ref.bib

@online{aluthgeAnnouncingJuliaHealthOrganization2020,
  title = {Announcing the {{JuliaHealth Organization}}},
  author = {Aluthge, Dilum},
  date = {2020-05},
  url = {https://discourse.julialang.org/t/announcing-the-juliahealth-organization/38574},
  urldate = {2023-12-27},
  abstract = {The Julia programming language forum: discuss usage, development, packages, and community.},
  langid = {english},
  organization = {{Julia Programming Language}},
  file = {/home/thecedarprince/Zotero/storage/Q67CC677/1.html}
}

@inproceedings{benderHL7FHIRAgile2013,
  title = {{{HL7 FHIR}}: {{An Agile}} and {{RESTful}} Approach to Healthcare Information Exchange},
  shorttitle = {{{HL7 FHIR}}},
  booktitle = {Proceedings of the 26th {{IEEE International Symposium}} on {{Computer-Based Medical Systems}}},
  author = {Bender, Duane and Sartipi, Kamran},
  date = {2013-06},
  pages = {326--331},
  issn = {1063-7125},
  doi = {10.1109/CBMS.2013.6627810},
  url = {https://ieeexplore.ieee.org/document/6627810},
  urldate = {2024-01-06},
  abstract = {This research examines the potential for new Health Level 7 (HL7) standard Fast Healthcare Interoperability Resources (FHIR, pronounced “fire”) standard to help achieve healthcare systems interoperability. HL7 messaging standards are widely implemented by the healthcare industry and have been deployed internationally for decades. HL7 Version 2 (“v2”) health information exchange standards are a popular choice of local hospital communities for the exchange of healthcare information, including electronic medical record information. In development for 15 years, HL7 Version 3 (“v3”) was designed to be the successor to Version 2, addressing Version 2's shortcomings. HL7 v3 has been heavily criticized by the industry for being internally inconsistent even in it's own documentation, too complex and expensive to implement in real world systems and has been accused of contributing towards many failed and stalled systems implementations. HL7 is now experimenting with a new approach to the development of standards with FHIR. This research provides a chronicle of the evolution of the HL7 messaging standards, an introduction to HL7 FHIR and a comparative analysis between HL7 FHIR and previous HL7 messaging standards.},
  eventtitle = {Proceedings of the 26th {{IEEE International Symposium}} on {{Computer-Based Medical Systems}}},
  file = {/home/thecedarprince/Zotero/storage/PI9BJ46U/6627810.html}
}

@article{ergina2013ideal,
  title = {{{IDEAL}} Framework for Surgical Innovation 2: Observational Studies in the Exploration and Assessment Stages},
  author = {Ergina, Patrick L and Barkun, Jeffrey S and McCulloch, Peter and Cook, Jonathan A and Altman, Douglas G},
  date = {2013},
  journaltitle = {BMJ (Clinical research ed.)},
  shortjournal = {Bmj},
  volume = {346},
  publisher = {{British Medical Journal Publishing Group}}
}

@software{evansFunOHDSIJl2022,
  title = {{{FunOHDSI}}.Jl},
  author = {Evans, Clark C. and Simonov, Kirill},
  date = {2022},
  origdate = {2021-04-21T19:41:01Z},
  url = {https://github.com/MechanicalRabbit/FunOHDSI.jl},
  urldate = {2023-12-28},
  abstract = {Applications of FunSQL to OMOP Common Data Model},
  organization = {{Mechanical Rabbit}}
}

@software{evansOHDSICohortExpressionsJl2023,
  title = {{{OHDSICohortExpressions}}.Jl},
  author = {Evans, Clark C. and Simonov, Kirill},
  date = {2023-03},
  origdate = {2021-06-02T01:08:47Z},
  url = {https://github.com/MechanicalRabbit/OHDSICohortExpressions.jl},
  urldate = {2023-12-28},
  abstract = {reimplementation of OHDSI's Circe JSON-{$>$}SQL compiler},
  organization = {{Mechanical Rabbit}}
}

@misc{FDARealWorldEvidence,
  title = {Real-World Evidence},
  author = {{U.S. Food and Drug Administration}},
  date = {2021-09},
  url = {https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence}
}

