Hospitalists

A comparative study on deep learning models for text classification of unstructured medical notes with various levels of class imbalance.

BACKGROUND: Discharge medical notes written by physicians contain important information about the health condition of patients. Many deep learning algorithms have been successfully applied to extract important information from unstructured medical notes data that can entail subsequent actionable results in the medical domain. This study aims to explore the model performance of various deep learnin...

Reconstruction of missing spring discharge by using deep learning models with ensemble empirical mode decomposition of precipitation.

A continuous and complete spring discharge record is critical in understanding the hydrodynamic behavior of karst aquifers and the variability of freshwater resources. However, due to equipment errors, failure of observation and other reasons, missing data is a common problem for spring discharge monitoring and further hydrological investigations and data analysis. In this study, a novel approach ...

Liver Damage Is Related to the Degree of Being Underweight in Anorexia Nervosa and Improves Rapidly with Weight Gain.

Background: The present study investigates the relationship between hypertransaminasemia and malnutrition on the basis of a very large number of patie...

Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT.

The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifyi...

Evaluation of the models generated from clinical features and deep learning-based segmentations: Can thoracic CT on admission help us to predict hospitalized COVID-19 patients who will require intensive care?

BACKGROUND: The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and arti...

A Long Short-Term Memory Network for Plasma Diagnosis from Langmuir Probe Data.

Electrostatic probe diagnosis is the main method of plasma diagnosis. However, the traditional diagnosis theory is affected by many factors, and it is...

Early prediction of acute necrotizing pancreatitis by artificial intelligence: a prospective cohort-analysis of 2387 cases.

Pancreatic necrosis is a consistent prognostic factor in acute pancreatitis (AP). However, the clinical scores currently in use are either too complic...

Early severity prediction of BPD for premature infants from chest X-ray images using deep learning: A study at the 28th day of oxygen inhalation.

BACKGROUND AND OBJECTIVE: Bronchopulmonary dysplasia is a common respiratory disease in premature infants. The severity is diagnosed at the 56th day a...

Optimization of Reservoir Flood Control Operation Based on Multialgorithm Deep Learning.

With the rapid development of China's social economy, it is the most important task for the water conservancy industry to make use of the existing wat...

Experimental analysis and parameter optimization on the reduction of NOx from diesel engine using RSM and ANN Model.

The major emission sources of NO are from automobiles, trucks, and various non-road vehicles, power plants, coal fired boilers, cement kilns, turbines...

Cloud-based neuro-fuzzy hydro-climatic model for water quality assessment under uncertainty and sensitivity.

River water quality is a function of various bio-physicochemical parameters which can be aggregated for calculating the Water Quality Index (WQI). How...

Outcome Prediction in Patients with Severe Traumatic Brain Injury Using Deep Learning from Head CT Scans.

Background After severe traumatic brain injury (sTBI), physicians use long-term prognostication to guide acute clinical care yet struggle to predict o...

Natural language processing of admission notes to predict severe maternal morbidity during the delivery encounter.

BACKGROUND: Severe maternal morbidity and mortality remain public health priorities in the United States, given their high rates relative to other hig...

Preliminary Outcomes After Same Day Discharge Protocol for Robot-Assisted Partial Nephrectomy: A Single Centre Experience.

OBJECTIVE: To assess the feasibility and safety of same-day discharge (SDD) surgery after robot-assisted partial nephrectomy (RAPN) in patients with s...

FoSSA Optimization-Based SVM Classifier for the Recognition of Partial Discharge Patterns in HV Cables.

In order to enhance the classification accuracy and the generalization performance of the SVM classifier in cable partial discharge (PD) pattern recog...

Early identification of ICU patients at risk of complications: Regularization based on robustness and stability of explanations.

The aim of this study is to build machine learning models to predict severe complications using administrative and clinical elements that are collecte...

Effect of Different Nursing Interventions on Discharged Patients with Cardiac Valve Replacement Evaluated by Deep Learning Algorithm-Based MRI Information.

This study was aimed to explore the application of cardiac magnetic resonance imaging (MRI) image segmentation model based on U-Net in the diagnosis o...

Characteristics of Computed Tomography Images for Patients with Acute Liver Injury Caused by Sepsis under Deep Learning Algorithm.

This study was aimed at exploring the application of image segmentation based on full convolutional neural network (FCN) in liver computed tomography ...

Automatic Deep-Learning Segmentation of Epicardial Adipose Tissue from Low-Dose Chest CT and Prognosis Impact on COVID-19.

Background: To develop a deep-learning (DL) pipeline that allowed an automated segmentation of epicardial adipose tissue (EAT) from low-dose computed ...

Neuronal Apoptosis in Patients with Liver Cirrhosis and Neuronal Epileptiform Discharge Model Based upon Multi-Modal Fusion Deep Learning.

Neurons refer to nerve cells. Each neuron is connected with thousands of other neurons to form a corresponding functional area and carry out complex c...