Hospital-Based Medicine

Hospitalists

Latest AI and machine learning research in hospitalists for healthcare professionals.

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Quo Vadis, Amadeo Hand Robot? A Randomized Study with a Hand Recovery Predictive Model in Subacute Stroke.

BACKGROUND: Early identification of hand-prognosis-factors at patient's admission could help to sele...

Same-Day Discharge After Robot-Assisted Partial Nephrectomy: Is It Worth It?

Robot-assisted partial nephrectomy (RAPN) has traditionally been performed as an inpatient procedur...

Robot-assisted rehabilitation training improves knee function and daily activity ability in older adults following total knee arthroplasty.

To evaluate the effects of robot-assisted rehabilitation training on knee function and the daily act...

Neurosurgery inpatient outcome prediction for discharge planning with deep learning and transfer learning.

INTRODUCTION: Deep learning may be able to assist with the prediction of neurosurgical inpatient out...

A Pilot Machine Learning Study Using Trauma Admission Data to Identify Risk for High Length of Stay.

INTRODUCTION: Trauma patients have diverse resource needs due to variable mechanisms and injury patt...

Application of classical and novel integrated machine learning models to predict sediment discharge during free-flow flushing.

In this study, the capabilities of classical and novel integrated machine learning models were inves...

Early prediction of patient discharge disposition in acute neurological care using machine learning.

BACKGROUND: Acute neurological complications are some of the leading causes of death and disability ...

Deep learning for automated epileptiform discharge detection from scalp EEG: A systematic review.

Automated interictal epileptiform discharge (IED) detection has been widely studied, with machine le...

Fully-Automated Spike Detection and Dipole Analysis of Epileptic MEG Using Deep Learning.

Magnetoencephalography (MEG) is a useful tool for clinically evaluating the localization of interict...

Comparison of physician and artificial intelligence-based symptom checker diagnostic accuracy.

Symptom checkers are increasingly used to assess new symptoms and navigate the health care system. T...

Application of an EMG-Rehabilitation Robot in Patients with Post-Coronavirus Fatigue Syndrome (COVID-19)-A Feasibility Study.

This pilot study aimed to assess the safety and feasibility of an EMG-driven rehabilitation robot in...

Fault Prediction Based on Leakage Current in Contaminated Insulators Using Enhanced Time Series Forecasting Models.

To improve the monitoring of the electrical power grid, it is necessary to evaluate the influence of...

Deep Learning-Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs.

Accurate estimation of mortality and time to death at admission for COVID-19 patients is important a...

Prediction algorithm for ICU mortality and length of stay using machine learning.

Machine learning can predict outcomes and determine variables contributing to precise prediction, an...

Predicting malnutrition from longitudinal patient trajectories with deep learning.

Malnutrition is common, morbid, and often correctable, but subject to missed and delayed diagnosis. ...

Clinical and oncological outcomes of open partial nephrectomy versus robot assisted partial nephrectomy over 15 years.

Partial nephrectomy (PN) is the gold standard surgical treatment for localized kidney cancer. The ob...

Mortality Prediction Analysis among COVID-19 Inpatients Using Clinical Variables and Deep Learning Chest Radiography Imaging Features.

The emergence of the COVID-19 pandemic over a relatively brief interval illustrates the need for rap...

Using natural language processing to identify acute care patients who lack advance directives, decisional capacity, and surrogate decision makers.

The prevalence of patients who are Incapacitated with No Evident Advance Directives or Surrogates (I...

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