@article{hripcsakCharacterizingTreatmentPathways2016,
  title = {Characterizing Treatment Pathways at Scale Using the {{OHDSI}} Network},
  author = {Hripcsak, George and Ryan, Patrick B. and Duke, Jon D. and Shah, Nigam H. and Park, Rae Woong and Huser, Vojtech and Suchard, Marc A. and Schuemie, Martijn J. and DeFalco, Frank J. and Perotte, Adler and Banda, Juan M. and Reich, Christian G. and Schilling, Lisa M. and Matheny, Michael E. and Meeker, Daniella and Pratt, Nicole and Madigan, David},
  date = {2016-07-05},
  journaltitle = {Proceedings of the National Academy of Sciences},
  shortjournal = {Proc. Natl. Acad. Sci. U.S.A.},
  volume = {113},
  number = {27},
  pages = {7329--7336},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1510502113},
  url = {https://pnas.org/doi/full/10.1073/pnas.1510502113},
  urldate = {2023-11-05},
  abstract = {Observational research promises to complement experimental research by providing large, diverse populations that would be infeasible for an experiment. Observational research can test its own clinical hypotheses, and observational studies also can contribute to the design of experiments and inform the generalizability of experimental research. Understanding the diversity of populations and the variance in care is one component. In this study, the Observational Health Data Sciences and Informatics (OHDSI) collaboration created an international data network with 11 data sources from four countries, including electronic health records and administrative claims data on 250 million patients. All data were mapped to common data standards, patient privacy was maintained by using a distributed model, and results were aggregated centrally. Treatment pathways were elucidated for type 2 diabetes mellitus, hypertension, and depression. The pathways revealed that the world is moving toward more consistent therapy over time across diseases and across locations, but significant heterogeneity remains among sources, pointing to challenges in generalizing clinical trial results. Diabetes favored a single first-line medication, metformin, to a much greater extent than hypertension or depression. About 10\% of diabetes and depression patients and almost 25\% of hypertension patients followed a treatment pathway that was unique within the cohort. Aside from factors such as sample size and underlying population (academic medical center versus general population), electronic health records data and administrative claims data revealed similar results. Large-scale international observational research is feasible.},
  langid = {english},
  file = {/home/thecedarprince/Zotero/storage/LDZZJJDR/Hripcsak et al_2016_Characterizing treatment pathways at scale using the OHDSI network.pdf}
}

@article{johnson2016mimic,
  title = {{{MIMIC-III}}, a Freely Accessible Critical Care Database},
  author = {Johnson, Alistair E W and Pollard, Tom J and Shen, Lu and Lehman, Li-Wei H and Feng, Mengling and Ghassemi, Mohammad and Moody, Benjamin and Szolovits, Peter and Celi, Leo Anthony and Mark, Roger G},
  date = {2016},
  journaltitle = {Scientific data},
  volume = {3},
  number = {1},
  pages = {1--9},
  publisher = {{Nature Publishing Group}}
}

@article{johnson2018mimic,
  title = {The {{MIMIC Code Repository}}: Enabling Reproducibility in Critical Care Research},
  author = {Johnson, Alistair EW and Stone, David J and Celi, Leo A and Pollard, Tom J},
  date = {2018},
  journaltitle = {Journal of the American Medical Informatics Association},
  volume = {25},
  number = {1},
  pages = {32--39},
  publisher = {{Oxford University Press}}
}

@online{kelechavaSQLStandardISO2018,
  title = {The {{SQL Standard}} - {{ISO}}/{{IEC}} 9075:2023 ({{ANSI X3}}.135)},
  shorttitle = {The {{SQL Standard}} - {{ISO}}/{{IEC}} 9075},
  author = {Kelechava, Brad},
  date = {2018-10-05T18:58:56+00:00},
  url = {https://blog.ansi.org/sql-standard-iso-iec-9075-2023-ansi-x3-135/},
  urldate = {2024-01-01},
  abstract = {SQL (Structured Query Language) for relational database management systems is specified by ISO/IEC 9075:2023, with origins in ANSI X3.135.},
  langid = {american},
  organization = {{The ANSI Blog}},
  file = {/home/thecedarprince/Zotero/storage/4P4R9VLS/sql-standard-iso-iec-9075-2023-ansi-x3-135.html}
}

