AIMC Topic: Patient Readmission
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DeepBackRib: Deep learning to understand factors associated with readmissions after rib fractures.
The journal of trauma and acute care surgery
Sep 19, 2022
BACKGROUND: Deep neural networks yield high predictive performance, yet obscure interpretability limits clinical applicability. We aimed to build an explainable deep neural network that elucidates factors associated with readmissions after rib fractu...
Machine Learning-Based Analysis and Prediction of Unplanned 30-Day Readmissions After Pituitary Adenoma Resection: A Multi-Institutional Retrospective Study With External Validation.
Neurosurgery
Apr 8, 2022
BACKGROUND: Unplanned readmission after transsphenoidal resection of pituitary adenoma can occur in up to 10% of patients but is unpredictable.
Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?
Journal of the American Heart Association
Mar 24, 2022
Background Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction ...
Machine Learning Improves Prediction Over Logistic Regression on Resected Colon Cancer Patients.
The Journal of surgical research
Mar 11, 2022
INTRODUCTION: Despite advances, readmission and mortality rates for surgical patients with colon cancer remain high. Prediction models using regression techniques allows for risk stratification to aid periprocedural care. Technological advances have ...
Surgical Complications and Hospital Costs in Robot-Assisted Versus Conventional Laparoscopic Hysterectomy With Concurrent Sacrocolpopexy: Analysis of the Nationwide Readmissions Database.
Female pelvic medicine & reconstructive surgery
Feb 1, 2022
OBJECTIVES: Despite increasing use of robotic technology for minimally invasive hysterectomy with sacrocolpopexy, evidence supporting the benefits of these costly procedures remains inconclusive. This study aimed to compare differences in perioperati...
Using explainable machine learning to identify patients at risk of reattendance at discharge from emergency departments.
Scientific reports
Nov 2, 2021
Short-term reattendances to emergency departments are a key quality of care indicator. Identifying patients at increased risk of early reattendance could help reduce the number of missed critical illnesses and could reduce avoidable utilization of em...
The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge.
Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
Oct 22, 2021
OBJECTIVES: The machine learning prediction model Pacmed Critical (PC), currently under development, may guide intensivists in their decision-making process on the most appropriate time to discharge a patient from the intensive care unit (ICU). Given...
Predictive modeling for 14-day unplanned hospital readmission risk by using machine learning algorithms.
BMC medical informatics and decision making
Oct 20, 2021
BACKGROUND: Early unplanned hospital readmissions are associated with increased harm to patients, increased medical costs, and negative hospital reputation. With the identification of at-risk patients, a crucial step toward improving care, appropriat...
Prediction of Readmission in Geriatric Patients From Clinical Notes: Retrospective Text Mining Study.
Journal of medical Internet research
Oct 19, 2021
BACKGROUND: Prior literature suggests that psychosocial factors adversely impact health and health care utilization outcomes. However, psychosocial factors are typically not captured by the structured data in electronic medical records (EMRs) but are...
Machine Learning to Predict Outcomes and Cost by Phase of Care After Coronary Artery Bypass Grafting.
The Annals of thoracic surgery
Sep 25, 2021
BACKGROUND: Machine learning may enhance prediction of outcomes after coronary artery bypass grafting (CABG). We sought to develop and validate a dynamic machine learning model to predict CABG outcomes at clinically relevant pre- and postoperative ti...
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