On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records | Paper Citation Record

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Implementing the learning health system: from concept to action

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Clarifying the concept of a learning health system for healthcare delivery organizations: Implications from a qualitative analysis of the scientific literature

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Toward a science of learning systems: A research agenda for the high-functioning learning health system

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records A framework for analysing learning health systems: Are we removing the most impactful barriers? LHS

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Applying implementation science to advance electronic health record-driven learning health systems: Case studies, challenges, and recommendations

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This paper cites Desiderata for computable representa- tions of electronic health records-driven phenotype algorithms.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Desiderata for computable representa- tions of electronic health records-driven phenotype algorithms

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Caveats for the use of operational electronic health record data in comparative effectiveness research

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records The evolving use of electronic health records (EHR) for research

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Use of EHRs data for clinical research: Historical progress and current applications

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Challenges in and opportunities for rlectronic health record-based data analysis and interpretation

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Assessing missing data assumptions in EHR-based studies: A complex and underappreciated task

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Mining for equitable health: Assessing the impact of missing data in electronic health records

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records The concept of allostasis in biology and biomedicine

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Allostasis: A new paradigm to explain arousal pathology

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Stress and the individual

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records What is in a name? Integrating homeostasis, allostasis and stress

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records A systematic review of allostatic load, health, and health disparities

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Allostatic load burden and racial disparities in mortality

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Combinations of biomarkers predictive of later life mortality

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Allostatic overload in patients with essential hypertension

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Allostatic Load and Its Impact on Health: A Systematic Review

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Stress, adaptation, and disease

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Allostatic load and mortality: A systematic review and meta-analysis

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Allostatic load as a marker of cumulative biological risk: MacArthur studies of successful aging

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Overcoming data challenges through enriched validation and targeted sampling to measure whole-person health in electronic health records

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Quality of data collection in a large HIV observational clinic database in sub-Saharan Africa: implications for clinical research and audit of care

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Improving public health information: A data quality intervention in KwaZulu-Natal, South Africa

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Design and implementation of a health management information system in Malawi: Issues, innovations and results

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Analysing the hindrance to the use of information and technology for improving efficiency of health care delivery system in Tanzania

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Multiwave validation sampling for error-prone electronic health records

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Measuring the quality of observational study data in an international HIV research network

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Self-audits as alternatives to travel- audits for improving data quality in the Caribbean, Central and South America network for HIV epidemiology

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Lessons learned from over a decade of data audits in international observational HIV cohorts in Latin America and East Africa

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Research electronic data capture (REDCap)–a metadata-driven methodology and workflow process for providing translational research informatics support

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Using Anchors to Estimate Clinical State without Labeled Data

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records International Statistical Classification of Diseases and Related Health Problems: Tenth Revision

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Using electronic health records to generate phenotypes for research

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP)

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records The validation of electronic health records in accurately identifying patients eligible for colorectal cancer screening in safety net clinics

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Toward a computable phenotype for determining eligibility of lung cancer screening using electronic health records

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Leveraging natural language process- ing to identify eligible lung cancer screening patients with the electronic health record

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Develop and validate a computable phenotype for the identification of Alzheimer’s disease patients using electronic health record data

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Performance of a machine learning algorithm using electronic health record data to identify and estimate survival in a longitudinal cohort of patients with lung cancer

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Using a data quality framework to clean data extracted from the electronic health record: A case study

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records A clustering approach for detecting implausible observation values in electronic health records data

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records An automated data cleaning method for Electronic Health Records by incorporating clinical knowledge

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records MLM-based typographical error correction of unstructured medical texts for named entity recognition

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Automated identifica- tion of implausible values in growth data from pediatric electronic health records

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Observation 4d4e3991-13e3-4990-81d9-9290b0e64b8d · outbound

This paper cites Cleaning of anthropometric data from PCORnet electronic health records using automated algorithms.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Cleaning of anthropometric data from PCORnet electronic health records using automated algorithms

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:48.682391Z

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Observation 4739e91a-e957-42b8-9b84-2b56bc5e6424 · outbound

This paper cites Identifying erroneous height and weight values from adult electronic health records in the All of Us research program.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Identifying erroneous height and weight values from adult electronic health records in the All of Us research program

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:48.744196Z

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Observation 1814cb7f-43bd-4732-aea6-fb12da2c379c · outbound

This paper cites Strategies for handling missing data in electronic health record derived data.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Strategies for handling missing data in electronic health record derived data

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:48.796985Z

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Observation d71ae843-2f80-40e6-b728-f12bf0853299 · outbound

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records DensityTransfer: A data driven approach for imputing electronic health records

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:48.894741Z

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Observation 674dafe1-7614-40f5-be46-2ace612be7fa · outbound

This paper cites Opportunities and challenges in developing risk prediction models with electronic health records data: A systematic review.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Opportunities and challenges in developing risk prediction models with electronic health records data: A systematic review

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:48.950113Z

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Observation 9850cad6-375b-4a34-9410-a4b9ec56f061 · outbound

This paper cites Characterizing and managing missing structured data in electronic health records: Data analysis.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Characterizing and managing missing structured data in electronic health records: Data analysis

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:49.015151Z

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Observation c7b84723-e9ff-47c9-bf54-a94856c1d96a · outbound

