Endocrinology

Establishment and Analysis of a Combined Diagnostic Model of Polycystic Ovary Syndrome with Random Forest and Artificial Neural Network.

Polycystic ovary syndrome (PCOS) is one of the most common metabolic and reproductive endocrinopathi...

Humanoid robot-assisted interventions among children with diabetes: A systematic scoping review.

BACKGROUND: Although the humanoid robot is highly engaging for children, whether humanoid robot-assi...

Learning machine approach reveals microbial signatures of diet and sex in dog.

The characterization of the microbial population of many niches of the organism, as the gastrointest...

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model.

Machine learning (ML) algorithms permit the integration of different features into a model to perfor...

Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review.

The prevalence of childhood and adolescence overweight an obesity is raising at an alarming rate in ...

MRI radiomics for the prediction of recurrence in patients with clinically non-functioning pituitary macroadenomas.

Twelve to 66% of patients with clinically non-functioning pituitary adenoma (NFPA) experience tumor ...

Prediction of Neuropeptides from Sequence Information Using Ensemble Classifier and Hybrid Features.

As hormones in the endocrine system and neurotransmitters in the immune system, neuropeptides (NPs) ...

Deep Learning Modeling of Androgen Receptor Responses to Prostate Cancer Therapies.

Gain-of-function mutations in human androgen receptor (AR) are among the major causes of drug resist...

Thyroid Ultrasound Reports: Will the Thyroid Imaging, Reporting, and Data System Improve Natural Language Processing Capture of Critical Thyroid Nodule Features?

BACKGROUND: Critical thyroid nodule features are contained in unstructured ultrasound (US) reports. ...

Diagnosis of diabetes in pregnant woman using a Chaotic-Jaya hybridized extreme learning machine model.

As stated by World Health Organization (WHO) report, 246 million individuals have suffered with diab...

A Novel Approach for Continuous Health Status Monitoring and Automatic Detection of Infection Incidences in People With Type 1 Diabetes Using Machine Learning Algorithms (Part 2): A Personalized Digital Infectious Disease Detection Mechanism.

BACKGROUND: Semisupervised and unsupervised anomaly detection methods have been widely used in vario...

Predictive modeling of estrogen receptor agonism, antagonism, and binding activities using machine- and deep-learning approaches.

As defined by the World Health Organization, an endocrine disruptor is an exogenous substance or mix...

Rule-based automatic diagnosis of thyroid nodules from intraoperative frozen sections using deep learning.

Frozen sections provide a basis for rapid intraoperative diagnosis that can guide surgery, but the d...

Congenital Hypothyroidism due to a Low Level of Maternal Thyrotropin Receptor-Blocking Antibodies.

INTRODUCTION: Maternal TSH receptor antibodies (TRAbs) can cross the placenta and affect fetal and n...

Statistical inference for natural language processing algorithms with a demonstration using type 2 diabetes prediction from electronic health record notes.

The pointwise mutual information statistic (PMI), which measures how often two words occur together ...

Discovery of different metabotypes in overconditioned dairy cows by means of machine learning.

Using data from targeted metabolomics in serum in combination with machine learning (ML) approaches,...

Efficient Deep Learning Architecture for Detection and Recognition of Thyroid Nodules.

Ultrasonography is widely used in the clinical diagnosis of thyroid nodules. Ultrasound images of th...

Economic Challenges of Artificial Intelligence Adoption for Diabetic Retinopathy.

Prediction of pituitary adenoma surgical consistency: radiomic data mining and machine learning on T2-weighted MRI.

PURPOSE: Pituitary macroadenoma consistency can influence the ease of lesion removal during surgery,...

Pituitary Tumors in the Computational Era, Exploring Novel Approaches to Diagnosis, and Outcome Prediction with Machine Learning.

BACKGROUND: Machine learning has emerged as a viable asset in the setting of pituitary surgery. In t...

EAGA-MLP-An Enhanced and Adaptive Hybrid Classification Model for Diabetes Diagnosis.

Disease diagnosis is a critical task which needs to be done with extreme precision. In recent times,...