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Enhancing Health Data Interoperability with Large Language Models: Conclusion & References

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This paper is available on arxiv under CC 4.0 license.

Authors:

(1) Yikuan Li, MS, Northwestern University Feinberg School of Medicine & Siemens Medical Solutions;

(2) Hanyin Wang, BMed, Northwestern University Feinberg School of Medicine;

(3) Halid Z. Yerebakan, PhD, Siemens Medical Solutions;

(4) Yoshihisa Shinagawa, PhD, Siemens Medical Solutions;

(5) Yuan Luo, PhD, FAMIA, Northwestern University Feinberg School of Medicine.

Table of Links

Introduction

Methods

Results and Discussions

Conclusion & References

Conclusion

In this study, we provided the foundations of leveraging LLMs to enhance health data interoperability by transforming free-text input into the FHIR resources. Future studies will aim to build upon these successes by extending the generation to additional FHIR resources and comparing the performance of various LLM models.

Reference

1. Hong N, Wen A, Shen F, Sohn S, Liu S, Liu H, Jiang G. Integrating structured and unstructured EHR data using an FHIR-based type system: a case study with medication data. AMIA Summits on Translational Science Proceedings. 2018;2018:74.


2. Hong N, Wen A, Shen F, Sohn S, Wang C, Liu H, Jiang G. Developing a scalable FHIR-based clinical data normalization pipeline for standardizing and integrating unstructured and structured electronic health record data. JAMIA open. 2019 Dec;2(4):570-9.


3. Johnson AE, Pollard TJ, Shen L, Lehman LW, Feng M, Ghassemi M, Moody B, Szolovits P, Anthony Celi L, Mark RG. MIMIC-III, a freely accessible critical care database. Scientific data. 2016 May 24;3(1):1-9.


4. Henry S, Buchan K, Filannino M, Stubbs A, Uzuner O. 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records. Journal of the American Medical Informatics Association. 2020 Jan;27(1):3-12.

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