@software{kirill_simonov_2023_7705325,
  title = {{{FunSQL}} : {{Julia}} Library for Compositional Construction of {{SQL}} Queries},
  author = {Simonov, Kirill and Evans, Clark C. and Zelko, Jacob S.},
  date = {2023-03},
  doi = {10.5281/zenodo.7705325},
  url = {https://doi.org/10.5281/zenodo.7705325},
  organization = {{Zenodo}},
  version = {v0.11.1}
}

@online{madrilATLASJSONSchema2022,
  title = {{{ATLAS JSON Schema}}},
  author = {Madril, Pablo},
  date = {2022},
  url = {https://github.com/OHDSIBr/ATLAS-JSON-Schema},
  urldate = {2023-12-28},
  file = {/home/thecedarprince/Zotero/storage/J9M2MVSW/ATLAS-JSON-Schema.html}
}

@article{mandelSMARTFHIRStandardsbased2016,
  title = {{{SMART}} on {{FHIR}}: A Standards-Based, Interoperable Apps Platform for Electronic Health Records},
  shorttitle = {{{SMART}} on {{FHIR}}},
  author = {Mandel, Joshua C and Kreda, David A and Mandl, Kenneth D and Kohane, Isaac S and Ramoni, Rachel B},
  date = {2016-09-01},
  journaltitle = {Journal of the American Medical Informatics Association},
  shortjournal = {Journal of the American Medical Informatics Association},
  volume = {23},
  number = {5},
  pages = {899--908},
  issn = {1067-5027},
  doi = {10.1093/jamia/ocv189},
  url = {https://doi.org/10.1093/jamia/ocv189},
  urldate = {2024-01-06},
  abstract = {Objective In early 2010, Harvard Medical School and Boston Children’s Hospital began an interoperability project with the distinctive goal of developing a platform to enable medical applications to be written once and run unmodified across different healthcare IT systems. The project was called Substitutable Medical Applications and Reusable Technologies (SMART). Methods We adopted contemporary web standards for application programming interface transport, authorization, and user interface, and standard medical terminologies for coded data. In our initial design, we created our own openly licensed clinical data models to enforce consistency and simplicity. During the second half of 2013, we updated SMART to take advantage of the clinical data models and the application-programming interface described in a new, openly licensed Health Level Seven draft standard called Fast Health Interoperability Resources (FHIR). Signaling our adoption of the emerging FHIR standard, we called the new platform SMART on FHIR. Results We introduced the SMART on FHIR platform with a demonstration that included several commercial healthcare IT vendors and app developers showcasing prototypes at the Health Information Management Systems Society conference in February 2014. This established the feasibility of SMART on FHIR, while highlighting the need for commonly accepted pragmatic constraints on the base FHIR specification. Conclusion In this paper, we describe the creation of SMART on FHIR, relate the experience of the vendors and developers who built SMART on FHIR prototypes, and discuss some challenges in going from early industry prototyping to industry-wide production use.},
  file = {/home/thecedarprince/Zotero/storage/BE6AJHIY/Mandel et al_2016_SMART on FHIR.pdf;/home/thecedarprince/Zotero/storage/EDE3YXCZ/2379865.html}
}

@book{ohdsi2019book,
  title = {The Book of {{OHDSI}}: {{Observational}} Health Data Sciences and Informatics},
  author = {{OHDSI}},
  date = {2019},
  publisher = {{OHDSI}}
}

@article{overhage2012validation,
  title = {Validation of a Common Data Model for Active Safety Surveillance Research},
  author = {Overhage, J Marc and Ryan, Patrick B and Reich, Christian G and Hartzema, Abraham G and Stang, Paul E},
  date = {2012},
  journaltitle = {Journal of the American Medical Informatics Association},
  volume = {19},
  number = {1},
  pages = {54--60},
  publisher = {{BMJ Group BMA House, Tavistock Square, London, WC1H 9JR}}
}

@article{physionet,
  title = {{{PhysioBank}}, {{PhysioToolkit}}, and {{PhysioNet}}: Components of a New Research Resource for Complex Physiologic Signals},
  author = {Goldberger, Ary L and Amaral, Luis AN and Glass, Leon and Hausdorff, Jeffrey M and Ivanov, Plamen Ch and Mark, Roger G and Mietus, Joseph E and Moody, George B and Peng, Chung-Kang and Stanley, H Eugene},
  date = {2000},
  journaltitle = {Circulation},
  volume = {101},
  number = {23},
  pages = {e215--e220},
  publisher = {{Am Heart Assoc}}
}

