Hospitalists

Decision support model for the patient admission scheduling problem based on picture fuzzy aggregation information and TOPSIS methodology.

Health care systems around the world do not have sufficient medical services to immediately offer el...

Evaluating the state of the art in missing data imputation for clinical data.

Clinical data are increasingly being mined to derive new medical knowledge with a goal of enabling g...

Strategies for Cost Optimization in Minimally Invasive Gynecologic Surgery.

BACKGROUND: Cost and quality are important, complex, and intertwined surgical outcomes. Evidence sug...

Electronic health record machine learning model predicts trauma inpatient mortality in real time: A validation study.

INTRODUCTION: Patient outcome prediction models are underused in clinical practice because of lack o...

Exploring Features Contributing to the Early Prediction of Sepsis Using Machine Learning.

The increasing availability of electronic health records and administrative data and the adoption of...

Artificial neural network prediction of same-day discharge following primary total knee arthroplasty based on preoperative and intraoperative variables.

AIMS: This study used an artificial neural network (ANN) model to determine the most important pre- ...

Predicting the Need For Vasopressors in the Intensive Care Unit Using an Attention Based Deep Learning Model.

BACKGROUND: Previous models on prediction of shock mostly focused on septic shock and often required...

[Robot-assisted single-anastomosis duodeno-ileal bypass with sleeve gastrectomy].

Single-anastomosis duodeno-ileal bypass with sleeve gastrectomy (SADI-S) is simpler and has similar ...

First Steps to Evaluate an NLP Tool's Medication Extraction Accuracy from Discharge Letters.

INTRODUCTION: The aim of this study is to evaluate the use of a natural language processing (NLP) so...

Low Predictability of Readmissions and Death Using Machine Learning in Cirrhosis.

INTRODUCTION: Readmission and death in cirrhosis are common, expensive, and difficult to predict. Ou...

Development of a Complication- and Treatment-Aware Prediction Model for Favorable Functional Outcome in Aneurysmal Subarachnoid Hemorrhage Based on Machine Learning.

BACKGROUND: Current prognostic tools in aneurysmal subarachnoid hemorrhage (aSAH) are constrained by...

Utilizing Machine Learning Methods for Preoperative Prediction of Postsurgical Mortality and Intensive Care Unit Admission.

OBJECTIVE: To compare the performance of machine learning models against the traditionally derived C...

Prediction of a Due Date Based on the Pregnancy History Data Using Machine Learning.

Prediction of a labor due date is important especially for the pregnancies with high risk of complic...

Machine Learning for Predictive Modeling of 90-day Readmission, Major Medical Complication, and Discharge to a Facility in Patients Undergoing Long Segment Posterior Lumbar Spine Fusion.

STUDY DESIGN: Retrospective case control study.

A Survey of Nurses' Need for Care Robots in Children's Hospitals: Combining Robot-Care, Game-Care, and Edu-Care.

The purpose of this study was to investigate nurses' need for care robots in children's hospitals an...

Real-time finger force prediction via parallel convolutional neural networks: a preliminary study.

Continuous and accurate decoding of intended motions is critical for human-machine interactions. Her...

Dexterous Force Estimation during Finger Flexion and Extension Using Motor Unit Discharge Information.

With the development of advanced robotic hands, a reliable neural-machine interface is essential to ...

Machine Learning Prediction of Postoperative Emergency Department Hospital Readmission.

BACKGROUND: Although prediction of hospital readmissions has been studied in medical patients, it ha...

Role of Instruction Adherence During Highly Structured Robotic Arm Training on Motor Outcomes for Individuals After Chronic Stroke.

The aim of this study was to examine the effects of instruction adherence on upper limb motor outcom...

Performance of a Deep Learning Algorithm for Automated Segmentation and Quantification of Traumatic Pelvic Hematomas on CT.

The volume of pelvic hematoma at CT has been shown to be the strongest independent predictor of majo...

Response to Comment on "Utilizing Machine Learning Methods for Preoperative Prediction of Postsurgical Mortality and Intensive Care Unit Admission".