Endocrinology

Machine Learning Approach to find the relation between Endometriosis, benign breast disease, cystitis and non-toxic goiter.

The exact mechanism of endometriosis is unknown. The recommendation system (RS) based on item similarities of machine learning has never been applied to the relationship between diseases. The study aim was to identify diseases associated with endometriosis by applying RS based on item similarities to insurance data in South Korea. Women aged 15 to 45 years extracted from the Korean Health Insuranc...

Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs.

Increasing evidence has indicated that microRNAs(miRNAs) play vital roles in various pathological processes and thus are closely related with many complex human diseases. The identification of potential disease-related miRNAs offers new opportunities to understand disease etiology and pathogenesis. Although there have been numerous computational methods proposed to predict reliable miRNA-disease a...

Simple Linear Support Vector Machine Classifier Can Distinguish Impaired Glucose Tolerance Versus Type 2 Diabetes Using a Reduced Set of CGM-Based Glycemic Variability Indices.

BACKGROUND: Many glycemic variability (GV) indices exist in the literature. In previous works, we demonstrated that a set of GV indices, extracted fro...

A data-driven approach to referable diabetic retinopathy detection.

UNLABELLED: Prior art on automated screening of diabetic retinopathy and direct referral decision shows promising performance; yet most methods build ...

Evolution Strategies in Transaxillary Robotic Thyroidectomy: Considerations on the First 449 Cases Performed.

BACKGROUND: In the past 20 years, the fast spread of new surgical technologies has reached an important peak with the advent of the robotic surgery. M...

A multi-scale data fusion framework for bone age assessment with convolutional neural networks.

Bone age assessment (BAA) has various clinical applications such as diagnosis of endocrine disorders and prediction of final adult height for adolesce...

Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT.

PURPOSE: To develop a deep learning-based computer-aided diagnosis (CAD) system for use in the CT diagnosis of cervical lymph node metastasis (LNM) in...

[Robot-assisted transaxillary thyroid surgery; 5 years later].

Five years ago, robot-assisted transaxillary thyroidectomy (RATT) was introduced in the Netherlands. The major advantage of this technique, when compa...

Risk-based postprandial hypoglycemia forecasting using supervised learning.

BACKGROUND: Predicting insulin-induced postprandial hypoglycemic events is critical for the safety of type 1 diabetes patients because an early warnin...

Detecting Hypoglycemia Incidents Reported in Patients' Secure Messages: Using Cost-Sensitive Learning and Oversampling to Reduce Data Imbalance.

BACKGROUND: Improper dosing of medications such as insulin can cause hypoglycemic episodes, which may lead to severe morbidity or even death. Although...

Light-driven ultrasensitive self-powered cytosensing of circulating tumor cells via integration of biofuel cells and a photoelectrochemical strategy.

Herein, a light-driven, membrane-less and mediator-less self-powered cytosensing platform via integration of biofuel cells (BFCs) and a photoelectroch...

Classifying Breast Cancer Subtypes Using Multiple Kernel Learning Based on Omics Data.

It is very significant to explore the intrinsic differences in breast cancer subtypes. These intrinsic differences are closely related to clinical dia...

DeephESC 2.0: Deep Generative Multi Adversarial Networks for improving the classification of hESC.

Human embryonic stem cells (hESC), derived from the blastocysts, provide unique cellular models for numerous potential applications. They have great p...

Discovery of small molecule binders of human FSHR(TMD) with novel structural scaffolds by integrating structural bioinformatics and machine learning algorithms.

BACKGROUND: The activation of follicle stimulating hormone receptor (FSHR) by FSH and the consequent downstream signaling activities are crucial for r...

Feasibility of simple machine learning approaches to support detection of non-glaucomatous visual fields in future automated glaucoma clinics.

OBJECTIVES: To assess the performance of feed-forward back-propagation artificial neural networks (ANNs) in detecting field defects caused by pituitar...

Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning.

The histological analysis of tissue samples, widely used for disease diagnosis, involves lengthy and laborious tissue preparation. Here, we show that ...

Detecting abnormal thyroid cartilages on CT using deep learning.

PURPOSE: The purpose of this study was to evaluate the performance of a deep learning algorithm in detecting abnormalities of thyroid cartilage from c...

Accuracy of ultrawide-field fundus ophthalmoscopy-assisted deep learning for detecting treatment-naïve proliferative diabetic retinopathy.

PURPOSE: We investigated using ultrawide-field fundus images with a deep convolutional neural network (DCNN), which is a machine learning technology, ...

Job interview training targeting nonverbal communication using an android robot for individuals with autism spectrum disorder.

Job interviews are significant barriers for individuals with autism spectrum disorder because these individuals lack good nonverbal communication skil...

Using natural language processing and machine learning to classify health literacy from secure messages: The ECLIPPSE study.

Limited health literacy is a barrier to optimal healthcare delivery and outcomes. Current measures requiring patients to self-report limitations are t...