Hospital-Based Medicine

Development and validation of a machine learning-based nomogram for predicting prognosis in lung cancer patients with malignant pleural effusion.

Malignant pleural effusion (MPE) is a common complication in patients with advanced lung cancer, significantly impacting their survival rates and quality of life. Effective tools for assessing the prognosis of these patients are urgently needed to enable early intervention. This study retrospectively analyzed patient data from the Affiliated Hospital of North Sichuan Medical College from 2013 to 2...

Innovative Technologies for Smarter and Efficient Operating Room Scheduling.

An optimized scheduling system for surgical procedures is considered fundamental for maximizing hospital resource utilization and improving patient outcomes. The integration of Artificial Intelligence (AI) tools and New Technologies is paramount in this project to enable personalized patient care and optimize perioperative clinical pathways. We read with interest the manuscript by Parks et al., wh...

Can Artificial Intelligence Software be Utilised for Thyroid Multi-Disciplinary Team Outcomes?

OBJECTIVES: ChatGPT is one of the most publicly available artificial intelligence (AI) softwares. Ear, nose and throat (ENT) services are often stretc...

Using Machine Learning to Identify Social Determinants of Health that Impact Discharge Disposition for Hospitalized Patients.

OBJECTIVE: To identify self-reported social determinants of health (SDOH) among hospitalized patients that predict discharge to a skilled nursing faci...

Multidisciplinary characterization of embarrassment through behavioral and acoustic modeling.

Embarrassment is a social emotion that shares many characteristics with social anxiety (SA). Most people experience embarrassment in their daily lives...

Multimodal treatment of colorectal liver metastases: Where are we? Current strategies and future perspectives.

Despite the continued high prevalence of colorectal cancer in the Western world, recent years have witnessed a decline in its mortality rate, largely ...

Battery state of health estimation under fast charging via deep transfer learning.

Accurate state of health (SOH) estimation is essential for effective lithium-ion battery management, particularly under fast-charging conditions with ...

Construction of a machine learning-based interpretable prediction model for acute kidney injury in hospitalized patients.

In this observational study, we used data from 59,936 hospitalized adults to construct a model. For the models constructed with all 53 variables, all ...

A hybrid vine copula-fuzzy model for groundwater level simulation under uncertainty.

Accurate simulation of groundwater level is crucial for the sustainable management of water resources. However, the numerous uncertainties in input da...

State-of-the-art for automated machine learning predicts outcomes in poor-grade aneurysmal subarachnoid hemorrhage using routinely measured laboratory & radiological parameters: coagulation parameters and liver function as key prognosticators.

The objective of this study was to develop and evaluate automated machine learning (aML) models for predicting short-term (1-month) and medium-term (3...

Development of an artificial intelligence-generated, explainable treatment recommendation system for urothelial carcinoma and renal cell carcinoma to support multidisciplinary cancer conferences.

BACKGROUND: Decisions on the best available treatment in clinical oncology are based on expert opinions in multidisciplinary cancer conferences (MCC)....

Harnessing Electronic Health Records and Artificial Intelligence for Enhanced Cardiovascular Risk Prediction: A Comprehensive Review.

Electronic health records (EHR) have revolutionized cardiovascular disease (CVD) research by enabling comprehensive, large-scale, and dynamic data col...

Development and validation of machine learning-based prediction model for outcome of cardiac arrest in intensive care units.

Cardiac arrest (CA) poses a significant global health challenge and often results in poor prognosis. We developed an interpretable and applicable mach...

Comparing neural language models for medical concept representation and patient trajectory prediction.

Effective representation of medical concepts is crucial for secondary analyses of electronic health records. Neural language models have shown promise...

Validation of patient-specific deep learning markerless lung tumor tracking aided by 4DCBCT.

. Tracking tumors with multi-leaf collimators and x-ray imaging can be a cost-effective motion management method to reduce internal target volume marg...

Intelligent risk stratification of hypertension based on ambulatory blood pressure monitoring and machine learning algorithms.

. Risk stratification of hypertension plays a crucial role in the treatment decisions and medication guidance during clinical practices. Although frui...

Dynamic HRV Monitoring and Machine Learning Predict NYHA Improvements in Acute Heart Failure Patients.

Heart failure (HF) is marked by significant morbidity, mortality, and readmission rates, highlighting a critical need for accurate assessment of treat...

Using Natural Language Processing in the LACE Index Scoring Tool to Predict Unplanned Trauma and Surgical Readmissions in South Africa.

BACKGROUND: Unplanned and potentially avoidable readmission within 30 days post discharge is a major financial burden.

Harnessing Artificial Intelligence for Precision Diagnosis and Treatment of Triple Negative Breast Cancer.

Triple-Negative Breast Cancer (TNBC) is a highly aggressive subtype of breast cancer (BC) characterized by the absence of estrogen, progesterone, and ...

Leveraging Artificial Intelligence to Reduce Neuroscience ICU Length of Stay.

GOAL: Efficient patient flow is critical at Tampa General Hospital (TGH), a large academic tertiary care center and safety net hospital with more than...