Information Technology

Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules.

BACKGROUND AND AIM: Guidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time within electronic health records (EHRs) would overcome a major impediment. This requires an automated mechanism to accurately identify ("phenotype") ...

Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis.

Sepsis, a life-threatening organ dysfunction, is a clinical syndrome triggered by acute infection and affects over 1 million Americans every year. Untreated sepsis can progress to septic shock and organ failure, making sepsis one of the leading causes of morbidity and mortality in hospitals. Early detection of sepsis and timely antibiotics administration is known to save lives. In this work, we de...

Selection of Clinical Text Features for Classifying Suicide Attempts.

Research has demonstrated cohort misclassification when studies of suicidal thoughts and behaviors (STBs) rely on ICD-9/10-CM diagnosis codes. Electro...

Catch Me if You Can: Acute Events Hidden in Structured Chronic Disease Diagnosis Descriptions Show Detectable Recording Patterns in EHR.

Our previous research shows that structured cancer DX description data accuracy varied across electronic health record (EHR) segments (e.g. encounter ...

Multi-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases.

The mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare. However, dat...

AllergyMap: An Open Source Corpus of Allergy Mention Normalizations.

Allergy mention normalization is challenging because of the wide range of possible allergens including medications, foods, plants, animals, and consum...

Application of Machine Learning to Predict Patient No-Shows in an Academic Pediatric Ophthalmology Clinic.

Patient "no-shows" are missed appointments resulting in clinical inefficiencies, revenue loss, and discontinuity of care. Using secondary electronic h...

Artificial Intelligence Decision Support for Medical Triage.

Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a tr...

Timely and Efficient AI Insights on EHR: System Design.

A patient's electronic health record (EHR) contains extensive documentation of the patient's medical history but is difficult for clinicians to review...

Telemedicine in Arab Countries: Innovation, Research Trends, and Way Forward.

The progress and innovation in telemedicine within the Middle Eastern countries have not been heavily monitored. Therefore, the present study aims to...

Explanatory Analysis of a Machine Learning Model to Identify Hypertrophic Cardiomyopathy Patients from EHR Using Diagnostic Codes.

Hypertrophic cardiomyopathy (HCM) is a genetic heart disease that is the leading cause of sudden cardiac death (SCD) in young adults. Despite the well...

Interpreting a recurrent neural network's predictions of ICU mortality risk.

Deep learning has demonstrated success in many applications; however, their use in healthcare has been limited due to the lack of transparency into ho...

Using natural language processing to classify social work interventions.

OBJECTIVES: Health care organizations are increasingly employing social workers to address patients' social needs. However, social work (SW) activitie...

Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review.

OBJECTIVES: Patient representation learning refers to learning a dense mathematical representation of a patient that encodes meaningful information fr...

Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit.

Traditional Machine Learning (ML) models have had limited success in predicting Coronoavirus-19 (COVID-19) outcomes using Electronic Health Record (EH...

Ontological representation, classification and data-driven computing of phenotypes.

BACKGROUND: The successful determination and analysis of phenotypes plays a key role in the diagnostic process, the evaluation of risk factors and the...

An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks.

Telemedicine and all types of monitoring systems have proven to be a useful and low-cost tool with a high level of applicability in cardiology. The ob...

Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: a case-study in pulmonary embolism detection.

Recent advancements in deep learning have led to a resurgence of medical imaging and Electronic Medical Record (EMR) models for a variety of applicati...

Using multivariate long short-term memory neural network to detect aberrant signals in health data for quality assurance.

BACKGROUND: The data quality of electronic health records (EHR) has been a topic of increasing interest to clinical and health services researchers. O...

Quality assurance and enrichment of biological and biomedical ontologies and terminologies.

Biological and biomedical ontologies and terminologies are used to organize and store various domain-specific knowledge to provide standardization of ...