Hospital-Based Medicine

Bridging Electronic Health Records and Clinical Texts: Contrastive Learning for Enhanced Clinical Tasks

Conventional machine learning models, particularly tree-based approaches, have demonstrated promising performance across various clinical prediction tasks using electronic health record (EHR) data. Despite their strengths, these models struggle with tasks that require deeper contextual understanding, such as predicting 30-day hospital readmission. This can be primarily due to the limited semanti...

Machine learning analysis of factors contributing to hypotension after lumbosacral epidural anaesthesia in dogs undergoing abdominal surgery.

The incidence of hypotension after a lumbosacral epidural in dogs depends on the volume of local anaesthetic administered. So far, there are no reports comparing both methods used to calculate this volume-body weight (BW) and occipito-coccygeal length (OCL)-in veterinary medicine. In this study, we evaluated the effect of these two common dosing strategies on risk of intraoperative hypotension in ...

Continually Self-Improving Language Models for Bariatric Surgery Question--Answering

While bariatric and metabolic surgery (MBS) is considered the gold standard treatment for severe and morbid obesity, its therapeutic efficacy hinges...

From Admission to Discharge: Leveraging NLP for Upstream Primary Coding with SNOMED CT.

This study aims to describe implementing a SNOMED CT-coded health problem (HP) list at Hospital Clínic de Barcelona. The project focuses on enhancing ...

Self-powered smart contact lenses: a multidisciplinary approach to micro-scale energy and 900 MHz - 1.1 GHz bandwidth microfabricated loop antennas communication systems

Smart contact lenses are at the forefront of integrating microelectronics, biomedical engineering, and optics into wearable technologies. This work ...

Characterization of subepithelial tumors of upper gastrointestinal tract by endoscopic ultrasound.

In this article we comment on the paper by Xu describing retrospective data on endoscopic treatment outcome of esophageal gastrointestinal stromal tu...

Practical, epistemic and normative implications of algorithmic bias in healthcare artificial intelligence: a qualitative study of multidisciplinary expert perspectives.

BACKGROUND: There is a growing concern about artificial intelligence (AI) applications in healthcare that can disadvantage already under-represented a...

Forecasting Surgical Bed Utilization: Architectural Design of a Machine Learning Pipeline Incorporating Predicted Length of Stay and Surgical Volume.

The objective of this study was to develop a machine learning model utilizing data from the electronic health record (EHR) to model length of stay and...

Exploring Moral Exercises for Human Oversight of AI systems: Insights from Three Pilot Studies

This paper elaborates on the concept of moral exercises as a means to help AI actors cultivate virtues that enable effective human oversight of AI s...

Deep Learning-Based Forecasting of Boarding Patient Counts to Address ED Overcrowding

This study develops deep learning models to forecast the number of patients in the emergency department (ED) boarding phase six hours in advance, ai...

Evaluating Prevalence of Preterm Postnatal Growth Faltering Using Fenton 2013 and INTERGROWTH-21st Growth Charts with Logistic and Machine Learning Models.

Postnatal growth faltering (PGF) significantly affects premature neonates, leading to compromised neurodevelopment and an increased risk of long-term...

Assessing artificial intelligence ability in predicting hospitalization duration for pleural empyema patients managed with uniportal video-assisted thoracoscopic surgery: a retrospective observational study.

BACKGROUND: This retrospective observational research evaluates the potential applicability of artificial intelligence models to predict the length of...

Early Prediction of In-Hospital ICU Mortality Using Innovative First-Day Data: A Review

The intensive care unit (ICU) manages critically ill patients, many of whom face a high risk of mortality. Early and accurate prediction of in-hospi...

Lexical associations can characterize clinical documentation trends related to palliative care and metastatic cancer.

Palliative care is known to improve quality of life in advanced cancer. Natural language processing offers insights to how documentation around pallia...

Predicting hospital outpatient volume using XGBoost: a machine learning approach.

Hospital outpatient volume is influenced by a variety of factors, including environmental conditions and healthcare resource availability. Accurate pr...

Exploring the Role of Digital Twins in Heart Care: Research Directions and Applications.

The concept of digital twins has emerged as a transformative innovation in healthcare. Digital twins are virtual replicas of physical entities that ca...

Machine Learning Models Predicting Hospital Admissions During Chemotherapy Utilising Longitudinal Symptom Severity Reports and Patient-Reported Outcome Measures.

Chemotherapy toxicity can lead to acute hospital admissions, negatively impacting the healthcare system and patients' well-being. Machine learning (ML...

Predicting 1-Year Survival Using Machine Learning in Very Old Patients Before ICU Admission.

Discussions about the benefits of admitting very old individuals to intensive care unit (ICU) remain challenging. We hypothesized that data-driven alg...

Participatory Design of an AI-Based CDSS for Delirium Prevention.

Delirium is a frequent and severe complication in inpatient care, leading to increased mortality and cognitive impairment. The KIDELIR project aims to...

Investigating the Genetic Links Between Immune Cell Profiles and Bladder Cancer: A Multidisciplinary Bioinformatics Approach.

: Bladder cancer (BC) is a common malignancy in the urinary system, with an increasing incidence rate. Immune cell infiltration within the tumor micro...