AIMC Topic: Electronic Health Records

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Mapping Patient Trajectories using Longitudinal Extraction and Deep Learning in the MIMIC-III Critical Care Database.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Electronic Health Records (EHRs) contain a wealth of patient data useful to biomedical researchers. At present, both the extraction of data and methods for analyses are frequently designed to work with a single snapshot of a patient's record. Health ...

Population-Based Analysis of Histologically Confirmed Melanocytic Proliferations Using Natural Language Processing.

JAMA dermatology
IMPORTANCE: Population-based information on the distribution of histologic diagnoses associated with skin biopsies is unknown. Electronic medical records (EMRs) enable automated extraction of pathology report data to improve our epidemiologic underst...

Electronic Health Record Phenotypes for Precision Medicine: Perspectives and Caveats From Treatment of Breast Cancer at a Single Institution.

Clinical and translational science
Precision medicine is at the forefront of biomedical research. Cancer registries provide rich perspectives and electronic health records (EHRs) are commonly utilized to gather additional clinical data elements needed for translational research. Howev...

Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes.

Journal of the American Medical Informatics Association : JAMIA
We propose Segment Convolutional Neural Networks (Seg-CNNs) for classifying relations from clinical notes. Seg-CNNs use only word-embedding features without manual feature engineering. Unlike typical CNN models, relations between 2 concepts are ident...

NLPReViz: an interactive tool for natural language processing on clinical text.

Journal of the American Medical Informatics Association : JAMIA
The gap between domain experts and natural language processing expertise is a barrier to extracting understanding from clinical text. We describe a prototype tool for interactive review and revision of natural language processing models of binary con...

DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records.

Cancer research
Precise phenotype information is needed to understand the effects of genetic and epigenetic changes on tumor behavior and responsiveness. Extraction and representation of cancer phenotypes is currently mostly performed manually, making it difficult t...

Natural Language Processing for EHR-Based Pharmacovigilance: A Structured Review.

Drug safety
The goal of pharmacovigilance is to detect, monitor, characterize and prevent adverse drug events (ADEs) with pharmaceutical products. This article is a comprehensive structured review of recent advances in applying natural language processing (NLP) ...

The use of natural language processing on pediatric diagnostic radiology reports in the electronic health record to identify deep venous thrombosis in children.

Journal of thrombosis and thrombolysis
Venous thromboembolism (VTE) is a potentially life-threatening condition that includes both deep vein thrombosis (DVT) and pulmonary embolism. We sought to improve detection and reporting of children with a new diagnosis of VTE by applying natural la...

Enhancing Risk Assessment in Patients Receiving Chronic Opioid Analgesic Therapy Using Natural Language Processing.

Pain medicine (Malden, Mass.)
OBJECTIVES: Clinical guidelines for the use of opioids in chronic noncancer pain recommend assessing risk for aberrant drug-related behaviors prior to initiating opioid therapy. Despite recent dramatic increases in prescription opioid misuse and abus...