@misc{PythonCall.jl,
  title = {{{PythonCall}}.Jl: {{Python}} and {{Julia}} in Harmony},
  author = {Rowley, Christopher},
  date = {2022},
  url = {https://github.com/JuliaPy/PythonCall.jl}
}

@online{RCallJl,
  title = {{{RCall}}.Jl},
  url = {https://github.com/JuliaInterop/RCall.jl},
  urldate = {2023-11-26},
  abstract = {Call R from Julia. Contribute to JuliaInterop/RCall.jl development by creating an account on GitHub.},
  langid = {english},
  organization = {{GitHub}},
  file = {/home/thecedarprince/Zotero/storage/LRT5HUBY/RCall.html}
}

@misc{roseFindingAnswersBig2008,
  title = {Finding {{Answers To Big Questions}}: {{Overcoming Disciplinary Boundaries Through Research Networks}}},
  author = {Rose, Robert},
  date = {2008},
  organization = {{MacArthur Foundation}}
}

@book{sachsonOurJourney2023,
  title = {Our {{Journey}}},
  author = {Sachson, Craig},
  date = {2023}
}

@software{schuemieEunomia2023,
  title = {Eunomia},
  author = {Schuemie, Martijn and DeFalco, Frank and Huser, Vojtech},
  date = {2023-09-19T21:18:34Z},
  origdate = {2019-02-06T10:46:22Z},
  url = {https://github.com/OHDSI/Eunomia},
  urldate = {2023-12-03},
  abstract = {A standard CDM dataset for testing and demonstration purposes.},
  organization = {{Observational Health Data Sciences and Informatics}},
  keywords = {hades}
}

@video{thejuliaprogramminglanguage100MillionPatients2023,
  entrysubtype = {video},
  title = {100 {{Million Patients}}: {{Julia}} for {{International Health Studies}} | {{Jacob Zelko}} | {{JuliaCon}} 2023},
  shorttitle = {100 {{Million Patients}}},
  editor = {{The Julia Programming Language}},
  editortype = {director},
  date = {2023-09-11},
  url = {https://www.youtube.com/watch?v=d7SA36kVaq0},
  urldate = {2023-12-03},
  abstract = {This talk explores the use of Julia in a novel observational health research study that explores health equity and mental health in \textasciitilde 100 million patients in an international collaborative effort across more than 4 countries. Contributions and efforts within the JuliaHealth and adjacent communities have made working with this data possible. The approaches and results shared will be valuable for potential researchers and will open new frontiers for high performance computing and health analytics. Conducting health research studies at scale to understand the health of specific communities and subpopulations has long been a struggle. This has been due to a variety of issues, such as a lack of international standards in the structure of electronic health records, patient claims data, and diagnoses. Moreover, the investigation of questions related to the topic of health equity (that is, the skewed distribution of health resources or services to various subpopulations seeking healthcare) has been largely stalled due to these problems. In a previous talk I gave, Using Julia for Observational Health Research, I presented early work on the success of using Julia within the space of observational health research in utilizing the OMOP Common Data Model. In that previous work, I conducted a pilot study to characterize prevalence rates in mental health care for intersectional subpopulations suffering from bipolar disorder, depression, and/or suicidality. This work utilized novel tooling and approaches created within Julia to successfully analyze data from \textasciitilde 2.5 million Medicaid subscribers within the U.S. state of Georgia. This work earned the highest awards at the top observational health research venue, drove another successful grant proposal, and resulted in multiple invited talks. Buoyed by the interest and success of this pilot work, my team and I have moved this project into the next phase: the examination of more than 100 million patients from more than 4 countries across the globe. In this talk, I will present advances within the JuliaHealth community and the broader Julia ecosystem that have made possible such large scale and federated analyses. In particular, novel JuliaHealth tools such as OMOPCDMCohortCreator.jl will be highlighted to show how to analyze "big" real world data, how using Julia can be of huge benefit within this space, and how Julia community members could start using these tools for their own research. As this study now takes place across multiple countries, time will also be spent discussing how Julia lends itself very well to robust analyses using literate programming tools such as Quarto or Weave.jl and versioning processes through DrWatson.jl or Data Version Control, which can be utilized to handle each country's specific needs. Additionally, I will spend some time discussing issues encountered (both technical and anthropological), ways that the Julia ecosystem could potentially grow to support future work in this research domain, and opportunities for Julia users to get involved. Finally, I will share my personal thoughts on what open questions there are to be addressed in observational health research and how Julia can be a tool to address public health questions and provide insight into questions of health disparities. In conclusion, this talk will highlight the real world use of Julia in large-scale health research studies built on real world data. Moreover, it will show the potential of the various ecosystems within Julia to analyze and tackle complex questions within health equity. Through this talk, I invite future Julia users and researchers to join me in pursuing the potential of Julia within the space of observational health research.}
}

