AIMC Topic: Natural Language Processing

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Can natural language processing provide accurate, automated reporting of wound infection requiring reoperation after lumbar discectomy?

The spine journal : official journal of the North American Spine Society
BACKGROUND: Surgical site infections are a major driver of morbidity and increased costs in the postoperative period after spine surgery. Current tools for surveillance of these adverse events rely on prospective clinical tracking, manual retrospecti...

Relation Extraction from Clinical Narratives Using Pre-trained Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Natural language processing (NLP) is useful for extracting information from clinical narratives, and both traditional machine learning methods and more-recent deep learning methods have been successful in various clinical NLP tasks. These methods oft...

Learning Inter-Sentence, Disorder-Centric, Biomedical Relationships from Medical Literature.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Relationships between disorders and their associated tests, treatments and symptoms underpin essential information needs of clinicians and can support biomedical knowledge bases, information retrieval and ultimately clinical decision support. These r...

Using FHIR to Construct a Corpus of Clinical Questions Annotated with Logical Forms and Answers.

AMIA ... Annual Symposium proceedings. AMIA Symposium
This paper describes a novel technique for annotating logical forms and answers for clinical questions by utilizing Fast Healthcare Interoperability Resources (FHIR). Such annotations are widely used in building the semantic parsing models (which aim...

Transfer Learning from BERT to Support Insertion of New Concepts into SNOMED CT.

AMIA ... Annual Symposium proceedings. AMIA Symposium
With advances in Machine Learning (ML), neural network-based methods, such as Convolutional/Recurrent Neural Networks, have been proposed to assist terminology curators in the development and maintenance of terminologies. Bidirectional Encoder Repres...

Leveraging Contextual Information in Extracting Long Distance Relations from Clinical Notes.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Relation extraction from biomedical text is important for clinical decision support applications. In post-marketing pharmacovigilance, for example, Adverse Drug Events (ADE) relate medical problems to the drugs that caused them and were the focus of ...

Achievability to Extract Specific Date Information for Cancer Research.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Accurate identification of temporal information such as date is crucial for advancing cancer research which often requires precise date information associated with related cancer events. However, there is a gap for existing natural language processin...

De-identification of Clinical Text via Bi-LSTM-CRF with Neural Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium
De-identification of clinical text, the prerequisite of electronic clinical data reuse, is a typical named entity recogni tion (NER) problem. A number of state-of-the-art deep learning methods for NER, such as Bi-LSTM-CRF (bidirec tional long-short-t...

Using Natural Language Processing to improve EHR Structured Data-based Surgical Site Infection Surveillance.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Surgical Site Infection surveillance in healthcare systems is labor intensive and plagued by underreporting as current methodology relies heavily on manual chart review. The rapid adoption of electronic health records (EHRs) has the potential to allo...

Clinical Tractor: A Framework for Automatic Natural Language Understanding of Clinical Practice Guidelines.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Computational representations of the semantic knowledge embedded within clinical practice guidelines (CPGs) may be a significant aid in creating computer interpretable guidelines (CIGs). Formalizing plain text CPGs into CIGs manually is a laborious a...