Published projects with scrambled reference lists

[bemoody]

These are the published projects that still have possibly broken lists of references (see issue #2137):

Broken version	Older version
bionlp-workshop-2023-task-1a/2.0.0	bionlp-workshop-2023-task-1a/1.1.0
butppg/2.0.0	butppg/1.0.0
chexchonet/1.0.0	None
chexmask-cxr-segmentation-data/0.3	chexmask-cxr-segmentation-data/0.2
chexmask-cxr-segmentation-data/0.4	chexmask-cxr-segmentation-data/0.2
corpus-fungal-infections/1.0.1	corpus-fungal-infections/1.0.0
encode-skin-color/1.0.0	None
inspire/1.1	inspire/1.0
inspire/1.2	inspire/1.0
inspire/1.3	inspire/1.0
mimic-iv-fhir/1.0	None
orchid/2.0.0	orchid/1.0.0
physiotag/1.0.0	None
sicdb/1.0.6	sicdb/1.0.5
sicdb/1.0.8	sicdb/1.0.5

If anybody wants to help with fixing these, please:

• Look at the broken version page and find all the bracketed citations.
• Look at the older version page, if any, to find the corresponding citation and figure out which reference it is meant to refer to.
• Make a list of all references in the broken version page, in the correct order.
• Please resist the urge to fix any typos or remove works that are not cited. Please just copy and paste the references exactly as they currently appear.
• Post the corrected list in a reply to this issue.

[bemoody]

Example

Project: bionlp-workshop-2023-task-1a/2.0.0

Corrected list of references:

1   Gao, Y., Caskey, J., Miller, T., Sharma, B., Churpek, M., Dligach, D., & Afshar, M. (2022). Tasks 1 and 3 from Progress Note Understanding Suite of Tasks: SOAP Note Tagging and Problem List Summarization (version 1.0.0). PhysioNet. https://doi.org/10.13026/wks0-w041.
2   Yanjun Gao, Dmitriy Dligach, Timothy Miller, Dongfang Xu, Matthew M. M. Churpek, and Majid Afshar. 2022. Summarizing Patients’ Problems from Hospital Progress Notes Using Pre-trained Sequence-to-Sequence Models. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2979–2991, Gyeongju, Republic of Korea. International Committee on Computational Linguistics. https://aclanthology.org/2022.coling-1.264/
3   Yanjun Gao, Dmitriy Dligach, Timothy Miller, Samuel Tesch, Ryan Laffin, Matthew M. Churpek, and Majid Afshar. 2022. Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 5484–5493, Marseille, France. European Language Resources Association.
4   Website for BIONLP 2023 and Shared Tasks @ ACL 2023. https://aclweb.org/aclwiki/BioNLP_Workshop [Accessed: 18 Jan 2023]
5   Johnson, A., Pollard, T., & Mark, R. (2016). MIMIC-III Clinical Database (version 1.4). PhysioNet. https://doi.org/10.13026/C2XW26
6   Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Celi, L. A., & Mark, R. G. (2016). MIMIC-III, a freely accessible critical care database. Scientific Data, 3, 160035.
7   Lin, Chin-Yew. 2004. ROUGE: a Package for Automatic Evaluation of Summaries. In Proceedings of the Workshop on Text Summarization Branches Out (WAS 2004), Barcelona, Spain, July 25 - 26, 2004.
8   Lin, Chin-Yew and Franz Josef Och. 2004. Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence and Skip-Bigram Statistics. In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL 2004), Barcelona, Spain, July 21 - 26, 2004.
9   Yanjun Gao, Dmitriy Dligach, Timothy Miller, and Majid Afshar. 2023. Overview of the Problem List Summarization (ProbSum) 2023 Shared Task on Summarizing Patients’ Active Diagnoses and Problems from Electronic Health Record Progress Notes. In The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks, pages 461–467, Toronto, Canada. Association for Computational Linguistics.