@article{walonoski2018synthea,
  title = {Synthea: {{An}} Approach, Method, and Software Mechanism for Generating Synthetic Patients and the Synthetic Electronic Health Care Record},
  author = {Walonoski, Jason and Kramer, Mark and Nichols, Joseph and Quina, Andre and Moesel, Chris and Hall, Dylan and Duffett, Carlton and Dube, Kudakwashe and Gallagher, Thomas and McLachlan, Scott},
  date = {2018},
  journaltitle = {Journal of the American Medical Informatics Association},
  volume = {25},
  number = {3},
  pages = {230--238},
  publisher = {{Oxford University Press}}
}

@software{zelko_2023_10232753,
  title = {{{OMOPCDMCohortCreator}} 0.4.0},
  author = {Zelko, Jacob and Chinta, Varshini and Abdelazeez, Fareeda and Sanjay, Jay},
  date = {2023-11},
  doi = {10.5281/zenodo.10232753},
  url = {https://doi.org/10.5281/zenodo.10232753},
  organization = {{Zenodo}},
  version = {v0.4.0}
}

@article{zelko2022pilot,
  title = {A Pilot Characterization Study Assessing Health Equity in Mental Healthcare Delivery within the State of Georgia},
  author = {Zelko, Jacob and Hy, Malina and Chinta, Varshini and Liau, Emily and Knowlton, Morgan},
  date = {2022}
}

@inproceedings{zelko2023julia,
  title = {Julia in Health and Medicine},
  booktitle = {{{JuliaCon}} 2023},
  author = {Zelko, Jacob S.},
  date = {2023-07},
  publisher = {{Birds of a Feather}},
  location = {{MIT, Cambridge, Boston}}
}

@online{zelkoDevelopingRobustComputable2023,
  title = {Developing a {{Robust Computable Phenotype Definition Workflow}} to {{Describe Health}} and {{Disease}} in {{Observational Health Research}}},
  author = {Zelko, Jacob S. and Gasman, Sarah and Freeman, Shenita R. and Lee, Dong Yun and Altosaar, Jaan and Shoaibi, Azza and Rao, Gowtham},
  date = {2023-03-30},
  eprint = {2304.06504},
  eprinttype = {arxiv},
  eprintclass = {cs},
  doi = {10.48550/arXiv.2304.06504},
  url = {http://arxiv.org/abs/2304.06504},
  urldate = {2023-04-16},
  abstract = {Health informatics can inform decisions that practitioners, patients, policymakers, and researchers need to make about health and disease. Health informatics is built upon patient health data leading to the need to codify patient health information. Such standardization is required to compute population statistics (such as prevalence, incidence, etc.) that are common metrics used in fields such as epidemiology. Reliable decision-making about health and disease rests on our ability to organize, analyze, and assess data repositories that contain patient health data. While standards exist to structure and analyze patient data across patient data sources such as health information exchanges, clinical data repositories, and health data marketplaces, analogous best practices for rigorously defining patient populations in health informatics contexts do not exist. Codifying best practices for developing disease definitions could support the effective development of clinical guidelines, inform algorithms used in clinical decision support systems, and additional patient guidelines. In this paper, we present a workflow for the development of phenotype definitions. This workflow presents a series of recommendations for defining health and disease. Various examples within this paper are presented to demonstrate this workflow in health informatics contexts.},
  pubstate = {preprint},
  keywords = {Computer Science - Computers and Society},
  file = {/home/thecedarprince/Zotero/storage/DYMRWTVD/2304.html}
}