Endocrinology

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) provide many opportunities for the discovery of new drugs and targets for nervous system disorders. In spite of their importance in the hormonal regulations and immune responses, the bioinformatics predictor for the identification of NPs is lacking. In this study, we develop a predictor for the iden...

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 resistance in prostate cancer (PCa). Identifying mutations that cause resistant phenotype is of critical importance for guiding treatment protocols, as well as for designing drugs that do not elicit adverse responses. However, experimental characterization of these mutations is time consuming and costly; ...

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. The Thyroid Imaging, Reporting, and Data System (T...

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 diabetes disease over worldwide and it is anticipated ...

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 various applications to detect anomalous objects from a...

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 mixture that alters function(s) of the endocrine syst...

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 diagnoses often challenge pathologists. Here we pro...

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 neonatal thyroid function. Maternal TSH receptor-bl...

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 in a document corpus, is a cornerstone of recently...

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, we aimed at (1) identifying divergent metabotypes...

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 thyroid nodules have different appearances, interior...

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, especially when using a transsphenoidal approach....

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 the past decade, the number of machine learning mod...

Early detection of type 2 diabetes mellitus using machine learning-based prediction models.

Most screening tests for T2DM in use today were developed using multivariate regression methods that are often further simplified to allow transformat...

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, medical data mining is gaining popularity in comp...

Leveraging Multimodal Deep Learning Architecture with Retina Lesion Information to Detect Diabetic Retinopathy.

PURPOSE: To improve disease severity classification from fundus images using a hybrid architecture with symptom awareness for diabetic retinopathy (DR...

Deep Physiological Model for Blood Glucose Prediction in T1DM Patients.

Accurate estimations for the near future levels of blood glucose are crucial for Type 1 Diabetes Mellitus (T1DM) patients in order to be able to react...

A deep learning approach based on convolutional LSTM for detecting diabetes.

Diabetes is a chronic disease that occurs when the pancreas does not generate sufficient insulin or the body cannot effectively utilize the produced i...

Advanced Diabetes Management Using Artificial Intelligence and Continuous Glucose Monitoring Sensors.

Wearable continuous glucose monitoring (CGM) sensors are revolutionizing the treatment of type 1 diabetes (T1D). These sensors provide in real-time, e...

Fatal case of hospital-acquired hypernatraemia in a neonate: lessons learned from a tragic error.

A 3-week-old boy with viral gastroenteritis was by error given 200 mL 1 mmol/mL hypertonic saline intravenously instead of isotonic saline. His plasma...