Hospitalists

Agile reversible shape-morphing of particle rafts.

Materials that transform shapes responding to external stimuli can bring unprecedented innovations to soft matter physics, soft robotics, wearable electronics, and architecture. As most conventional soft actuation technologies induce large deformations only in a preprogrammed manner at designated locations, the material systems capable of agile reversible deformations without prescribed patterns a...

Daily estimates of individual discharge likelihood with deep learning natural language processing in general medicine: a prospective and external validation study.

Machine learning, in particular deep learning, may be able to assist in the prediction of the length of stay and timing of discharge for individual patients. Artificial neural networks applied to medical text have previously shown promise in this area. In this study, a previously derived artificial neural network was applied to prospective and external validation datasets. In the prediction of dis...

Comparison of Supervised Machine Learning Algorithms for Classifying of Home Discharge Possibility in Convalescent Stroke Patients: A Secondary Analysis.

OBJECTIVES: Classifying the possibility of home discharge is important during stroke rehabilitation to support decision-making. There have been severa...

Assistive Framework for Automatic Detection of All the Zones in Retinopathy of Prematurity Using Deep Learning.

Retinopathy of prematurity (ROP) is a potentially blinding disorder seen in low birth weight preterm infants. In India, the burden of ROP is high, wit...

A machine learning based exploration of COVID-19 mortality risk.

Early prediction of patient mortality risks during a pandemic can decrease mortality by assuring efficient resource allocation and treatment planning....

Comparison of deep learning, radiomics and subjective assessment of chest CT findings in SARS-CoV-2 pneumonia.

PURPOSE: Comparison of deep learning algorithm, radiomics and subjective assessment of chest CT for predicting outcome (death or recovery) and intensi...

Predictive modelling of piezometric head and seepage discharge in earth dam using soft computational models.

Predictions of pore pressure and seepage discharge are the most important parameters in the design of earth dams and assessing their safety during the...

Contrasting factors associated with COVID-19-related ICU admission and death outcomes in hospitalised patients by means of Shapley values.

Identification of those at greatest risk of death due to the substantial threat of COVID-19 can benefit from novel approaches to epidemiology that lev...

Video-Sensing Characterization for Hydrodynamic Features: Particle Tracking-Based Algorithm Supported by a Machine Learning Approach.

The efficient and reliable monitoring of the flow of water in open channels provides useful information for preventing water slow-downs due to the dep...

Hybridized neural networks for non-invasive and continuous mortality risk assessment in neonates.

Premature birth is the primary risk factor in neonatal deaths, with the majority of extremely premature babies cared for in neonatal intensive care un...

Minimizing Selection and Classification Biases. Comment on "Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing".

Authors' Reply to: Minimizing Selection and Classification Biases Comment on "Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing".

Prediction of Bedridden Duration of Hospitalized Patients by Machine Learning Based on EMRs at Admission.

Being bedridden is a frequent comorbid condition that leads to a series of complications in clinical practice. The present study aimed to predict bedr...

Comparison of machine-learning methodologies for accurate diagnosis of sepsis using microarray gene expression data.

We investigate the feasibility of molecular-level sample classification of sepsis using microarray gene expression data merged by in silico meta-analy...

Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge.

Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains. However, these architectures have ...

Development and Validation of Machine Learning Models to Predict Admission From Emergency Department to Inpatient and Intensive Care Units.

STUDY OBJECTIVE: This study aimed to develop and validate 2 machine learning models that use historical and current-visit patient data from electronic...

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records.

Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-lear...

Machine learning analysis: general features, requirements and cardiovascular applications.

Artificial intelligence represents the science which will probably change the future of medicine by solving actually challenging issues. In this speci...

Diagnostic and prognostic capabilities of a biomarker and EMR-based machine learning algorithm for sepsis.

Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad-spectrum antibiotics is associated...

Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit.

Acute gastrointestinal bleeding is the most common gastrointestinal cause for hospitalization. For high-risk patients requiring intensive care unit st...