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Natural Language Processing in Surgery: A Systematic Review and Meta-analysis.

Joseph A Mellia¹, Marten N Basta², Yoshiko Toyoda¹

¹Division of Plastic Surgery, Department of Surgery, University of Pennsylvania. Philadelphia, PA.

Annals of Surgery

|October 19, 2020

Summary

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Natural Language Processing (NLP) enhances surgical outcomes research by improving the identification of postoperative complications from electronic health records. NLP models offer superior accuracy in confirming and ruling out outcomes compared to traditional methods.

Area of Science:

Medical Informatics
Health Services Research
Computational Linguistics

Background:

Electronic health records (EHRs) generate vast patient data, but traditional analysis methods are labor-intensive and prone to bias.
Extracting meaningful surgical outcomes from unstructured EHR data presents significant challenges.

Purpose of the Study:

To systematically evaluate the application and benefits of Natural Language Processing (NLP) in surgical outcomes research.
To compare the performance of NLP models against traditional methods for identifying surgical outcomes.

Main Methods:

A comprehensive literature search was conducted across major databases (PubMed, MEDLINE, Web of Science, Embase) for studies from 2000 onwards.
Included studies utilized NLP models to assess perioperative surgical outcomes, with evaluation metrics assessed via pooled analysis and meta-analysis.

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Qualitative synthesis was employed to analyze results and assess the risk of bias.

Main Results:

The review included 29 articles, with a notable increase in publications after 2018.
NLP models demonstrated higher sensitivity in identifying postoperative complications (0.92) compared to non-NLP methods (0.58).
While specificities were comparable, NLP models proved more reliable for both confirming and ruling out documented surgical outcomes.

Conclusions:

Natural Language Processing (NLP) application in surgical outcomes research, especially for postoperative complications, is rapidly expanding.
NLP offers comparable performance to traditional methods but excels in ruling out the documentation of surgical outcomes, enhancing data reliability.