Nephrology

A novel CS-NET architecture based on the unification of CNN, SVM and super-resolution spectrogram to monitor and classify blood pressure using photoplethysmography.

CONTEXT: Continuous blood pressure (BP) monitoring plays an important role while treating various cardiovascular diseases and hypertension. A high correlation between arterial blood pressure (ABP) and Photoplethysmogram (PPG) signal enables using a PPG signal to monitor and classify BP continuously. Control of BP in realtime is the basis for the prevention of hypertension.

Stratification of diabetes in the context of comorbidities, using representation learning and topological data analysis.

Diabetes is a heterogenous, multimorbid disorder with a large variation in manifestations, trajectories, and outcomes. The aim of this study is to validate a novel machine learning method for the phenotyping of diabetes in the context of comorbidities. Data from 9967 multimorbid patients with a new diagnosis of diabetes were extracted from Clinical Practice Research Datalink. First, using BEHRT (a...

Propensity-matched analysis of robotic versus sternotomy approaches for mitral valve replacement.

To compare early and medium-term outcomes between robotic and sternotomy approaches for mitral valve replacement (MVR). Clinical data of 1393 cases wh...

Open versus robot-assisted partial nephrectomy for highly complex renal masses: a meta-analysis of perioperitive and functional outcomes.

Robot-assisted partial nephrectomy (RAPN) is increasingly being used for the complex surgical management of renal masses. The comparison of RAPN with ...

Deep Learning on Electrocardiograms for Prediction of In-hospital Intradialytic Hypotension in Patients with ESKD.

Intradialytic hypotension is common in patients who are on hemodialysis. We applied deep learning techniques to ECGs to predict patients at risk of ID...

Physically defined long-term and short-term synapses for the development of reconfigurable analog-type operators capable of performing health care tasks.

Extracting valuable information from the overflowing data is a critical yet challenging task. Dealing with high volumes of biometric data, which are o...

Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study.

Predicting clinical deterioration in COVID-19 patients remains a challenging task in the Emergency Department (ED). To address this aim, we developed ...

High accuracy epidermal growth factor receptor mutation prediction via histopathological deep learning.

BACKGROUND: The detection of epidermal growth factor receptor (EGFR) mutations in patients with non-small cell lung cancer is critical for tyrosine ki...

A CT-based deep learning radiomics nomogram outperforms the existing prognostic models for outcome prediction in clear cell renal cell carcinoma: a multicenter study.

OBJECTIVES: To develop and validate a CT-based deep learning radiomics nomogram (DLRN) for outcome prediction in clear cell renal cell carcinoma (ccRC...

Fully Automated Longitudinal Assessment of Renal Stone Burden on Serial CT Imaging Using Deep Learning.

Use deep learning (DL) to automate the measurement and tracking of kidney stone burden over serial CT scans. This retrospective study included 259 s...

Deep Learning Identifies Intelligible Predictors of Poor Prognosis in Chronic Kidney Disease.

Early diagnosis and prediction of chronic kidney disease (CKD) progress within a given duration are critical to ensure personalized treatment, which c...

Classification and deep-learning-based prediction of Alzheimer disease subtypes by using genomic data.

Late-onset Alzheimer's disease (LOAD) is the most common multifactorial neurodegenerative disease among elderly people. LOAD is heterogeneous, and the...

Predicting dry weight change in Hemodialysis patients using machine learning.

BACKGROUND: Machine Learning has been increasingly used in the medical field, including managing patients undergoing hemodialysis. The random forest c...

Development of a portable device to quantify hepatic steatosis in potential donor livers.

An accurate estimation of liver fat content is necessary to predict how a donated liver will function after transplantation. Currently, a pathologist ...

A three-dimensional renal tumor anatomy and intrarenal relationship nephrometry (ADDD) for robot-assisted partial nephrectomy : 3D-CT based nephrometry for RAPN.

OBJECTIVE: To develop a 3D scoring system of tumor anatomy and intrarenal relationship for assessing surgical complexity and outcomes of robot-assiste...

Representation of time-varying and time-invariant EMR data and its application in modeling outcome prediction for heart failure patients.

OBJECTIVE: To represent a patient record with both time-invariant and time-varying features as a single vector using an end-to-end deep learning model...

Risk predictions of hospital-acquired pressure injury in the intensive care unit based on a machine learning algorithm.

Pressure injury (PI), or local damage to soft tissues and skin caused by prolonged pressure, remains controversial in the medical world. Patients in i...

A deep learning-based dynamic model for predicting acute kidney injury risk severity in postoperative patients.

BACKGROUND: Acute kidney injury is a common postoperative complication affecting between 10% and 30% of surgical patients. Acute kidney injury is asso...

Robot-assisted partial nephrectomy for complex renal tumors: Analysis of a large multi-institutional database.

INTRODUCTION: Highly complex renal masses pose a challenge to urologic surgeons' ability to perform robotic partial nephrectomy (RPN). Given the incre...

Construction of a neural network diagnostic model for renal fibrosis and investigation of immune infiltration characteristics.

BACKGROUND: Recently, the incidence rate of renal fibrosis has been increasing worldwide, greatly increasing the burden on society. However, the diagn...