AIMC Topic: Natural Language Processing

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Natural language processing and String Metric-assisted Assessment of Semantic Heterogeneity method for capturing and standardizing unstructured nursing activities in a hospital setting: a retrospective study.

Annali di igiene : medicina preventiva e di comunita
BACKGROUND: Nurses record data in electronic health records (EHRs) using different terminologies and coding systems. The purpose of this study was to identify unstructured free-text nursing activities recorded by nurses in EHRs with natural language ...

Can We Geographically Validate a Natural Language Processing Algorithm for Automated Detection of Incidental Durotomy Across Three Independent Cohorts From Two Continents?

Clinical orthopaedics and related research
BACKGROUND: Incidental durotomy is an intraoperative complication in spine surgery that can lead to postoperative complications, increased length of stay, and higher healthcare costs. Natural language processing (NLP) is an artificial intelligence me...

Effects of data and entity ablation on multitask learning models for biomedical entity recognition.

Journal of biomedical informatics
MOTIVATION: Training domain-specific named entity recognition (NER) models requires high quality hand curated gold standard datasets which are time-consuming and expensive to create. Furthermore, the storage and memory required to deploy NLP models c...

Towards a robust out-of-the-box neural network model for genomic data.

BMC bioinformatics
BACKGROUND: The accurate prediction of biological features from genomic data is paramount for precision medicine and sustainable agriculture. For decades, neural network models have been widely popular in fields like computer vision, astrophysics and...

E-scooter related injuries: Using natural language processing to rapidly search 36 million medical notes.

PloS one
BACKGROUND: Shareable e-scooters have become popular, but injuries to riders and bystanders have not been well characterized. The goal of this study was to describe e-scooter injuries and estimate the rate of injury per e-scooter trip.

When BERT meets Bilbo: a learning curve analysis of pretrained language model on disease classification.

BMC medical informatics and decision making
BACKGROUND: Natural language processing (NLP) tasks in the health domain often deal with limited amount of labeled data due to high annotation costs and naturally rare observations. To compensate for the lack of training data, health NLP researchers ...

Quantitative Patient-Reported Experience Measures Derived From Natural Language Processing Have a Normal Distribution and No Ceiling Effect.

Quality management in health care
BACKGROUND AND OBJECTIVES: Patient-reported experience measures have the potential to guide improvement in health care delivery. Many patient-reported experience measures are limited by the presence of strong ceiling effects that limit their analytic...

Visualization of medical concepts represented using word embeddings: a scoping review.

BMC medical informatics and decision making
BACKGROUND: Analyzing the unstructured textual data contained in electronic health records (EHRs) has always been a challenging task. Word embedding methods have become an essential foundation for neural network-based approaches in natural language p...

Information Extraction From Electronic Health Records to Predict Readmission Following Acute Myocardial Infarction: Does Natural Language Processing Using Clinical Notes Improve Prediction of Readmission?

Journal of the American Heart Association
Background Social risk factors influence rehospitalization rates yet are challenging to incorporate into prediction models. Integration of social risk factors using natural language processing (NLP) and machine learning could improve risk prediction ...

Word-level text highlighting of medical texts for telehealth services.

Artificial intelligence in medicine
The medical domain is often subject to information overload. The digitization of healthcare, constant updates to online medical repositories, and increasing availability of biomedical datasets make it challenging to effectively analyze the data. This...