Information Technology

Querying clinical data in HL7 RIM based relational model with morph-RDB.

BACKGROUND: Semantic interoperability is essential when carrying out post-genomic clinical trials where several institutions collaborate, since researchers and developers need to have an integrated view and access to heterogeneous data sources. One possible approach to accommodate this need is to use RDB2RDF systems that provide RDF datasets as the unified view. These RDF datasets may be materiali...

Design of an extensive information representation scheme for clinical narratives.

BACKGROUND: Knowledge representation frameworks are essential to the understanding of complex biomedical processes, and to the analysis of biomedical texts that describe them. Combined with natural language processing (NLP), they have the potential to contribute to retrospective studies by unlocking important phenotyping information contained in the narrative content of electronic health records (...

Combining Biomarkers with EMR Data to Identify Patients in Different Phases of Sepsis.

Sepsis is a leading cause of death and is the most expensive condition to treat in U.S. hospitals. Despite targeted efforts to automate earlier detect...

Word2Vec inversion and traditional text classifiers for phenotyping lupus.

BACKGROUND: Identifying patients with certain clinical criteria based on manual chart review of doctors' notes is a daunting task given the massive am...

TEPAPA: a novel in silico feature learning pipeline for mining prognostic and associative factors from text-based electronic medical records.

Vast amounts of clinically relevant text-based variables lie undiscovered and unexploited in electronic medical records (EMR). To exploit this untappe...

A cascaded approach for Chinese clinical text de-identification with less annotation effort.

With rapid adoption of Electronic Health Records (EHR) in China, an increasing amount of clinical data has been available to support clinical research...

Predicting all-cause risk of 30-day hospital readmission using artificial neural networks.

Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for pati...

Defining and characterizing the critical transition state prior to the type 2 diabetes disease.

BACKGROUND: Type 2 diabetes mellitus (T2DM), with increased risk of serious long-term complications, currently represents 8.3% of the adult population...

Detecting clinically relevant new information in clinical notes across specialties and settings.

BACKGROUND: Automated methods for identifying clinically relevant new versus redundant information in electronic health record (EHR) clinical notes is...

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization.

With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly covet...

Automated Critical Test Findings Identification and Online Notification System Using Artificial Intelligence in Imaging.

Purpose To evaluate the performance of an artificial intelligence (AI) tool using a deep learning algorithm for detecting hemorrhage, mass effect, or ...

NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation.

BACKGROUND: Ontologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate ...

Automated problem list generation and physicians perspective from a pilot study.

OBJECTIVE: An accurate, comprehensive and up-to-date problem list can help clinicians provide patient-centered care. Unfortunately, problem lists crea...

A new near-lossless EEG compression method using ANN-based reconstruction technique.

Compression algorithm is an essential part of Telemedicine systems, to store and transmit large amount of medical signals. Most of existing compressio...

Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks.

Purpose To evaluate the efficacy of deep convolutional neural networks (DCNNs) for detecting tuberculosis (TB) on chest radiographs. Materials and Met...

EHR-based phenotyping: Bulk learning and evaluation.

In data-driven phenotyping, a core computational task is to identify medical concepts and their variations from sources of electronic health records (...

The hospital of tomorrow in 10 points.

Technology has advanced rapidly in recent years and is continuing to do so, with associated changes in multiple areas, including hospital structure an...

Unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients.

BACKGROUND: Allowing patients to access their own electronic health record (EHR) notes through online patient portals has the potential to improve pat...

Prediction of Adverse Events in Patients Undergoing Major Cardiovascular Procedures.

Electronic health records (EHR) provide opportunities to leverage vast arrays of data to help prevent adverse events, improve patient outcomes, and re...

Early recognition of multiple sclerosis using natural language processing of the electronic health record.

BACKGROUND: Diagnostic accuracy might be improved by algorithms that searched patients' clinical notes in the electronic health record (EHR) for signs...