Information Technology

Application of electronic trigger tools to identify targets for improving diagnostic safety.

Progress in reducing diagnostic errors remains slow partly due to poorly defined methods to identify errors, high-risk situations, and adverse events. Electronic trigger (e-trigger) tools, which mine vast amounts of patient data to identify signals indicative of a likely error or adverse event, offer a promising method to efficiently identify errors. The increasing amounts of longitudinal electron...

Predicting of anaphylaxis in big data EMR by exploring machine learning approaches.

Anaphylaxis is a life-threatening allergic reaction that occurs suddenly after contact with an allergen. Epidemiological studies about anaphylaxis are very important in planning and evaluating new strategies that prevent this reaction, but also in providing a guide to the treatment of patients who have just suffered an anaphylactic reaction. Electronic Medical Records (EMR) are one of the most eff...

One-pass-throw-away learning for cybersecurity in streaming non-stationary environments by dynamic stratum network.

Throughout recent times, cybersecurity problems have occurred in various business applications. Although previous researchers proposed to cope with th...

Predicting hospital readmission for lupus patients: An RNN-LSTM-based deep-learning methodology.

Hospital readmission is one of the critical metrics used for measuring the performance of hospitals. The HITECH Act imposes penalties when patients ar...

Machine learning models in electronic health records can outperform conventional survival models for predicting patient mortality in coronary artery disease.

Prognostic modelling is important in clinical practice and epidemiology for patient management and research. Electronic health records (EHR) provide l...

Methodological variations in lagged regression for detecting physiologic drug effects in EHR data.

We studied how lagged linear regression can be used to detect the physiologic effects of drugs from data in the electronic health record (EHR). We sys...

Disease Specific Ontology of Adverse Events: Ontology extension and adaptation for Chronic Kidney Disease.

BACKGROUND: Adverse Event (AE) ontology can be used to support interoperability and computer-assisted reasoning of AEs. Despite significant progress i...

Automated ontology generation framework powered by linked biomedical ontologies for disease-drug domain.

OBJECTIVE AND BACKGROUND: The exponential growth of the unstructured data available in biomedical literature, and Electronic Health Record (EHR), requ...

Risk prediction using natural language processing of electronic mental health records in an inpatient forensic psychiatry setting.

OBJECTIVE: Instruments rating risk of harm to self and others are widely used in inpatient forensic psychiatry settings. A potential alternate or supp...

Conditional random fields for clinical named entity recognition: A comparative study using Korean clinical texts.

BACKGROUND: This study demonstrates clinical named entity recognition (NER) methods on the clinical texts of rheumatism patients in South Korea. Despi...

Telemedicine and telerobotics: from science fiction to reality.

Advances in communication technologies have paved the way for telemedicine to transform the delivery of medical care throughout the world. Coinciding ...

Discovering and identifying New York heart association classification from electronic health records.

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

Natural Language Processing and Its Implications for the Future of Medication Safety: A Narrative Review of Recent Advances and Challenges.

The safety of medication use has been a priority in the United States since the late 1930s. Recently, it has gained prominence due to the increasing a...

Natural Language Processing for EHR-Based Computational Phenotyping.

This article reviews recent advances in applying natural language processing (NLP) to Electronic Health Records (EHRs) for computational phenotyping. ...

Prediction task guided representation learning of medical codes in EHR.

There have been rapidly growing applications using machine learning models for predictive analytics in Electronic Health Records (EHR) to improve the ...

A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set.

Recently, recurrent neural networks (RNNs) have been applied in predicting disease onset risks with Electronic Health Record (EHR) data. While these m...

From lexical regularities to axiomatic patterns for the quality assurance of biomedical terminologies and ontologies.

Ontologies and terminologies have been identified as key resources for the achievement of semantic interoperability in biomedical domains. The develop...

Profiling Lung Cancer Patients Using Electronic Health Records.

If Electronic Health Records contain a large amount of information about the patient's condition and response to treatment, which can potentially revo...

Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models.

With the widespread adoption of electronic health records (EHRs), large repositories of structured and unstructured patient data are becoming availabl...

Deep Patient Similarity Learning for Personalized Healthcare.

Predicting patients' risk of developing certain diseases is an important research topic in healthcare. Accurately identifying and ranking the similari...