Hospital-Based Medicine

Inpatient stroke rehabilitation: prediction of clinical outcomes using a machine-learning approach.

BACKGROUND: In clinical practice, therapists often rely on clinical outcome measures to quantify a patient's impairment and function. Predicting a patient's discharge outcome using baseline clinical information may help clinicians design more targeted treatment strategies and better anticipate the patient's assistive needs and discharge care plan. The objective of this study was to develop predict...

The value of artificial neural networks for predicting length of stay, discharge disposition, and inpatient costs after anatomic and reverse shoulder arthroplasty.

HYPOTHESIS/PURPOSE: The objective is to develop and validate an artificial intelligence model, specifically an artificial neural network (ANN), to predict length of stay (LOS), discharge disposition, and inpatient charges for primary anatomic total (aTSA), reverse total (rTSA), and hemi- (HSA) shoulder arthroplasty to establish internal validity in predicting patient-specific value metrics.

Deep Learning for Improved Risk Prediction in Surgical Outcomes.

The Norwood surgical procedure restores functional systemic circulation in neonatal patients with single ventricle congenital heart defects, but this ...

Using machine learning to predict early readmission following esophagectomy.

OBJECTIVE: To establish a machine learning (ML)-based prediction model for readmission within 30 days (early readmission or early readmission) of pati...

Deep learning for predicting the occurrence of cardiopulmonary diseases in Nanjing, China.

The efficiency of disease prevention and medical care service necessitated the prediction of incidence. However, predictive accuracy and power were la...

Experimental Data Based Machine Learning Classification Models with Predictive Ability to Select in Vitro Active Antiviral and Non-Toxic Essential Oils.

In the last decade essential oils have attracted scientists with a constant increase rate of more than 7% as witnessed by almost 5000 articles. Among ...

Supervised mixture of experts models for population health.

We propose a machine learning driven approach to derive insights from observational healthcare data to improve public health outcomes. Our goal is to ...

Is P&T Ready to Add Rapid Cycle Analytics to Formulary?

The intent of this article is to evaluate a novel approach, using rapid cycle analytics and real world evidence, to optimize and improve the medicati...

Correlation of Measured and Estimated Creatinine Clearance in Hospitalized Elderly Patients: A Retrospective Cohort Study.

Accurate assessment of renal function is essential in hospitalized elderly patients. Few studies have examined the accuracy of Cockcroft-Gault (C-G) ...

Predicting hospital admission for older emergency department patients: Insights from machine learning.

BACKGROUND: Emergency departments (ED) are a portal of entry into the hospital and are uniquely positioned to influence the health care trajectories o...

A machine learning approach to risk assessment for alcohol withdrawal syndrome.

At present, risk assessment for alcohol withdrawal syndrome relies on clinical judgment. Our aim was to develop accurate machine learning tools to pre...

Machine learning in GI endoscopy: practical guidance in how to interpret a novel field.

There has been a vast increase in GI literature focused on the use of machine learning in endoscopy. The relative novelty of this field poses a challe...

Implementation of eHealth and AI integrated diagnostics with multidisciplinary digitized data: are we ready from an international perspective?

Digitization of medicine requires systematic handling of the increasing amount of health data to improve medical diagnosis. In this context, the integ...

Predicting the Risk of Inpatient Hypoglycemia With Machine Learning Using Electronic Health Records.

OBJECTIVE: We analyzed data from inpatients with diabetes admitted to a large university hospital to predict the risk of hypoglycemia through the use ...

Prediction of Postoperative Length of Hospital Stay Based on Differences in Nursing Narratives in Elderly Patients with Epithelial Ovarian Cancer.

OBJECTIVES:  The current study sought to evaluate whether nursing narratives can be used to predict postoperative length of hospital stay (LOS) follow...

Exploration of critical care data by using unsupervised machine learning.

BACKGROUND AND OBJECTIVE: Identification of subgroups may be useful to understand the clinical characteristics of ICU patients. The purposes of this s...

Predicting severe clinical events by learning about life-saving actions and outcomes using distant supervision.

Medical error is a leading cause of patient death in the United States. Among the different types of medical errors, harm to patients caused by doctor...

Proceedings from the First Global Artificial Intelligence in Gastroenterology and Endoscopy Summit.

BACKGROUND AND AIMS: Artificial intelligence (AI), specifically deep learning, offers the potential to enhance the field of GI endoscopy in areas rang...

A machine learning model for predicting risk of hospital readmission within 30 days of discharge: validated with LACE index and patient at risk of hospital readmission (PARR) model.

The objective of this study was to design and develop a predictive model for 30-day risk of hospital readmission using machine learning techniques. Th...

Exploring the Role of Artificial Intelligence in an Emergency and Trauma Radiology Department.

Emergency and trauma radiologists, emergency department's physicians and nurses, researchers, departmental leaders, and health policymakers have attem...