AIMC Topic: Patient Discharge

Clear Filters Showing 41 to 50 of 168 articles

Leveraging Summary Guidance on Medical Report Summarization.

IEEE journal of biomedical and health informatics
This study presents three deidentified large medical text datasets, named DISCHARGE, ECHO and RADIOLOGY, which contain 50 K, 16 K and 378 K pairs of report and summary that are derived from MIMIC-III, respectively. We implement convincing baselines o...

Surgery's Rosetta Stone: Natural language processing to predict discharge and readmission after general surgery.

Surgery
BACKGROUND: This study aimed to examine the accuracy with which multiple natural language processing artificial intelligence models could predict discharge and readmissions after general surgery.

Assessment of routine same-day discharge surgery for robot-assisted radical prostatectomy.

World journal of urology
INTRODUCTION: It is important for robotic surgery to be cost-effective, especially by reducing the length of stay (LOS). Therefore, we developed a protocol for day-case robot-assisted radical prostatectomy (RARP). This study aimed to validate this as...

Home-based upper limb stroke rehabilitation mechatronics: challenges and opportunities.

Biomedical engineering online
Interest in home-based stroke rehabilitation mechatronics, which includes both robots and sensor mechanisms, has increased over the past 12 years. The COVID-19 pandemic has exacerbated the existing lack of access to rehabilitation for stroke survivor...

Identifying inpatient mortality in MarketScan claims data using machine learning.

Pharmacoepidemiology and drug safety
PURPOSE: Inpatient mortality is an important variable in epidemiology studies using claims data. In 2016, MarketScan data began obscuring specific hospital discharge status types for patient privacy, including inpatient deaths, by setting the values ...

The Adelaide Score: An artificial intelligence measure of readiness for discharge after general surgery.

ANZ journal of surgery
BACKGROUND: This study aimed to examine the performance of machine learning algorithms for the prediction of discharge within 12 and 24 h to produce a measure of readiness for discharge after general surgery.

Supervised deep learning with vision transformer predicts delirium using limited lead EEG.

Scientific reports
As many as 80% of critically ill patients develop delirium increasing the need for institutionalization and higher morbidity and mortality. Clinicians detect less than 40% of delirium when using a validated screening tool. EEG is the criterion standa...