Dataset Information

Thyroid Ultrasound Appropriateness Identification Through Natural Language Processing of Electronic Health Records.

ABSTRACT:

Objective

To address thyroid cancer overdiagnosis, we aim to develop a natural language processing (NLP) algorithm to determine the appropriateness of thyroid ultrasounds (TUS).

Patients and methods

Between 2017 and 2021, we identified 18,000 TUS patients at Mayo Clinic and selected 628 for chart review to create a ground truth dataset based on consensus. We developed a rule-based NLP pipeline to identify TUS as appropriate TUS (aTUS) or inappropriate TUS (iTUS) using patients' clinical notes and additional meta information. In addition, we designed an abbreviated NLP pipeline (aNLP) solely focusing on labels from TUS order requisitions to facilitate deployment at other health care systems. Our dataset was split into a training set of 468 (75%) and a test set of 160 (25%), using the former for rule development and the latter for performance evaluation.

Results

There were 449 (95.9%) patients identified as aTUS and 19 (4.06%) as iTUS in the training set; there are 155 (96.88%) patients identified as aTUS and 5 (3.12%) were iTUS in the test set. In the training set, the pipeline achieved a sensitivity of 0.99, specificity of 0.95, and positive predictive value of 1.0 for detecting aTUS. The testing cohort revealed a sensitivity of 0.96, specificity of 0.80, and positive predictive value of 0.99. Similar performance metrics were observed in the aNLP pipeline.

Conclusion

The NLP models can accurately identify the appropriateness of a thyroid ultrasound from clinical documentation and order requisition information, a critical initial step toward evaluating the drivers and outcomes of TUS use and subsequent thyroid cancer overdiagnosis.

SUBMITTER: Jacome CS

PROVIDER: S-EPMC10947349 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Publications

Thyroid Ultrasound Appropriateness Identification Through Natural Language Processing of Electronic Health Records.

Jacome Cristian Soto CS Torres Danny Segura DS Fan Jungwei W JW Loor-Torres Ricardo R Duran Mayra M Zahidy Misk Al MA Cabezas Esteban E Borras-Osorio Mariana M Toro-Tobon David D Wu Yuqi Y Wu Yonghui Y Ospina Naykky Singh NS Brito Juan P JP

Mayo Clinic proceedings. Digital health 20240201 1

<h4>Objective</h4>To address thyroid cancer overdiagnosis, we aim to develop a natural language processing (NLP) algorithm to determine the appropriateness of thyroid ultrasounds (TUS).<h4>Patients and methods</h4>Between 2017 and 2021, we identified 18,000 TUS patients at Mayo Clinic and selected 628 for chart review to create a ground truth dataset based on consensus. We developed a rule-based NLP pipeline to identify TUS as appropriate TUS (aTUS) or inappropriate TUS (iTUS) using patients' cl ...[more]

PMID: 38501072

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Dataset Information

Thyroid Ultrasound Appropriateness Identification Through Natural Language Processing of Electronic Health Records.

Objective

Patients and methods

Results

Conclusion

Publications

Thyroid Ultrasound Appropriateness Identification Through Natural Language Processing of Electronic Health Records.

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