Exploring Named Entity Recognition Potential and the Value of Tailored Natural Language Processing Pipelines for Radiology, Pathology, and Progress Notes in Clinical Decision Support: Quantitative Study.

BACKGROUND

Clinical notes house rich, yet unstructured, patient data, making analysis challenging due to medical jargon, abbreviations, and synonyms causing ambiguity. This complicates real-time extraction for decision support tools.

OBJECTIVE

This study aimed to examine the data curation, technology, and workflow of the named entity recognition (NER) pipeline, a component of a broader clinical decision support tool that identifies key entities using NER models and classifies these entities as present or absent in the patient through an NER assertion model.

METHODS

We gathered progress care, radiology, and pathology notes from 5000 patients, dividing them into 5 batches of 1000 patients each. Metrics such as notes and reports per patient, sentence count, token size, runtime, central processing unit, and memory use were measured per note type. We also evaluated the precision of the NER outputs and then the precision and recall of NER assertion models against manual annotations by a clinical expert.

RESULTS

Using Spark natural language processing clinical pretrained NER models on 138,250 clinical notes, we observed excellent NER precision, with a peak in procedures at 0.989 (95% CI 0.977-1.000) and an accuracy in the assertion model of 0.889 (95% CI 0.856-0.922). Our analysis highlighted long-tail distributions in notes per patient, note length, and entity density. Progress care notes had notably more entities per sentence than radiology and pathology notes, showing 4-fold and 16-fold differences, respectively.

CONCLUSIONS

Further research should explore the analysis of clinical notes beyond the scope of our study, including discharge summaries and psychiatric evaluation notes. Recognizing the unique linguistic characteristics of different note types underscores the importance of developing specialized NER models or natural language processing pipeline setups tailored to each type. By doing so, we can enhance their performance across a more diverse range of clinical scenarios.