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An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP

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Natural Language Processing Versus Diagnosis Code-Based Methods for Postherpetic Neuralgia Identification: Algorithm

Chengyi Zheng¹, Bradley Ackerson², Sijia Qiu¹

¹Department of Research & Evaluation, Kaiser Permanente Southern California, 100 S Los Robles Ave, 2nd Floor, Pasadena, CA, 91101, United States, 1 626-986-8665, 1 626-564-7872.

JMIR Medical Informatics

|September 10, 2024

Summary

This summary is machine-generated.

A new natural language processing (NLP) algorithm accurately identifies postherpetic neuralgia (PHN) from electronic health records. This automated method offers a more efficient alternative to manual chart review for PHN research.

Keywords:

EHR EHR data algorithm artificial intelligence development diagnosis electronic health record herpes zoster natural language processing neuralgia postherpetic neuralgia real-world data recombinant zoster vaccine sensitivity specificity validation validation data

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Published on: September 13, 2024

Area of Science:

Medical Informatics
Computational Linguistics
Epidemiology

Background:

Postherpetic neuralgia (PHN) is a painful complication of herpes zoster (HZ).
Current methods for identifying PHN in electronic health records (EHRs) using diagnosis codes and prescription data have questionable accuracy.
Manual chart review is accurate but costly and time-consuming.

Purpose of the Study:

To develop and validate a natural language processing (NLP) algorithm for automated PHN identification from unstructured EHR data.
To compare the performance of the NLP algorithm against traditional code-based methods.

Main Methods:

A retrospective study utilizing EHR data from Kaiser Permanente Southern California.
Development and validation datasets were randomly sampled from patients aged ≥50 years with incident HZ and antiviral prescriptions.
Performance metrics including sensitivity, specificity, PPV, NPV, F-score, and MCC were evaluated against manual chart review as the reference standard.

Main Results:

The NLP algorithm demonstrated high accuracy, achieving 90.9% sensitivity, 98.5% specificity, 82% PPV, and 99.3% NPV.
NLP algorithm's composite scores were 0.89 (F-score) and 0.85 (MCC).
Code-based methods showed significantly lower performance, with F-scores and MCCs ranging from 0.45-0.59 and 0.32-0.61, respectively.

Conclusions:

An automated NLP-based approach accurately identifies PHN cases from EHRs.
This NLP method presents a viable and efficient tool for population-based PHN research.