Hospital-Based Medicine

Predicting adverse events for risk stratification of chemotherapy based stem cell mobilization in multiple myeloma.

Autologous stem-cell transplantation is a fundamental therapy for multiple myeloma. Although inpatient chemo-based stem-cell mobilization (SCM) is standard care in Germany, outpatient approaches could ease healthcare constraints. We analyzed 109 myeloma patients undergoing SCM and collection at the University Medical Center Göttingen for safety. We then trained machine learning models to predict a...

Predicting non-emergency healthcare use in Australia using machine learning on longitudinal household data.

The persistent increase in healthcare expenditure has become a major challenge for the sustainability of public financing worldwide. Therefore, identifying the characteristics of at-risk population and their predictability for healthcare use is crucial to inform targeted policy and interventions to curb with increasing healthcare use and expenditure. Drawing on three waves of the HILDA survey that...

Global priorities for advancing cancer survivorship.

PURPOSE OF REVIEW: Cancer survivorship is increasingly recognized as an important component of cancer care, yet access to high-quality care remains in...

Forecasting Local Surges in COVID-19 Hospitalizations through Adaptive Decision Tree Classifiers.

IntroductionDuring the COVID-19 pandemic, many communities across the United States experienced surges in hospitalizations, which strained the local h...

Evaluation of Large Language Models for Radiologists' Support in Multidisciplinary Breast Cancer Teams: Comparative Study.

BACKGROUND: Artificial intelligence tools, particularly large language models (LLMs), have shown considerable potential across various domains. Howeve...

Protocol for the PROSECCA study: a new approach for predicting radiotherapy outcome using artificial intelligence and electronic population-based healthcare data.

INTRODUCTION: Within the UK there are 33 deaths every day from prostate cancer, second only to lung cancer as the most common cause of cancer death in...

Municipal solid waste management forecasting using neural networks at discharge point scale.

Urbanisation and population growth continue to accelerate waste generation, posing serious environmental and logistical challenges for the management ...

Harnessing artificial intelligence for cardio-oncology:Towards a new future of cardiovascular care for the cancer patient.

Artificial intelligence (AI) offers new opportunities in cardio-oncology for early detection, risk stratification, and personalized management of card...

Machine Learning-based Prediction of Mortality Among Malnourished Patients Hospitalized With Inflammatory Bowel Disease.

BACKGROUND: Malnourished patients hospitalized with inflammatory bowel disease (IBD) have a high risk of morbidity and mortality. Risk stratification ...

A novel, standardised approach to balancing effectiveness, efficiency and utility of surveillance AI prediction models for hospitalised patients using sepsis prediction as an exemplar.

OBJECTIVE: To introduce a novel, standardised approach to evaluating AI prediction models in balancing effectiveness, efficiency and utility, using a ...

Listening to the note: clinician perspectives on ambient artificial intelligence scribes in medical documentation.

OBJECTIVES: To qualitatively characterize barriers and facilitators to implementing and using an ambient scribe across a large academic medical center...

Artificial Intelligence-derived Measurements of Myosteatosis from Coronary Artery Calcium CT Scans to Predict COPD: The Multi-Ethnic Study of Atherosclerosis.

Purpose To evaluate the predictive value of myosteatosis as an opportunistic finding in coronary artery calcium (CAC) CT scans for clinically diagnose...

Machine learning for optimizing mAs in KUB radiography with metal implants.

BACKGROUND AND PURPOSE: Kidney-ureter-bladder (KUB) radiography is a common examination that exposes patients to a higher radiation dose and increased...

Multisite derivation of a machine learning algorithm using high sensitivity troponin to predict major adverse cardiac events in the emergency department.

OBJECTIVE: To develop a machine learning (ML) algorithm to stratify risk for major adverse cardiac events (MACE) within 30 days in emergency departmen...

Evaluation of large language models as decision support tools for head and neck cancer management: A blinded multidisciplinary simulation study.

BACKGROUND: The management of head and neck cancer relies on multidisciplinary expertise; however, access to tumor boards remains variable. Large lang...

Explainable machine learning model for predicting traumatic brain injury-induced coagulopathy in elderly patients: A multicenter cohort study.

BACKGROUND: Traumatic brain injury-induced coagulopathy (TBI-IC) in the elderly is a severe complication of traumatic brain injury (TBI) that leads to...

Investigation of Bias Using ChatGPT-4 in Pain Management and Patient Profiling.

BACKGROUND: Large language models, such as ChatGPT (cGPT), are being integrated increasingly into clinical workflows and medical education. However, c...

Descriptive content analysis assessment of ChatGPT responses to substance use disorder treatment questions compared to National health guidelines.

BACKGROUND: Artificial intelligence (AI)-powered large language models like ChatGPT are increasingly used by the public to access health information. ...

Comparison of the predictive performance of machine learning and conventional logistic regression models for poor discharge outcomes in patients with Aneurysmal subarachnoid hemorrhage: A retrospective cohort study.

How ready are we to use artificial intelligence in our fight against Antimicrobial resistance? An ESGAID and EAAS perspective.

INTRODUCTION: Antimicrobial resistance (AMR) remains one of the greatest threats to global health, requiring innovative approaches to antibiotic disco...