Information Technology

Assessing the practice of biomedical ontology evaluation: Gaps and opportunities.

With the proliferation of heterogeneous health care data in the last three decades, biomedical ontologies and controlled biomedical terminologies play a more and more important role in knowledge representation and management, data integration, natural language processing, as well as decision support for health information systems and biomedical research. Biomedical ontologies and controlled termin...

Informatics and machine learning to define the phenotype.

For the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained stagnant. Phenotype characterization has recently emerged as an exciting area of informatics and machine learning. The copious amounts of diverse bio...

Automated chart review utilizing natural language processing algorithm for asthma predictive index.

BACKGROUND: Thus far, no algorithms have been developed to automatically extract patients who meet Asthma Predictive Index (API) criteria from the Ele...

Ascertainment of asthma prognosis using natural language processing from electronic medical records.

NLP algorithm successfully determined asthma prognosis (i.e., no remission, long-term remission, and intermittent remission) by taking into account as...

Applying natural language processing techniques to develop a task-specific EMR interface for timely stroke thrombolysis: A feasibility study.

OBJECTIVE: To reduce errors in determining eligibility for intravenous thrombolytic therapy (IVT) in stroke patients through use of an enhanced task-s...

Predicting Chronic Disease Hospitalizations from Electronic Health Records: An Interpretable Classification Approach.

Urban living in modern large cities has significant adverse effects on health, increasing the risk of several chronic diseases. We focus on the two le...

Development and Validation of Machine Learning Models for Prediction of 1-Year Mortality Utilizing Electronic Medical Record Data Available at the End of Hospitalization in Multicondition Patients: a Proof-of-Concept Study.

BACKGROUND: Predicting death in a cohort of clinically diverse, multicondition hospitalized patients is difficult. Prognostic models that use electron...

Labeling for Big Data in radiation oncology: The Radiation Oncology Structures ontology.

PURPOSE: Leveraging Electronic Health Records (EHR) and Oncology Information Systems (OIS) has great potential to generate hypotheses for cancer treat...

The eXtensible ontology development (XOD) principles and tool implementation to support ontology interoperability.

Ontologies are critical to data/metadata and knowledge standardization, sharing, and analysis. With hundreds of biological and biomedical ontologies d...

Delirium Prediction using Machine Learning Models on Preoperative Electronic Health Records Data.

Electronic Health Records (EHR) are mainly designed to record relevant patient information during their stay in the hospital for administrative purpos...

Harnessing electronic medical records to advance research on multiple sclerosis.

BACKGROUND: Electronic medical records (EMR) data are increasingly used in research, but no studies have yet evaluated similarity between EMR and rese...

Improving the interoperability of biomedical ontologies with compound alignments.

BACKGROUND: Ontologies are commonly used to annotate and help process life sciences data. Although their original goal is to facilitate integration an...

Prediction of venous thromboembolism using semantic and sentiment analyses of clinical narratives.

Venous thromboembolism (VTE) is the third most common cardiovascular disorder. It affects people of both genders at ages as young as 20 years. The inc...

How Confounder Strength Can Affect Allocation of Resources in Electronic Health Records.

When electronic health record (EHR) data are used, multiple approaches may be available for measuring the same variable, introducing potentially confo...

[Position Paper of The AG Digital Health DNVF on Digital Health Applications: Framework Conditions For Use in Health Care, Structural Development and Science].

The term "digital health" is currently the most comprehensive term that includes all information and communication technologies in healthcare, includi...

Classifying clinical notes with pain assessment using machine learning.

Pain is a significant public health problem, affecting millions of people in the USA. Evidence has highlighted that patients with chronic pain often s...

Automatic Methods to Extract New York Heart Association Classification from Clinical Notes.

Cardiac Resynchronization Therapy (CRT) is an established pacing therapy for heart failure patients. The New York Heart Association (NYHA) classificat...

Estimating Local Costs Associated With Clostridium difficile Infection Using Machine Learning and Electronic Medical Records.

BACKGROUND Reported per-patient costs of Clostridium difficile infection (CDI) vary by 2 orders of magnitude among different hospitals, implying that ...

Efficient k-NN Implementation for Real-Time Detection of Cough Events in Smartphones.

The potential  of telemedicine in respiratory health care has not been completely unveiled in part due to the inexistence of reliable objective measur...

Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis.

The past decade has seen an explosion in the amount of digital information stored in electronic health records (EHRs). While primarily designed for ar...