[bemoody]

Project: butppg/2.0.0

Corrected list of references:

1   Orphanidou, C. (2018). Signal Quality Assessment in Physiological Monitoring State of the Art and Practical Considerations. Cham: Springer. doi:10.1007/978-3-319-68415-4.
2   Naeini, E. K., Azimi, I., Rahmani, A. M., Liljeberg, P., & Dutt, N. (2019). A Real-time PPG Quality Assessment Approach for Healthcare Internet-of-Things. Procedia Computer Science, 151, 551-558. doi:10.1016/j.procs.2019.04.074.
3   Nemcova, A., Jordanova, I., Varecka, M., Smisek, R., Marsanova, L., Smital, L., & Vitek, M. (2020). Monitoring of heart rate, blood oxygen saturation, and blood pressure using a smartphone. Biomedical Signal Processing and Control, 59. doi:10.1016/j.bspc.2020.101928.
4   Siddiqui, S. A., Zhang, Y., Feng, Z., & Kos, A. (2016). A Pulse Rate Estimation Algorithm Using PPG and Smartphone Camera. Journal of Medical Systems, 40(5). doi:10.1007/s10916-016-0485-6.
5   Tabei, F., Zaman, R., Foysal, K. H., Kumar, R., Kim, Y., & Chong, J. W. (2019). A novel diversity method for smartphone camera-based heart rhythm signals in the presence of motion and noise artifacts. Plos One, 14(6). doi:10.1371/journal.pone.0218248.
6   Smital, L., Marsanova, L., Smisek, R., Nemcova, A., & Vitek, M. (2020, September). Robust QRS Detection Using Combination of Three Independent Methods. In Computing in cardiology 2020.
7   International Electrotechnical Commission. (2014). Medical electrical equipment. Particular requirements for the basic safety and essential performance of electrocardiographic monitoring equipment (IEC 60601-2-27).
8   Peng, R., Zhou, X., Lin, W., & Zhang, Y. (2015). Extraction of Heart Rate Variability from Smartphone Photoplethysmograms. Computational and Mathematical Methods in Medicine, 2015, 1-11. doi:10.1155/2015/516826.

[bemoody]

Project: chexchonet/1.0.0

Corrected list of references:

1   d'Arcy, J. L., Coffey, S., Loudon, M. A., Kennedy, A., Pearson-Stuttard, J., Birks, J., ... & Prendergast, B. D. (2016). Large-scale community echocardiographic screening reveals a major burden of undiagnosed valvular heart disease in older people: the OxVALVE Population Cohort Study. European heart journal, 37(47), 3515-3522.
2   Maron, M. S., Hellawell, J. L., Lucove, J. C., Farzaneh-Far, R., & Olivotto, I. (2016). Occurrence of clinically diagnosed hypertrophic cardiomyopathy in the United States. The American journal of cardiology, 117(10), 1651-1654.
3   Alexander, K. M., Orav, J., Singh, A., Jacob, S. A., Menon, A., Padera, R. F., ... & Dorbala, S. (2018). Geographic disparities in reported US amyloidosis mortality from 1979 to 2015: potential underdetection of cardiac amyloidosis. JAMA cardiology, 3(9), 865-870.
4   Cook, C. H., Praba, A. C., Beery, P. R., & Martin, L. C. (2002). Transthoracic echocardiography is not cost-effective in critically ill surgical patients. Journal of Trauma and Acute Care Surgery, 52(2), 280-284.
5   Lang RM, Badano LP, Mor-Avi V, Afilalo J, Armstrong A, Ernande L, Flachskampf FA, Foster E, Goldstein SA, Kuznetsova T, Lancellotti P. Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging. European Heart Journal-Cardiovascular Imaging. 2015 Mar 1;16(3):233-71.

