Endocrinology

Extracting Healthcare Quality Information from Unstructured Data.

Healthcare quality research is a fundamental task that involves assessing treatment patterns and measuring the associated patient outcomes to identify potential areas for improving healthcare. While both qualitative and quantitative approaches are used, a major obstacle for the quantitative approach is that many useful healthcare quality indicators are buried within provider narrative notes, requi...

The Problems of Realism-Based Ontology Design: a Case Study in Creating Definitions for an Application Ontology for Diabetes Camps.

A requirement of realism-based ontology design is that classes denote exclusively entities that exist objectively in reality and that their definitions adhere to strict criteria to ensure that the classes are re-usable in other ontologies while preserving their ontological commitment. Building realism-based ontologies is therefore quite challenging and time-consuming, demanding considerable traini...

Utilizing Smartphone-Based Machine Learning in Medical Monitor Data Collection: Seven Segment Digit Recognition.

Biometric measurements captured from medical devices, such as blood pressure gauges, glucose monitors, and weighing scales, are essential to tracking ...

Long-term outcome in inherited nephrogenic diabetes insipidus.

BACKGROUND: Inherited nephrogenic diabetes insipidus (NDI) is a rare disorder characterized by impaired urinary concentrating ability. Little clinical...

Accurate Diabetes Risk Stratification Using Machine Learning: Role of Missing Value and Outliers.

Diabetes mellitus is a group of metabolic diseases in which blood sugar levels are too high. About 8.8% of the world was diabetic in 2017. It is proje...

Impaired left ventricular diastolic function in T2DM patients is closely related to glycemic control.

BACKGROUND: Left ventricular (LV) diastolic dysfunction commonly is observed in individuals with type 2 diabetes mellitus (T2DM). We employed transtho...

Artificial neural network model for predicting the bioavailability of tacrolimus in patients with renal transplantation.

The objective of the current study was to explore the role of ABCB1 and CYP3A5 genetic polymorphisms in predicting the bioavailability of tacrolimus a...

Pharmacological therapy selection of type 2 diabetes based on the SWARA and modified MULTIMOORA methods under a fuzzy environment.

Medication selection for Type 2 Diabetes (T2D) is a challenging medical decision-making problem involving multiple medications that can be prescribed ...

The Promise and Perils of Wearable Physiological Sensors for Diabetes Management.

Development of truly useful wearable physiologic monitoring devices for use in diabetes management is still in its infancy. From wearable activity mon...

Grader Variability and the Importance of Reference Standards for Evaluating Machine Learning Models for Diabetic Retinopathy.

PURPOSE: Use adjudication to quantify errors in diabetic retinopathy (DR) grading based on individual graders and majority decision, and to train an i...

Application of an artificial neural network model for diagnosing type 2 diabetes mellitus and determining the relative importance of risk factors.

OBJECTIVES: To identify the most important demographic risk factors for a diagnosis of type 2 diabetes mellitus (T2DM) using a neural network model.

Antiphospholipase 2 receptor antibody levels to predict complete spontaneous remission in primary membranous nephropathy.

BACKGROUND: M-type phospholipase A2 receptor (APLA2R) is considered the major antigen involved in the pathogenesis of adult primary membranous nephrop...

Personalized prediction of drug efficacy for diabetes treatment via patient-level sequential modeling with neural networks.

Patients with type 2 diabetes mellitus are generally under continuous long-term medical treatment based on anti-diabetic drugs to achieve the desired ...

Friendship with a robot: Children's perception of similarity between a robot's physical and virtual embodiment that supports diabetes self-management.

OBJECTIVE: The PAL project develops a conversational agent with a physical (robot) and virtual (avatar) embodiment to support diabetes self-management...

Multiscale deep neural network based analysis of FDG-PET images for the early diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases with a commonly seen prodromal mild cognitive impairment (MCI) phase whe...

SetSVM: An Approach to Set Classification in Nuclei-Based Cancer Detection.

Due to the importance of nuclear structure in cancer diagnosis, several predictive models have been described for diagnosing a wide variety of cancers...

Optimized multi-level elongated quinary patterns for the assessment of thyroid nodules in ultrasound images.

Ultrasound imaging is one of the most common visualizing tools used by radiologists to identify the location of thyroid nodules. However, visual asses...

DMTO: a realistic ontology for standard diabetes mellitus treatment.

BACKGROUND: Treatment of type 2 diabetes mellitus (T2DM) is a complex problem. A clinical decision support system (CDSS) based on massive and distribu...

Comparing Three Data Mining Algorithms for Identifying the Associated Risk Factors of Type 2 Diabetes.

BACKGROUND: Increasing the prevalence of type 2 diabetes has given rise to a global health burden and a concern among health service providers and hea...

Characterization of Adrenal Lesions on Unenhanced MRI Using Texture Analysis: A Machine-Learning Approach.

BACKGROUND: Adrenal adenomas (AA) are the most common benign adrenal lesions, often characterized based on intralesional fat content as either lipid-r...