AIMC Topic: Electronic Health Records

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Applying deep neural networks to unstructured text notes in electronic medical records for phenotyping youth depression.

Evidence-based mental health
BACKGROUND: We report a study of machine learning applied to the phenotyping of psychiatric diagnosis for research recruitment in youth depression, conducted with 861 labelled electronic medical records (EMRs) documents. A model was built that could ...

Learning a Health Knowledge Graph from Electronic Medical Records.

Scientific reports
Demand for clinical decision support systems in medicine and self-diagnostic symptom checkers has substantially increased in recent years. Existing platforms rely on knowledge bases manually compiled through a labor-intensive process or automatically...

The Economic Burden of ACPA-Positive Status Among Patients with Rheumatoid Arthritis.

Journal of managed care & specialty pharmacy
BACKGROUND: Anticitrullinated protein antibodies (ACPAs) are serological biomarkers associated with early, rapidly progressing rheumatoid arthritis (RA), including more severe disease and joint damage. ACPA testing has become a routine tool for RA di...

Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review.

Journal of biomedical informatics
We followed a systematic approach based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses to identify existing clinical natural language processing (NLP) systems that generate structured information from unstructured free text...

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

PloS one
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 patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proacti...

Recurrent neural networks for classifying relations in clinical notes.

Journal of biomedical informatics
We proposed the first models based on recurrent neural networks (more specifically Long Short-Term Memory - LSTM) for classifying relations from clinical notes. We tested our models on the i2b2/VA relation classification challenge dataset. We showed ...

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

PloS one
BACKGROUND: Type 2 diabetes mellitus (T2DM), with increased risk of serious long-term complications, currently represents 8.3% of the adult population. We hypothesized that a critical transition state prior to the new onset T2DM can be revealed throu...

Entity recognition from clinical texts via recurrent neural network.

BMC medical informatics and decision making
BACKGROUND: Entity recognition is one of the most primary steps for text analysis and has long attracted considerable attention from researchers. In the clinical domain, various types of entities, such as clinical entities and protected health inform...

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

BMC medical informatics and decision making
BACKGROUND: Automated methods for identifying clinically relevant new versus redundant information in electronic health record (EHR) clinical notes is useful for clinicians and researchers involved in patient care and clinical research, respectively....

A Novel Approach towards Medical Entity Recognition in Chinese Clinical Text.

Journal of healthcare engineering
Medical entity recognition, a basic task in the language processing of clinical data, has been extensively studied in analyzing admission notes in alphabetic languages such as English. However, much less work has been done on nonstructural texts that...