AIMC Topic: Natural Language Processing

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Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules.

Journal of gastroenterology and hepatology
BACKGROUND AND AIM: Guidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time...

Using natural language processing to explore heterogeneity in moral terminology in palliative care consultations.

BMC palliative care
BACKGROUND: High quality serious illness communication requires good understanding of patients' values and beliefs for their treatment at end of life. Natural Language Processing (NLP) offers a reliable and scalable method for measuring and analyzing...

AI Accelerated Human-in-the-loop Structuring of Radiology Reports.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Rule-based Natural Language Processing (NLP) pipelines depend on robust domain knowledge. Given the long tail of important terminology in radiology reports, it is not uncommon for standard approaches to miss items critical for understanding the image...

A Preliminary Characterization of Canonicalized and Non-Canonicalized Section Headers Across Variable Clinical Note Types.

AMIA ... Annual Symposium proceedings. AMIA Symposium
In the electronic health record, the majority of clinically relevant information is stored within clinical notes. Most clinical notes follow a set organizational structure composed of canonicalized section headers that facilitate clinical review and ...

AllergyMap: An Open Source Corpus of Allergy Mention Normalizations.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Allergy mention normalization is challenging because of the wide range of possible allergens including medications, foods, plants, animals, and consumer products. This paper describes the process of mapping free-text allergy information from an elect...

Patient Cohort Retrieval using Transformer Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium
We apply deep learning-based language models to the task of patient cohort retrieval (CR) with the aim to assess their efficacy. The task ofCR requires the extraction of relevant documents from the electronic health records (EHRs) on the basis of a g...

Natural Language Processing and Machine Learning to Enable Clinical Decision Support for Treatment of Pediatric Pneumonia.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Pneumonia is the most frequent cause of infectious disease-related deaths in children worldwide. Clinical decision support (CDS) applications can guide appropriate treatment, but the system must first recognize the appropriate diagnosis. To enable CD...

Automatic MeSH Indexing: Revisiting the Subheading Attachment Problem.

AMIA ... Annual Symposium proceedings. AMIA Symposium
This year less than 200 National Library of Medicine indexers expect to index 1 million articles, and this would not be possible without the assistance of the Medical Text Indexer (MTI) system. MTI is an automated indexing system that provides MeSH m...

Identification of Gout Flares in Chief Complaint Text Using Natural Language Processing.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Many patients with gout flares treated in the Emergency Department (ED) often do not receive optimal continuity of care after an ED visit. Thus, developing methods to identify patients with gout flares in the ED and referring them to appropriate outp...

Facilitating information extraction without annotated data using unsupervised and positive-unlabeled learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Information extraction (IE), the distillation of specific information from unstructured data, is a core task in natural language processing. For rare entities (<1% prevalence), collection of positive examples required to train a model may require an ...