This paper cites A deep learning–based unsupervised method to impute missing values in patient records for improved management of cardiovascular patients.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records A deep learning–based unsupervised method to impute missing values in patient records for improved management of cardiovascular patients

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:49.066504Z

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Observation c10c587a-a36f-4491-b7a5-fb9ec4e13ef3 · outbound

This paper cites A novel missing data imputation approach based on clinical conditional generative adversarial networks applied to EHR datasets.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records A novel missing data imputation approach based on clinical conditional generative adversarial networks applied to EHR datasets

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:49.112179Z

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Observation 0e00f0a7-8d8e-48f6-96d7-ed7332789c1a · outbound

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Imputation of missing data in electronic health records based on patients’ similarities

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:49.161204Z

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Observation 4620a96e-f38b-498f-96e9-69b19dce23de · outbound

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Don’t do imputation: Dealing with informative missing values in EHR data analysis

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:49.208836Z

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Observation 8d44da6b-0ecf-42cd-94a0-e785fb677095 · outbound

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Methods and dimensions of electronic health record data quality assessment: Enabling reuse for clinical research

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:49.265795Z

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Observation 87acf42d-be63-4e31-9e9d-792f2b5bb69a · outbound

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records ellmer: Chat with Large Language Models; 2025

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:49.342353Z

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Observation 7b4086b0-1c9f-4804-9f4e-159e27f30653 · outbound

This paper cites Gemini: A Family of Highly Capable Multimodal Models.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Gemini: A Family of Highly Capable Multimodal Models

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:49.395393Z

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Observation 1ef82735-3798-41aa-a1c8-c0f7da9950c6 · outbound

This paper cites An evaluation of GPT models for phenotype concept recognition.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records An evaluation of GPT models for phenotype concept recognition

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:49.514032Z

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Observation 89fc57f2-9e39-4120-9c19-7341197bd80d · outbound

This paper cites Retrieving evidence from EHRs with LLMs: Possibilities and challenges.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Retrieving evidence from EHRs with LLMs: Possibilities and challenges

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:49.642978Z

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Observation 4473f930-64b5-40b4-b5b3-e57a336549ea · outbound

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

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Large language models for data extraction from unstructured and semi-structured electronic health records: a multiple model performance evaluation

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:49.797680Z

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Observation 37a81ff4-00f9-4404-8738-666d9f8d3a42 · outbound

This paper cites Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:49.996868Z

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Observation cb301680-f336-4560-84ca-72d1e2d98b2d · outbound

This paper cites Increasing patient portal usage: Preliminary outcomes from the MyChart Genius Project.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Increasing patient portal usage: Preliminary outcomes from the MyChart Genius Project

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:50.103954Z

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Observation dbe7bc66-0652-4b83-b210-580f822b2565 · outbound

This paper cites From smartphone to EHR: A case report on integrating patient-generated health data.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records From smartphone to EHR: A case report on integrating patient-generated health data

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:50.269673Z

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Observation 972c9bde-5767-4ee9-9aa8-d234c9cfbdd2 · outbound

This paper cites GPT-4 Technical Report.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records GPT-4 Technical Report

Reference 76

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:50.472292Z

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Observation c229581b-c075-4c53-8528-3f84e7911240 · outbound

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On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Introducing the next generation of Claude; 2024

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:50.555399Z

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Observation 9e160b8f-b44e-4f88-ac5b-6c16fea8b464 · outbound

This paper cites Introducing Meta Llama 3: The most capable openly available LLM to date; 2024.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Introducing Meta Llama 3: The most capable openly available LLM to date; 2024

Reference 78

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:50.649647Z

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Observation e2df44e8-b780-464c-805d-1c9f123e9e2a · outbound

This paper cites Copilot; 2023.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Copilot; 2023

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:50.743454Z

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Observation f00bd714-f8aa-4d68-8eec-b45e8d72a5ec · outbound

This paper cites Frequency and types of patient-reported errors in electronic health record ambulatory care notes.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Frequency and types of patient-reported errors in electronic health record ambulatory care notes

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:50.822817Z

Source-reported events for the cited work

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Observation 2015a210-ce93-4ed3-9899-6953f6f02631 · outbound

This paper cites Adolescents identifying errors and omissions in their electronic health records: A national survey.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Adolescents identifying errors and omissions in their electronic health records: A national survey

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:51.047509Z

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Observation f69a8972-c53e-4cb6-821b-b06311456fde · outbound

This paper cites Learning about missing data mechanisms in electronic health records-based research: A survey-based approach.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Learning about missing data mechanisms in electronic health records-based research: A survey-based approach

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:51.084378Z

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Observation 32cb1233-dbcb-4a5f-89bb-bf81a8524ba6 · outbound

This paper cites Automated ICD coding via unsupervised knowledge integration (UNITE).

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Automated ICD coding via unsupervised knowledge integration (UNITE)

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:51.225674Z

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Observation 8b1f4578-ce5c-4e7e-9055-ff2d4ff683fd · outbound

This paper cites Leveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data.

On Using Large Language Models to Enhance Clinically-Driven Missing Data Recovery Algorithms in Electronic Health Records Leveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-04T11:38:51.299205Z

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