Intensivists

Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images.

PURPOSE: Needle-based procedures for diagnosing and treating prostate cancer, such as biopsy and brachytherapy, have incorporated three-dimensional (3D) transrectal ultrasound (TRUS) imaging to improve needle guidance. Using these images effectively typically requires the physician to manually segment the prostate to define the margins used for accurate registration, targeting, and other guidance ...

Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records.

BACKGROUND: Many mortality prediction models have been developed for patients in intensive care units (ICUs); most are based on data available at ICU admission. We investigated whether machine learning methods using analyses of time-series data improved mortality prognostication for patients in the ICU by providing real-time predictions of 90-day mortality. In addition, we examined to what extent ...

Multi-Person Pose Estimation Using an Orientation and Occlusion Aware Deep Learning Network.

Image based human behavior and activity understanding has been a hot topic in the field of computer vision and multimedia. As an important part, skele...

A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections.

Improved identification of bacterial and viral infections would reduce morbidity from sepsis, reduce antibiotic overuse, and lower healthcare costs. H...

Towards Reliable ARDS Clinical Decision Support: ARDS Patient Analytics with Free-text and Structured EMR Data.

In this work, we utilize a combination of free-text and structured data to build Acute Respiratory Distress Syndrome(ARDS) prediction models and ARDS ...

Deep Learning from Incomplete Data: Detecting Imminent Risk of Hospital-acquired Pneumonia in ICU Patients.

Hospital acquired pneumonia (HAP) is the second most common nosocomial infection in the ICU and costs an estimated $3.1 billion annually. The ability ...

Artificial intelligence in health care: accountability and safety.

The prospect of patient harm caused by the decisions made by an artificial intelligence-based clinical tool is something to which current practices of...

Using Domain Knowledge for Interpretable and Competitive Multi-Class Human Activity Recognition.

Human activity recognition (HAR) has become an increasingly popular application of machine learning across a range of domains. Typically the HAR task ...

Early detection of sepsis utilizing deep learning on electronic health record event sequences.

BACKGROUND: The timeliness of detection of a sepsis incidence in progress is a crucial factor in the outcome for the patient. Machine learning models ...

Multi-Contrast Super-Resolution MRI Through a Progressive Network.

Magnetic resonance imaging (MRI) is widely used for screening, diagnosis, image-guided therapy, and scientific research. A significant advantage of MR...

Intravenous Lignocaine-Fentanyl Versus Epidural Ropivacaine-Fentanyl for Postoperative Analgesia After Major Abdominal Oncosurgery: A Pilot Prospective Randomised Study.

OBJECTIVE: Epidural injection of local anaesthetics and intravenous opioid injection are two common analgesic strategies following major abdominal onc...

Combined Use of Three Machine Learning Modeling Methods to Develop a Ten-Gene Signature for the Diagnosis of Ventilator-Associated Pneumonia.

BACKGROUND This study aimed to use three modeling methods, logistic regression analysis, random forest analysis, and fully-connected neural network an...

An Artificial Neural Network-based Predictive Model to Support Optimization of Inpatient Glycemic Control.

Achieving glycemic control in critical care patients is of paramount importance, and has been linked to reductions in mortality, intensive care unit ...

Clinical management of sepsis can be improved by artificial intelligence: no.

Mixed-integer optimization approach to learning association rules for unplanned ICU transfer.

After admission to emergency department (ED), patients with critical illnesses are transferred to intensive care unit (ICU) due to unexpected clinical...

Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk.

To compare different deep learning architectures for predicting the risk of readmission within 30 days of discharge from the intensive care unit (ICU)...

Stability of Pentobarbital Hydrogel for Rectal Administration in Pediatric Procedural Sedation.

Pentobarbital is a sedative agent to limit children motion during computed tomography or magnetic resonance imaging (MRI) and ensures the successful ...

Computer-Aided Design of Antimicrobial Peptides: Are We Generating Effective Drug Candidates?

Antimicrobial peptides (AMPs), especially antibacterial peptides, have been widely investigated as potential alternatives to antibiotic-based therapie...

Machine-learning facilitates selection of a novel diagnostic panel of metabolites for the detection of heart failure.

The metabolic derangement is common in heart failure with reduced ejection fraction (HFrEF). The aim of the study was to check feasibility of the comb...

Deep Natural Language Processing Identifies Variation in Care Preference Documentation.

CONTEXT: Documentation of care preferences within 48 hours of admission to an intensive care unit (ICU) is a National Quality Forum-endorsed quality m...