A Generative Approach for Semantic Auditing of Electronic Health Records | Paper Citation Record

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This paper cites Perspectives for medical informatics.

A Generative Approach for Semantic Auditing of Electronic Health Records Perspectives for medical informatics

Reference 1

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Observation 08233b04-84ec-4d35-bc97-7ea613cf599f · outbound

This paper cites Validity of The Health Improvement Network (THIN) for the study of psoriasis.

A Generative Approach for Semantic Auditing of Electronic Health Records Validity of The Health Improvement Network (THIN) for the study of psoriasis

Reference 2

Resolution: verified exact; raw_fallback, observed 2026-08-06T20:30:49.573907Z

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Observation 5f817fb8-3e12-4715-8160-1cef7bf9763d · outbound

This paper cites Electronic health records: new opportunities for clinical research.

A Generative Approach for Semantic Auditing of Electronic Health Records Electronic health records: new opportunities for clinical research

Reference 3

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Observation 890c0df2-34ab-4046-a6a5-8d5768989d48 · outbound

This paper cites Research data warehouse: using electronic health records to conduct population- based observational studies.

A Generative Approach for Semantic Auditing of Electronic Health Records Research data warehouse: using electronic health records to conduct population- based observational studies

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:30:49.798558Z

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This paper cites Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care: A Systematic Review.

A Generative Approach for Semantic Auditing of Electronic Health Records Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care: A Systematic Review

Reference 5

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This paper cites Use of Artificial Intelligence in the Search for New Information Through Routine Laboratory Tests: Systematic Review.

A Generative Approach for Semantic Auditing of Electronic Health Records Use of Artificial Intelligence in the Search for New Information Through Routine Laboratory Tests: Systematic Review

Reference 6

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Observation 049304b7-8f56-436c-93c0-ac3e65fe2145 · outbound

This paper cites Artificial Intelligence Algo- rithm for Subclinical Breast Cancer Detection.

A Generative Approach for Semantic Auditing of Electronic Health Records Artificial Intelligence Algo- rithm for Subclinical Breast Cancer Detection

Reference 7

Resolution: verified exact; raw_fallback, observed 2026-08-06T20:30:49.423353Z

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Observation 71b9e656-d19f-4906-be0c-29df2195f42e · outbound

This paper cites Digital Health Data Quality Issues: Systematic Review.

A Generative Approach for Semantic Auditing of Electronic Health Records Digital Health Data Quality Issues: Systematic Review

Reference 8

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Observation 4a6f7f06-f700-4eb7-85dd-8ac518768141 · outbound

This paper cites Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research.

A Generative Approach for Semantic Auditing of Electronic Health Records Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:30:49.789140Z

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This paper cites Everyone wants to do the model work, not the data work.

A Generative Approach for Semantic Auditing of Electronic Health Records Everyone wants to do the model work, not the data work

Reference 10

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Observation 7d15364d-e49b-49eb-b814-da04a9827367 · outbound

This paper cites Garbage in–garbage out.

A Generative Approach for Semantic Auditing of Electronic Health Records Garbage in–garbage out

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:30:49.779931Z

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Observation 55fd7ff8-5045-406d-90ee-94c4b4d71a2c · outbound

This paper cites Transparent reporting of data quality in distributed data networks.

A Generative Approach for Semantic Auditing of Electronic Health Records Transparent reporting of data quality in distributed data networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:30:49.771677Z

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Observation 3f9c22f4-7cd0-49e5-b05b-12bafb8ab1b7 · outbound

This paper cites Fundamentals of quality control and improvement.

A Generative Approach for Semantic Auditing of Electronic Health Records Fundamentals of quality control and improvement

Reference 13

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This paper cites The economics of unit testing.

A Generative Approach for Semantic Auditing of Electronic Health Records The economics of unit testing

Reference 14

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This paper cites Continuous Integration, Delivery and Deployment: A Systematic Review on Approaches, Tools, Challenges and Practices.

A Generative Approach for Semantic Auditing of Electronic Health Records Continuous Integration, Delivery and Deployment: A Systematic Review on Approaches, Tools, Challenges and Practices

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:30:49.747778Z

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This paper cites Veracity in big data: How good is good enough.

A Generative Approach for Semantic Auditing of Electronic Health Records Veracity in big data: How good is good enough

Reference 16

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This paper cites Electronic health record data quality assessment and tools: a systematic re- view.

A Generative Approach for Semantic Auditing of Electronic Health Records Electronic health record data quality assessment and tools: a systematic re- view

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:30:49.731326Z

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Observation 377bc88c-d54c-4efb-b93e-ae820784057c · outbound

This paper cites Large language models for data extraction from unstructured and semi- structured electronic health records: a multiple model performance evaluation.

A Generative Approach for Semantic Auditing of Electronic Health Records Large language models for data extraction from unstructured and semi- structured electronic health records: a multiple model performance evaluation

Reference 18

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This paper cites Zero-shot interpretable phenotyping of postpartum hemorrhage using large language models.

A Generative Approach for Semantic Auditing of Electronic Health Records Zero-shot interpretable phenotyping of postpartum hemorrhage using large language models

Reference 19

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Observation 3bf298d1-bee2-4fd2-a2c7-05f578e8475a · outbound

This paper cites Enhancing phenotype recognition in clinical notes using large language models: PhenoBCBERT and PhenoGPT.

A Generative Approach for Semantic Auditing of Electronic Health Records Enhancing phenotype recognition in clinical notes using large language models: PhenoBCBERT and PhenoGPT

Reference 20

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Observation a92341ef-2be2-4395-8d4c-a7e16ae57f7c · outbound

This paper cites Towards automated phenotype definition extraction using large language models.

