AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Patient Discharge

Showing 1 to 10 of 157 articles

Clear Filters

Predicting onward care needs at admission to reduce discharge delay using explainable machine learning.

Scientific reports
Early identification of patients who require onward referral to social care can prevent delays to discharge from hospital. We introduce an explainable machine learning (ML) model to identify potential social care needs at the first point of admission...

Automated generation of discharge summaries: leveraging large language models with clinical data.

Scientific reports
This study explores the use of open-source large language models (LLMs) to automate generation of German discharge summaries from structured clinical data. The structured data used to produce AI-generated summaries were manually extracted from electr...

Development of clinical decision support for patients older than 65 years with fall-related TBI using artificial intelligence modeling.

PloS one
BACKGROUND: Older persons comprise most traumatic brain injury (TBI)-related hospitalizations and deaths and are particularly susceptible to fall-induced TBIs. The combination of increased frailty and susceptibility to clinical decline creates a sign...

Enhancing readmission prediction model in older stroke patients by integrating insight from readiness for hospital discharge: Prospective cohort study.

International journal of medical informatics
BACKGROUND: The 30-day hospital readmission rate is a key indicator of healthcare quality and system efficiency. This study aimed to develop machine-learning (ML) models to predict unplanned 30-day readmissions in older patients with ischemic stroke ...

Machine Learning-Driven Modeling to Predict Postdischarge Venous Thromboembolism After Pancreatectomy for Pancreas Cancer.

Annals of surgical oncology
BACKGROUND: Postdischarge venous thromboembolism (pdVTE) is a life-threatening complication following resection for pancreatic cancer (PC). While national guidelines recommend extended chemoprophylaxis for all, adherence is low and ranges from 1.5 to...

Adaptable graph neural networks design to support generalizability for clinical event prediction.

Journal of biomedical informatics
OBJECTIVE: While many machine learning and deep learning-based models for clinical event prediction leverage various data elements from electronic healthcare records such as patient demographics and billing codes, such models face severe challenges w...

[Application Practice of AI Empowering Post-discharge Specialized Disease Management in Postoperative Rehabilitation of the Lung Cancer Patients Undergoing Surgery].

Zhongguo fei ai za zhi = Chinese journal of lung cancer
BACKGROUND: Lung cancer is the leading malignancy in China in terms of both incidence and mortality. With increased health awareness and the widespread use of low-dose computed tomography (CT), early diagnosis rates have been steadily improving. Surg...

Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms.

BMC medical informatics and decision making
INTRODUCTION: Accurate and timely discharge from the Post-Anesthesia Care Unit (PACU) is essential to prevent postoperative complications and optimize hospital resource utilization. Premature discharge can lead to severe issues such as respiratory or...

Predicting Readmission Among High-Risk Discharged Patients Using a Machine Learning Model With Nursing Data: Retrospective Study.

JMIR medical informatics
BACKGROUND: Unplanned readmissions increase unnecessary health care costs and reduce the quality of care. It is essential to plan the discharge care from the beginning of hospitalization to reduce the risk of readmission. Machine learning-based readm...

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

Journal of the American Medical Directors Association
OBJECTIVE: To identify self-reported social determinants of health (SDOH) among hospitalized patients that predict discharge to a skilled nursing facility (SNF).