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MTERMS is a natural language processing (NLP) ecosystem (including subsequent applications such as Deep Snow) for biomedical text. Originally designed to extract medication information from clinical notes to facilitate real-time medication reconciliation, MTERMS (branded as the Medical Text Extraction, Reasoning and Mapping System) has been extended to support a variety of clinical informatics applications and projects. The MTERMS ecosystem utilizes a mix of linguistics focused methods, unsupervised methods (e.g., clustering), and supervised statistical focused methods (e.g., machine learning, deep learning, active learning). These applications include:

  • Clinical Documentation
    • Automatic error checking of various¬† free-text clinical documents, including misspellings in typed notes and “real-word” errors in dictated notes
    • Mining similar notes to help generate note templates
  • Information Extraction/Retrieval and Encoding
    • Extracting diverse clinical information (e.g., diagnoses, symptoms, medications, social-behavioral information)¬† from free-text clinical documents
    • Encoding extracted information using standard medical terminologies (e.g., SNOMED-CT, ICD-9/10, RxNorm)
    • Handling contextual information (e.g., sections, negations, status/changes)
    • Generating output in standard (e.g., HL7 document standards) or custom formats
    • De-identification of clinical notes
  • Data Mining and Knowledge Discovery
    • Documentation classification, e.g., classifying malpractice claim descriptions according to allegation, contributing factors, severity, and responsible service
    • Clustering, e.g., discovering food allergen cross-sensitivity
    • Relation identification, e.g., temporal reasoning over medical events
    • Distributional semantic analysis, e.g., topic modeling to generate themes from social media posts and clinical documents
    • Active learning, e.g., using word embeddings to assist information extraction tasks
  • Clinical Decision Support
    • Creating an application to facilitate clinical information reconciliation in the electronic health record
    • Developing a prototype application for care coordination
    • Developing a system for automatic malpractice case encoding and auditing
    • Developing predictive models for identifying patients for early palliative care interventions
    • Identifying patient cohorts (e.g., patients with heart failure)
  • Research, Innovation and others
    • Developing applications to identify adverse drug events (e.g., adverse drug reactions to opioids)
    • Participating in community shared tasks (e.g., classifying psychiatric symptom severity for the 2016 i2b2 NLP Shared Task)
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