A Generative Approach for Semantic Auditing of Electronic Health Records Towards automated phenotype definition extraction using large language models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:30:49.723439Z

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Observation 7b59f2c2-3592-4733-94ed-00ceae6318df · outbound

This paper cites Utility of Large Language Models for Concept Set Curation.

A Generative Approach for Semantic Auditing of Electronic Health Records Utility of Large Language Models for Concept Set Curation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:30:49.715213Z

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Observation a694c0d2-5a81-4ca8-afd5-42e2d16a22ff · outbound

This paper cites GenSpectrum Chat: Data Exploration in Public Health Using Large Language Models.

A Generative Approach for Semantic Auditing of Electronic Health Records GenSpectrum Chat: Data Exploration in Public Health Using Large Language Models

Reference 23

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This paper cites Evaluation of GPT-4 for 10-year cardiovascular risk prediction: Insights from the UK Biobank and KoGES data.

A Generative Approach for Semantic Auditing of Electronic Health Records Evaluation of GPT-4 for 10-year cardiovascular risk prediction: Insights from the UK Biobank and KoGES data

Reference 24

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This paper cites Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events.

A Generative Approach for Semantic Auditing of Electronic Health Records Event Stream GPT: A Data Pre-processing and Modeling Library for Generative, Pre-trained Transformers over Continuous-time Sequences of Complex Events

Reference 25

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This paper cites Can Foundation Models Wrangle Your Data?.

A Generative Approach for Semantic Auditing of Electronic Health Records Can Foundation Models Wrangle Your Data?

Reference 26

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Observation 2591c501-dbd7-49e2-a5b7-4c282f99d679 · outbound

This paper cites Represen- tation of race and ethnicity in the contemporary US health cohort all of US research program.

A Generative Approach for Semantic Auditing of Electronic Health Records Represen- tation of race and ethnicity in the contemporary US health cohort all of US research program

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:30:49.707365Z

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A Generative Approach for Semantic Auditing of Electronic Health Records Unresolved cited work

Reference 28

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A Generative Approach for Semantic Auditing of Electronic Health Records Unresolved cited work

Reference 29

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Observation ffa8c5b5-7617-4734-a9fc-b60e8adbdbf9 · outbound

This paper cites Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations.

A Generative Approach for Semantic Auditing of Electronic Health Records Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations

Reference 30

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A Generative Approach for Semantic Auditing of Electronic Health Records Structured programming

Reference 31

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Observation d1cb9286-c975-4c2d-881b-e416742455b3 · outbound

This paper cites The implied truth effect: Attaching warn- ings to a subset of fake news headlines increases perceived accuracy of headlines without warnings.

A Generative Approach for Semantic Auditing of Electronic Health Records The implied truth effect: Attaching warn- ings to a subset of fake news headlines increases perceived accuracy of headlines without warnings

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:30:49.672304Z

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This paper cites Code coverage and test suite effectiveness: Empirical study with real bugs in large systems.

A Generative Approach for Semantic Auditing of Electronic Health Records Code coverage and test suite effectiveness: Empirical study with real bugs in large systems

Reference 33

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This paper cites Self-refine: Iterative refinement with self-feedback.

A Generative Approach for Semantic Auditing of Electronic Health Records Self-refine: Iterative refinement with self-feedback

Reference 34

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This paper cites Statistical significance versus clinical relevance.

A Generative Approach for Semantic Auditing of Electronic Health Records Statistical significance versus clinical relevance

Reference 35

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This paper cites Comparing Prevalence Estimates From Population-Based Surveys to Inform Surveil- lance Using Electronic Health Records.

A Generative Approach for Semantic Auditing of Electronic Health Records Comparing Prevalence Estimates From Population-Based Surveys to Inform Surveil- lance Using Electronic Health Records

Reference 36

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This paper cites MIMIC-III, a freely accessible critical care database.

A Generative Approach for Semantic Auditing of Electronic Health Records MIMIC-III, a freely accessible critical care database

Reference 37

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This paper cites Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record.

A Generative Approach for Semantic Auditing of Electronic Health Records Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:30:49.654643Z

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This paper cites GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models.

A Generative Approach for Semantic Auditing of Electronic Health Records GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

Reference 39

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A Generative Approach for Semantic Auditing of Electronic Health Records Unresolved cited work

Reference 40

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A Generative Approach for Semantic Auditing of Electronic Health Records Unresolved cited work

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Observation 0d419b0c-9232-4ba4-8219-2f38bf672e64 · outbound

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A Generative Approach for Semantic Auditing of Electronic Health Records Unresolved cited work

Reference 43

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A Generative Approach for Semantic Auditing of Electronic Health Records use a vertical line | as separator

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:30:49.610865Z

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A Generative Approach for Semantic Auditing of Electronic Health Records use a vertical line | as separator

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:30:49.592612Z

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Observation a97199a4-8f8c-4a2d-a064-a6d5f6288a6e · outbound

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Reference 53

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Observation e76bce1d-10e0-4463-8d0f-fccc1f00db45 · outbound

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A Generative Approach for Semantic Auditing of Electronic Health Records Unresolved cited work

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Observation e692bcb5-2a2e-4863-98ca-c374adbb8fcf · outbound

This paper cites an unresolved cited work.

A Generative Approach for Semantic Auditing of Electronic Health Records Unresolved cited work

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Resolution: unresolved; raw_fallback, observed 2026-08-06T20:30:49.628954Z

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A Generative Approach for Semantic Auditing of Electronic Health Records Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-06T20:30:49.620416Z

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A Generative Approach for Semantic Auditing of Electronic Health Records use a vertical line | as separator

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T20:30:49.582878Z

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Pith citing papers