Hospitalists

Stratification of Length of Stay Prediction following Surgical Cytoreduction in Advanced High-Grade Serous Ovarian Cancer Patients Using Artificial Intelligence; the Leeds L-AI-OS Score.

(1) Background: Length of stay (LOS) has been suggested as a marker of the effectiveness of short-term care. Artificial Intelligence (AI) technologies could help monitor hospital stays. We developed an AI-based novel predictive LOS score for advanced-stage high-grade serous ovarian cancer (HGSOC) patients following cytoreductive surgery and refined factors significantly affecting LOS. (2) Methods:...

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 patterns. The aim of this study was to build a tool that uses only data available at time of admission to predict prolonged hospital length of stay (LOS).

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 investigated to predict sediment discharge (Q) in free-...

Unraveling the complexities of urban fluvial flood hydraulics through AI.

As urbanization increases across the globe, urban flooding is an ever-pressing concern. Urban fluvial systems are highly complex, depending on a myria...

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 in the U.S. The medical professionals that treat p...

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 learning methods at the forefront in recent years. A...

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 interictal spikes. Neurophysiologists visually identify sp...

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. The aim of this study was to compare the accuracy o...

A Comprehensive Quantitative and Biological Neural Network Optimization Model of Sports Industry Structure Based on Knowledge Mapping.

In this paper, a comprehensive quantitative and biological neural network optimization model of sports industry structure is thoroughly studied and an...

Insulator Leakage Current Prediction Using Hybrid of Particle Swarm Optimization and Gene Algorithm-Based Neural Network and Surface Spark Discharge Data.

This study proposes a new superior hybrid algorithm, which is the particle swarm optimization (PSO) and gene algorithm (GA)-based neural network to pr...

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 patients with Post-Viral Fatigue (PVF) syndrome a...

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 contamination in relation to leakage current and ...

Decision-Making Application of the Cloud-Fog Hybrid Model Based on the Improved Convolutional Neural Network in Financial Services in Smart Medical Care.

In order to alleviate the "difficulty in seeing a doctor" for the masses, continuously optimize the service process, and explore new financial service...

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 and several deep learning models have been created ...

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

Machine learning can predict outcomes and determine variables contributing to precise prediction, and can thus classify patients with different risk f...

Predicting malnutrition from longitudinal patient trajectories with deep learning.

Malnutrition is common, morbid, and often correctable, but subject to missed and delayed diagnosis. Better screening and prediction could improve clin...

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 objective of our study was to compare clinical and p...

Identifying endotypes of individuals after an attack of pancreatitis based on unsupervised machine learning of multiplex cytokine profiles.

After an attack of pancreatitis, individuals may develop metabolic sequelae (eg, new-onset diabetes) and/or pancreatic cancer. These new-onset morbidi...

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 rapid data-driven approaches to facilitate clinical d...

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 (INEADS) remains unknown because such data are not r...