Hospital-Based Medicine

Ethical risks in robot health education: A qualitative study.

BackgroundAs health education robots may potentially become a significant support force in nursing practice in the future, it is imperative to adhere to the European Union's concept of "Responsible Research and Innovation" (RRI) and deeply reflect on the ethical risks hidden in the process of intelligent robotic health education.AimThis study explores the perceptions of professional nursing profes...

Predicting outcomes after hospitalisation for COPD exacerbation using machine learning.

BACKGROUND: Early readmission and death are critical adverse outcomes following hospitalisation due to exacerbation of chronic obstructive pulmonary disease (ECOPD). This study aimed to develop and validate machine learning models to enhance the prediction of these outcomes after ECOPD hospitalisation.

Using an AI-Powered Solution to Transform Nursing Workflow and Improve Inpatient Care: A Retrospective Observational Study.

BACKGROUND: Nurses face an escalating workload, including tasks not directly related to patient care, such as responding to patients' requests for wat...

Attention-enabled Explainable AI for Bladder Cancer Recurrence Prediction

Non-muscle-invasive bladder cancer (NMIBC) is a relentless challenge in oncology, with recurrence rates soaring as high as 70-80%. Each recurrence t...

Hallucinations and Key Information Extraction in Medical Texts: A Comprehensive Assessment of Open-Source Large Language Models

Clinical summarization is crucial in healthcare as it distills complex medical data into digestible information, enhancing patient understanding and...

Assessing the Impact of External and Internal Factors on Emergency Department Overcrowding

Study Objective: To analyze the factors influencing Emergency Department (ED) overcrowding by examining the impacts of operational, environmental, a...

Machine Learning and Statistical Insights into Hospital Stay Durations: The Italian EHR Case

Length of hospital stay is a critical metric for assessing healthcare quality and optimizing hospital resource management. This study aims to identi...

Learning to fuse: dynamic integration of multi-source data for accurate battery lifespan prediction

Accurate prediction of lithium-ion battery lifespan is vital for ensuring operational reliability and reducing maintenance costs in applications lik...

Temporal Entailment Pretraining for Clinical Language Models over EHR Data

Clinical language models have achieved strong performance on downstream tasks by pretraining on domain specific corpora such as discharge summaries ...

Early Detection of Multidrug Resistance Using Multivariate Time Series Analysis and Interpretable Patient-Similarity Representations

Background and Objectives: Multidrug Resistance (MDR) is a critical global health issue, causing increased hospital stays, healthcare costs, and mor...

ExOSITO: Explainable Off-Policy Learning with Side Information for Intensive Care Unit Blood Test Orders

Ordering a minimal subset of lab tests for patients in the intensive care unit (ICU) can be challenging. Care teams must balance between ensuring th...

Optimizing Post-Cancer Treatment Prognosis: A Study of Machine Learning and Ensemble Techniques

The aim is to create a method for accurately estimating the duration of post-cancer treatment, particularly focused on chemotherapy, to optimize pat...

Cultivating Multidisciplinary Research and Education on GPU Infrastructure for Mid-South Institutions at the University of Memphis: Practice and Challenge

To support rapid scientific advancement and promote access to large-scale computing resources for under-resourced institutions at the Mid-South regi...

Cloud based DevOps Framework for Identifying Risk Factors of Hospital Utilization

A scalable and reliable system is required to analyze the National Health and Nutrition Examination Survey (NHANES) data efficiently to understand h...

Prognosis Of Lithium-Ion Battery Health with Hybrid EKF-CNN+LSTM Model Using Differential Capacity

Battery degradation is a major challenge in electric vehicles (EV) and energy storage systems (ESS). However, most degradation investigations focus ...

Selective Attention Federated Learning: Improving Privacy and Efficiency for Clinical Text Classification

Federated Learning (FL) faces major challenges regarding communication overhead and model privacy when training large language models (LLMs), especi...

Paging Dr. GPT: Extracting Information from Clinical Notes to Enhance Patient Predictions

There is a long history of building predictive models in healthcare using tabular data from electronic medical records. However, these models fail t...

Reconstructing Sepsis Trajectories from Clinical Case Reports using LLMs: the Textual Time Series Corpus for Sepsis

Clinical case reports and discharge summaries may be the most complete and accurate summarization of patient encounters, yet they are finalized, i.e...

Inferring Outcome Means of Exponential Family Distributions Estimated by Deep Neural Networks

While deep neural networks (DNNs) are widely used for prediction, inference on DNN-estimated subject-specific means for categorical or exponential f...

Can Reasoning LLMs Enhance Clinical Document Classification?

Clinical document classification is essential for converting unstructured medical texts into standardised ICD-10 diagnoses, yet it faces challenges ...