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. 2025 May 22:2024:262-270.
eCollection 2024.

Toward Identifying New Risk Aversions and Subsequent Limitations and Biases When Making De-identified Structured Data Sets Openly Available in a Post-LLM world

Fangyi Chen  1 , Kenrick Cato  2   3 , Gamze Gürsoy  1 , Patricia C Dykes  4   3 , Graham Lowenthal  4 , Sarah Rossetti  1   5
Affiliations

Toward Identifying New Risk Aversions and Subsequent Limitations and Biases When Making De-identified Structured Data Sets Openly Available in a Post-LLM world

Fangyi Chen et al. AMIA Annu Symp Proc. .

Abstract

Making clinical datasets openly available is critical to promote reproducibility and transparency of scientific research. Currently, few datasets are accessible to the public. To support the open science initiative, we plan to release the structured clinical datasets from the CONCERN study. In this paper, we are presenting our de-identification approaches for structured data, considering the future inclusion of de-identified narrative notes and re-identification risks in the LLM era. Through literature review and collaborative consensus sessions, our team made informed decisions regarding dataset release, weighing the pros and cons of each choice, outlining limitation and bias introduced by the de-identification algorithm. To our best knowledge, this is the first study describing the rationales of de-identification decisions in the LLMs era, delineating the consequent problems that should be considered when using our data set. We advocate for transparent disclosure of de-identification decisions and associated limitations and biases with all openly available datasets.

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Figures

Figure 1:
Figure 1:
An overview of the data elements. The final version is subject to change and might differ from the preliminary version depicted above.
See this image and copyright information in PMC

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