-notes from multiple streams of EHR data.

Electronic health records (EHR) contain data from disparate sources, spanning various biological and temporal scales. In this work, we introduce the M...

Development and validation of a transformer model-based early warning score for real-time prediction of adverse outcomes in the emergency department.

This study aimed to develop and validate a transformer-based early warning score (TEWS) system for predicting adverse events (AEs) in the emergency de...

Soft Robotics for Parkinson's Disease Supported by Functional Materials and Artificial Intelligence.

Progressive neurodegenerative disease known as Parkinson's disease (PD) is characterized by both motor and nonmotor symptoms that severely reduce the ...

Artificial intelligence-driven streamlining of prostate cancer multidisciplinary team recommendations in a tertiary NHS centre in the UK.

OBJECTIVES: To evaluate the effectiveness of a rules-based artificial intelligence (AI) clinical decision support system (CDSS) called the PROState AI...

Patient radiation safety in the intensive care unit.

The aim of this commentary review was to summarize the main research evidences on radiation exposure and to underline the best clinical and radiologic...

Predictive model of ulcerative colitis syndrome with ensemble learning and interpretability methods.

In recent years, the prevalence of chronic diseases such as Ulcerative Colitis (UC) has increased, bringing a heavy burden to healthcare systems. Trad...

Identifying real time surveillance indicators to estimate COVID-19 hospital admissions in Colorado during and after the public health emergency.

Questions remain about how best to focus surveillance efforts for COVID-19 and other emerging respiratory diseases. We used an archive of COVID-19 dat...

Machine learning based prediction modeling of micro-EDM of Ti-29Nb-13Ta-4.6Zr (TNTZ).

Although Ti-6Al-4V stands out as one of the best in biomedical, automotive, and aerospace applications due to its low density and higher corrosion res...

Modeling the prediction of spontaneous rupture and bleeding in hepatocellular carcinoma via machine learning algorithms.

This study aimed to identify the risk factors associated with spontaneous rupture and bleeding in hepatocellular carcinoma, establish a prediction mod...

Developed a knowledge base of risk factors for postoperative cognitive dysfunction: a retrospective database study.

Postoperative cognitive dysfunction (POCD), a heterogeneous spectrum of surgery/anesthesia-associated neurocognitive impairments, represents a critica...

Machine learning to improve predictive performance of prehospital early warning scores.

Early warning scores are used to assess acute patients' risk of being in a critical situation, allowing for early appropriate treatment, avoiding crit...

Development of a machine learning model to identify the predictors of the neonatal intensive care unit admission.

Scientists aim to create a system that can predict the likelihood of newborns being admitted to the neonatal intensive care unit (NICU) by combining v...

Profiling short-term longitudinal severity progression and associated genes in COVID-19 patients using EHR and single-cell analysis.

Here we propose CovSF, a deep learning model designed to track and forecast short-term severity progression of COVID-19 patients using longitudinal cl...

An interval type-2 fuzzy multi-criteria decision-making approach for patient bed allocation.

Effective inpatient bed management is critical for optimizing healthcare resource utilization and ensuring high-quality patient care. The growing imba...