Critical Care

Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach.

There is increasing evidence that patient heterogeneity significantly hinders advancement in clinical trials and individualized care. This study aimed to identify distinct phenotypes in extremely low birth weight infants. We performed an agglomerative hierarchical clustering on principal components. Cluster validation was performed by cluster stability assessment with bootstrapping method. A total...

Ms RED: A novel multi-scale residual encoding and decoding network for skin lesion segmentation.

Computer-Aided Diagnosis (CAD) for dermatological diseases offers one of the most notable showcases where deep learning technologies display their impressive performance in acquiring and surpassing human experts. In such the CAD process, a critical step is concerned with segmenting skin lesions from dermoscopic images. Despite remarkable successes attained by recent deep learning efforts, much imp...

A priori prediction of local failure in brain metastasis after hypo-fractionated stereotactic radiotherapy using quantitative MRI and machine learning.

This study investigated the effectiveness of pre-treatment quantitative MRI and clinical features along with machine learning techniques to predict lo...

A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases.

Cardiovascular and chronic respiratory diseases are global threats to public health and cause approximately 19 million deaths worldwide annually. This...

Real-time 3D motion estimation from undersampled MRI using multi-resolution neural networks.

PURPOSE: To enable real-time adaptive magnetic resonance imaging-guided radiotherapy (MRIgRT) by obtaining time-resolved three-dimensional (3D) deform...

Automated Machine-Learning Framework Integrating Histopathological and Radiological Information for Predicting IDH1 Mutation Status in Glioma.

Diffuse gliomas are the most common malignant primary brain tumors. Identification of isocitrate dehydrogenase 1 (IDH1) mutations aids the diagnostic ...

A novel artificial intelligence based intensive care unit monitoring system: using physiological waveforms to identify sepsis.

A massive amount of multimodal data are continuously collected in the intensive care unit (ICU) along each patient stay, offering a great opportunity ...

Estimating redundancy in clinical text.

The current mode of use of Electronic Health Records (EHR) elicits text redundancy. Clinicians often populate new documents by duplicating existing no...

The Potential Cost-Effectiveness of a Machine Learning Tool That Can Prevent Untimely Intensive Care Unit Discharge.

OBJECTIVES: The machine learning prediction model Pacmed Critical (PC), currently under development, may guide intensivists in their decision-making p...

Applying artificial neural network for early detection of sepsis with intentionally preserved highly missing real-world data for simulating clinical situation.

PURPOSE: Some predictive systems using machine learning models have been developed to predict sepsis; however, they were mostly built with a low perce...

Can Deep Learning-Based Volumetric Analysis Predict Oxygen Demand Increase in Patients with COVID-19 Pneumonia?

: This study aimed to investigate whether predictive indicators for the deterioration of respiratory status can be derived from the deep learning data...

The impact of recency and adequacy of historical information on sepsis predictions using machine learning.

Sepsis is a major public and global health concern. Every hour of delay in detecting sepsis significantly increases the risk of death, highlighting th...

Development of a tracking error prediction system for the CyberKnife Synchrony Respiratory Tracking System with use of support vector regression.

PURPOSE: The accuracy of the CyberKnife Synchrony Respiratory Tracking System is dependent on the breathing pattern of a patient. Therefore, the track...

An empirical study of using radiology reports and images to improve ICU-mortality prediction.

The predictive Intensive Care Unit (ICU) scoring system plays an important role in ICU management for its capability of predicting important outcomes,...

A Machine Learning Model for Accurate Prediction of Sepsis in ICU Patients.

Although numerous studies are conducted every year on how to reduce the fatality rate associated with sepsis, it is still a major challenge faced by ...

A Method for Short-Term Prediction of the Metro Station's Individual Energy Consumption Item Based on G-ACO-BP Model.

This paper proposes a new method to make short-term predictions for the three kinds of primary energy consumption of power, lighting, and ventilated a...

Sentiment Analysis Based on the Nursing Notes on In-Hospital 28-Day Mortality of Sepsis Patients Utilizing the MIMIC-III Database.

In medical visualization, nursing notes contain rich information about a patient's pathological condition. However, they are not widely used in the pr...

Breath-hold 3D magnetic resonance cholangiopancreatography at 1.5 T using a deep learning-based noise-reduction approach: Comparison with the conventional respiratory-triggered technique.

OBJECTIVES: To assess the image quality of conventional respiratory-triggered 3-dimentional (3D) magnetic resonance cholangiopancreatography (Resp-MRC...

Multi-Source Transfer Learning Via Multi-Kernel Support Vector Machine Plus for B-Mode Ultrasound-Based Computer-Aided Diagnosis of Liver Cancers.

B-mode ultrasound (BUS) imaging is a routine tool for diagnosis of liver cancers, while contrast-enhanced ultrasound (CEUS) provides additional inform...

Budget constrained machine learning for early prediction of adverse outcomes for COVID-19 patients.

The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients th...