Advanced Care Planning Content Encoding with Natural Language Processing.

While advanced care planning (ACP) is an essential practice for ensuring patient-centered care, its adoption remains poor and the completeness of its documentation variable. Natural language processing (NLP) approaches hold promise for supporting ACP, including its use for decision support to improve ACP gaps at the point of care. ACP themes were annotated on palliative care notes across four annotators (Fleiss kappa = 0.

A fast, resource efficient, and reliable rule-based system for COVID-19 symptom identification.

Objective: With COVID-19, there was a need for a rapidly scalable annotation system that facilitated real-time integration with clinical decision support systems (CDS). Current annotation systems suffer from a high-resource utilization and poor scalability limiting real-world integration with CDS. A potential solution to mitigate these issues is to use the rule-based gazetteer developed at our institution.

Natural language processing of prehospital emergency medical services trauma records allows for automated characterization of treatment appropriateness.

Background: Incomplete prehospital trauma care is a significant contributor to preventable deaths. Current databases lack timelines easily constructible of clinical events. Temporal associations and procedural indications are critical to characterize treatment appropriateness.

Named Entity Recognition in Prehospital Trauma Care.

Natural language processing (NLP) methods would improve outcomes in the area of prehospital Emergency Medical Services (EMS) data collection and abstraction. This study evaluated off-the-shelf solutions for automating labelling of clinically relevant data from EMS reports. A qualitative approach for choosing the best possible ensemble of pretrained NLP systems was developed and validated along with a feature using word embeddings to test phrase synonymy.

Recurrent Deep Network Models for Clinical NLP Tasks: Use Case with Sentence Boundary Disambiguation.

Although a number of foundational natural language processing (NLP) tasks like text segmentation are considered a simple problem in the general English domain dominated by well-formed text, complexities of clinical documentation lead to poor performance of existing solutions designed for the general English domain. We present an alternative solution that relies on a convolutional neural network layer followed by a bidirectional long short-term memory layer (CNN-Bi-LSTM) for the task of sentence boundary disambiguation and describe an ensemble approach for domain adaptation using two training corpora. Implementations using the Keras neural-networks API are available at https://github.

NLP-PIER: A Scalable Natural Language Processing, Indexing, and Searching Architecture for Clinical Notes.

Many design considerations must be addressed in order to provide researchers with full text and semantic search of unstructured healthcare data such as clinical notes and reports. Institutions looking at providing this functionality must also address the big data aspects of their unstructured corpora. Because these systems are complex and demand a non-trivial investment, there is an incentive to make the system capable of servicing future needs as well, further complicating the design.