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Information Technology

Latest AI and machine learning research in information technology for healthcare professionals.

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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 h...

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....

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 m...

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 d...

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 heal...

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

BACKGROUND: Semantic interoperability is essential when carrying out post-genomic clinical trials wh...

Design of an extensive information representation scheme for clinical narratives.

BACKGROUND: Knowledge representation frameworks are essential to the understanding of complex biomed...

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. D...

Word2Vec inversion and traditional text classifiers for phenotyping lupus.

BACKGROUND: Identifying patients with certain clinical criteria based on manual chart review of doct...

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 electro...

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 da...

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 a...

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,...

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

BACKGROUND: Automated methods for identifying clinically relevant new versus redundant information i...

Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization.

With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent di...

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 al...

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

BACKGROUND: Ontologies and controlled terminologies have become increasingly important in biomedical...

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 patien...

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 amou...

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 tubercu...

EHR-based phenotyping: Bulk learning and evaluation.

In data-driven phenotyping, a core computational task is to identify medical concepts and their vari...

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