Transplantation

Predicting Acute Graft-Versus-Host Disease Using Machine Learning and Longitudinal Vital Sign Data From Electronic Health Records.

PURPOSE: Acute graft-versus-host disease (aGVHD) remains a significant complication of allogeneic hematopoietic cell transplantation (HCT) and limits its broader application. The ability to predict grade II to IV aGVHD could potentially mitigate morbidity and mortality. To date, researchers have focused on using snapshots of a patient (eg, biomarkers at a single time point) to predict aGVHD onset....

Comparison of semi-automatic and deep learning-based automatic methods for liver segmentation in living liver transplant donors.

PURPOSE: To compare the accuracy and repeatability of emerging machine learning based (i.e. deep) automatic segmentation algorithms with those of well-established semi-automatic (interactive) methods for determining liver volume in living liver transplant donors at computerized tomography (CT) imaging.

Automatic Segmentation of Multiple Organs on 3D CT Images by Using Deep Learning Approaches.

This chapter focuses on modern deep learning techniques that are proposed for automatically recognizing and segmenting multiple organ regions on three...

[Volume Measurements of Post-transplanted Liver of Pediatric Recipients Using Workstations and Deep Learning].

PURPOSE: The purpose of this study was to propose a method for segmentation and volume measurement of graft liver and spleen of pediatric transplant r...

Iatrogenic Cushing's syndrome as a consequence of nasal use of Betamethasone spray during pregnancy.

INTRODUCTION: Glucocorticoids (GC) are largely used for their anti-inflammatory and immunosuppressive effects. Until recently "local" administration (...

Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation.

Acute graft-versus-host disease (aGVHD) is 1 of the critical complications that often occurs following allogeneic hematopoietic stem cell transplantat...

Relevant Features in Nonalcoholic Steatohepatitis Determined Using Machine Learning for Feature Selection.

We investigated the prevalence and the most relevant features of nonalcoholic steatohepatitis (NASH), a stage of nonalcoholic fatty liver disease, (N...

Graft Rejection Prediction Following Kidney Transplantation Using Machine Learning Techniques: A Systematic Review and Meta-Analysis.

Kidney transplantation is recommended for patients with End-Stage Renal Disease (ESRD). However, complications, such as graft rejection are hard to pr...

Uncovering the mouse olfactory long non-coding transcriptome with a novel machine-learning model.

Very little is known about long non-coding RNAs (lncRNAs) in the mammalian olfactory sensory epithelia. Deciphering the non-coding transcriptome in ol...

DeepDSSR: Deep Learning Structure for Human Donor Splice Sites Recognition.

Human genes often, through alternative splicing of pre-messenger RNAs, produce multiple mRNAs and protein isoforms that may have similar or completely...

A sustainable HL7 FHIR based ontology for PHR data.

One of the most widely acknowledged standards in health informatics is HL7 (Health Level 7 International). HL7 FHIR® (Fast Healthcare Interoperability...

Re: Minimally Invasive, Robot-Assisted Procedure for Kidney Transplantation among Morbidly Obese: Positive Outcomes at 5 Years Post-Transplant.

A Robotic Platform for 3D Forelimb Rehabilitation with Rats.

In an attempt to promote greater functional recovery after spinal cord injury, researchers have begun exploring combinatorial treatments, such as robo...

SuperCT: a supervised-learning framework for enhanced characterization of single-cell transcriptomic profiles.

Characterization of individual cell types is fundamental to the study of multicellular samples. Single-cell RNAseq techniques, which allow high-throug...

Liver and Lung Transplant Advances.

Using Deep Learning in Automated Detection of Graft Detachment in Descemet Membrane Endothelial Keratoplasty: A Pilot Study.

PURPOSE: To evaluate a deep learning-based method to automatically detect graft detachment (GD) after Descemet membrane endothelial keratoplasty (DMEK...

Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department.

Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving...

LigVoxel: inpainting binding pockets using 3D-convolutional neural networks.

MOTIVATION: Structure-based drug discovery methods exploit protein structural information to design small molecules binding to given protein pockets. ...

Results of a Literature Review to Prepare Data Modelling in the Context of Kidney Transplant Rejection Diagnosis.

Due to demographic change the number of serious kidney diseases and thus required transplantations will increase. The increased demand for donor organ...

GIDB: a knowledge database for the automated curation and multidimensional analysis of molecular signatures in gastrointestinal cancer.

Gastrointestinal (GI) cancer is common, characterized by high mortality, and includes oesophagus, gastric, liver, bile duct, pancreas, rectal and colo...