AIMC Topic: Length of Stay

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Predicting 2-Day Mortality of Thrombocytopenic Patients Based on Clinical Laboratory Data Using Machine Learning.

Medical care
BACKGROUND: Clinical laboratories have traditionally used a single critical value for thrombocytopenic events. This system, however, could lead to inaccuracies and inefficiencies, causing alarm fatigue and compromised patient safety.

Drivers of Prolonged Hospitalization Following Spine Surgery: A Game-Theory-Based Approach to Explaining Machine Learning Models.

The Journal of bone and joint surgery. American volume
BACKGROUND: Understanding the interactions between variables that predict prolonged hospital length of stay (LOS) following spine surgery can help uncover drivers of this risk in patients. This study utilized a novel game-theory-based approach to dev...

Adding Continuous Vital Sign Information to Static Clinical Data Improves the Prediction of Length of Stay After Intubation: A Data-Driven Machine Learning Approach.

Respiratory care
BACKGROUND: Bedside monitors in the ICU routinely measure and collect patients' physiologic data in real time to continuously assess the health status of patients who are critically ill. With the advent of increased computational power and the abilit...

Predicting Length of Stay for Cardiovascular Hospitalizations in the Intensive Care Unit: Machine Learning Approach.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Predicting Cardiovascular Length of stay based hospitalization at the time of patients' admitting to the coronary care unit (CCU) or (cardiac intensive care units CICU) is deemed as a challenging task to hospital management systems globally. Recently...

Dr. Agent: Clinical predictive model via mimicked second opinions.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Prediction of disease phenotypes and their outcomes is a difficult task. In practice, patients routinely seek second opinions from multiple clinical experts for complex disease diagnosis. Our objective is to mimic such a practice of seekin...

A Novel Machine Learning Model Developed to Assist in Patient Selection for Outpatient Total Shoulder Arthroplasty.

The Journal of the American Academy of Orthopaedic Surgeons
INTRODUCTION: Patient selection for outpatient total shoulder arthroplasty (TSA) is important to optimizing patient outcomes. This study aims to develop a machine learning tool that may aid in patient selection for outpatient total should arthroplast...

The Impact of Robotics in Learning Roux-en-Y Gastric Bypass: a Retrospective Analysis of 214 Laparoscopic and Robotic Procedures : Robotic Vs. Laparoscopic RYGB.

Obesity surgery
BACKGROUND: Proximal Roux-en-Y gastric bypass is commonly used to manage obesity, performed using laparoscopic or robot-assisted minimally invasive surgery. As the prevalence of robotic bariatric surgery increases, further data is required to justify...

EXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Predicting patient outcomes using healthcare/genomics data is an increasingly popular/important area. However, some diseases are rare and require data from multiple institutions to construct generalizable models. To address institutional d...

[Multicenter comparison of complications after robot-assisted and open simple prostatectomy].

Der Urologe. Ausg. A
INTRODUCTION: Robot-assisted simple prostatectomy (RASP) is a relatively new minimally invasive procedure for surgical treatment to manage symptomatic, therapy-refractory benign prostate hyperplasia (BPH) in prostate volumes >80 cm. Thus, postoperati...