Information Technology

Strategies to Tackle the Global Burden of Diabetic Retinopathy: From Epidemiology to Artificial Intelligence.

Diabetes is a global public health disease projected to affect 642 million adults by 2040, with about 75% residing in low- and middle-income countries. Diabetic retinopathy (DR) affects 1 in 3 people with diabetes and remains the leading cause of blindness in working-aged adults. There are 3 broad strategic imperatives to prevent blindness caused by DR. Primary prevention requires preventing or de...

A hybrid mathematical programming model for optimal project portfolio selection using fuzzy inference system and analytic hierarchy process.

The primary goal in project portfolio management is to select and manage the optimal set of projects that contribute the maximum in business value. However, selecting Information Technology (IT) projects is a difficult task due to the complexities and uncertainties inherent in the strategic-operational nature of the process, and the existence of both quantitative and qualitative criteria. We propo...

Impact of Coronary Computerized Tomography Angiography-Derived Plaque Quantification and Machine-Learning Computerized Tomography Fractional Flow Reserve on Adverse Cardiac Outcome.

This study investigated the impact of coronary CT angiography (cCTA)-derived plaque markers and machine-learning-based CT-derived fractional flow rese...

Bimodal learning via trilogy of skip-connection deep networks for diabetic retinopathy risk progression identification.

BACKGROUND: Diabetic Retinopathy (DR) is considered a pathology of retinal vascular complications, which stays in the top causes of vision impairment ...

What Can We Expect Following Anterior Total Hip Arthroplasty on a Regular Operating Table? A Validation Study of an Artificial Intelligence Algorithm to Monitor Adverse Events in a High-Volume, Nonacademic Setting.

BACKGROUND: Quality monitoring is increasingly important to support and assure sustainability of the orthopedic practice. Surgeons in nonacademic sett...

Readmission prediction using deep learning on electronic health records.

Unscheduled 30-day readmissions are a hallmark of Congestive Heart Failure (CHF) patients that pose significant health risks and escalate care cost. I...

Implementation of a cloud-based referral platform in ophthalmology: making telemedicine services a reality in eye care.

BACKGROUND: Hospital Eye Services (HES) in the UK face an increasing number of optometric referrals driven by progress in retinal imaging. The Nationa...

TyG-er: An ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health Records.

BACKGROUND: Insulin resistance is an early-stage deterioration of Type 2 diabetes. Identification and quantification of insulin resistance requires sp...

Telemedicine, Telestroke, and Artificial Intelligence Can Be Coded with the International Classification of Health Interventions.

Performance of a Natural Language Processing Method to Extract Stone Composition From the Electronic Health Record.

OBJECTIVES: To demonstrate the utility of a natural language processing (NLP) algorithm for mining kidney stone composition in a large-scale electroni...

A disease inference method based on symptom extraction and bidirectional Long Short Term Memory networks.

The wide applications of automatic disease inference in many medical fields improve the efficiency of medical treatments. Many efforts have been made ...

Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings.

In order to advance precision medicine, detailed clinical features ought to be described in a way that leverages current knowledge. Although data coll...

Detection of probable dementia cases in undiagnosed patients using structured and unstructured electronic health records.

BACKGROUND: Dementia is underdiagnosed in both the general population and among Veterans. This underdiagnosis decreases quality of life, reduces oppor...

Augmented intelligence with natural language processing applied to electronic health records for identifying patients with non-alcoholic fatty liver disease at risk for disease progression.

OBJECTIVE: Electronic health record (EHR) systems contain structured data (such as diagnostic codes) and unstructured data (clinical documentation). C...

Relevant Word Order Vectorization for Improved Natural Language Processing in Electronic Health Records.

Electronic health records (EHR) represent a rich resource for conducting observational studies, supporting clinical trials, and more. However, much of...

Using natural language processing of clinical text to enhance identification of opioid-related overdoses in electronic health records data.

PURPOSE: To enhance automated methods for accurately identifying opioid-related overdoses and classifying types of overdose using electronic health re...

Enhancing ontology-driven diagnostic reasoning with a symptom-dependency-aware Naïve Bayes classifier.

BACKGROUND: Ontology has attracted substantial attention from both academia and industry. Handling uncertainty reasoning is important in researching o...

Natural Language Processing to Quantify Microbial Keratitis Measurements.

A natural language processing (NLP) algorithm to extract microbial keratitis morphology measurements from the electronic health record (EHR) was 75-96...

Deep Sequential Models for Suicidal Ideation From Multiple Source Data.

This paper presents a novel method for predicting suicidal ideation from electronic health records (EHR) and ecological momentary assessment (EMA) dat...

Automating Ischemic Stroke Subtype Classification Using Machine Learning and Natural Language Processing.

OBJECTIVE: The manual adjudication of disease classification is time-consuming, error-prone, and limits scaling to large datasets. In ischemic stroke ...