[bemoody]

Project: chexmask-cxr-segmentation-data/0.3

Corrected list of references:

1   Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A, editors. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Cham: Springer; 2015. p. 234-241. (Lecture Notes in Computer Science; vol 9351).
2   Moukheiber D, Mahindre S, Moukheiber L, Moukheiber M, Wang S, Ma C, Shih G, Peng Y, Gao M. Few-Shot Learning Geometric Ensemble for Multi-label Classification of Chest X-Rays. InData Augmentation, Labelling, and Imperfections: Second MICCAI Workshop, DALI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings 2022 Sep 16 (pp. 112-122). Cham: Springer Nature Switzerland.
3   Wang R, Chen LC, Moukheiber L, Seastedt KP, Moukheiber M, Moukheiber D, Zaiman Z, Moukheiber S, Litchman T, Trivedi H, Steinberg R. Enabling chronic obstructive pulmonary disease diagnosis through chest X-rays: A multi-site and multi-modality study. International Journal of Medical Informatics. 2023 Oct 1;178:105211.
4   Gaggion N, Mansilla L, Mosquera C, Milone DH, Ferrante E. Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis. IEEE Trans Med Imaging. 2022. doi:10.1109/TMI.2022.3224660.
5   Feng S, et al. Curation of the candid-ptx dataset with free-text reports. Radiology: Artificial Intelligence. 2021;3(6):e210136.
6   Wang X, et al. Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
7   Irvin J, et al. Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. Proceedings of the AAAI conference on artificial intelligence. 2019;33(01).
8   Johnson AE, et al. MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs. arXiv preprint. 2019. arXiv:1901.07042.
9   Bustos A, et al. Padchest: A large chest x-ray image dataset with multi-label annotated reports. Med Image Anal. 2020;66:101797.
10  Nguyen HQ, et al. VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations. Sci Data. 2022;9(1):429.
11  Valindria VV, et al. Reverse classification accuracy: predicting segmentation performance in the absence of ground truth. IEEE Trans Med Imaging. 2017;36:1597–1606.
12  Gaggion N. Chest-xray-landmark-dataset [Internet]. GitHub repository. Available from: https://github.com/ngaggion/Chest-xray-landmark-dataset. [Accessed 6/27/2023]
13  Gaggion N, Vakalopoulou M, Milone DH, Ferrante E. Multi-center anatomical segmentation with heterogeneous labels via landmark-based models. In: 20th IEEE International Symposium on Biomedical Imaging (ISBI). IEEE; 2023.

[bemoody]

Project: chexmask-cxr-segmentation-data/0.4

Corrected list of references:

1   Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A, editors. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. Cham: Springer; 2015. p. 234-241. (Lecture Notes in Computer Science; vol 9351).
2   Gaggion N, Mansilla L, Mosquera C, Milone DH, Ferrante E. Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis. IEEE Trans Med Imaging. 2022. doi:10.1109/TMI.2022.3224660.
3   Wang X, et al. Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
4   Irvin J, et al. Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. Proceedings of the AAAI conference on artificial intelligence. 2019;33(01).
5   Johnson AE, et al. MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs. arXiv preprint. 2019. arXiv:1901.07042.
6   Bustos A, et al. Padchest: A large chest x-ray image dataset with multi-label annotated reports. Med Image Anal. 2020;66:101797.
7   Nguyen HQ, et al. VinDr-CXR: An open dataset of chest X-rays with radiologist’s annotations. Sci Data. 2022;9(1):429.
8   Valindria VV, et al. Reverse classification accuracy: predicting segmentation performance in the absence of ground truth. IEEE Trans Med Imaging. 2017;36:1597–1606.
9   Gaggion N. Chest-xray-landmark-dataset [Internet]. GitHub repository. Available from: https://github.com/ngaggion/Chest-xray-landmark-dataset. [Accessed 6/27/2023]
10  Gaggion N, Vakalopoulou M, Milone DH, Ferrante E. Multi-center anatomical segmentation with heterogeneous labels via landmark-based models. In: 20th IEEE International Symposium on Biomedical Imaging (ISBI). IEEE; 2023.
11  Wang R, Chen LC, Moukheiber L, Seastedt KP, Moukheiber M, Moukheiber D, Zaiman Z, Moukheiber S, Litchman T, Trivedi H, Steinberg R. Enabling chronic obstructive pulmonary disease diagnosis through chest X-rays: A multi-site and multi-modality study. International Journal of Medical Informatics. 2023 Oct 1;178:105211.

[bemoody]

Project: corpus-fungal-infections/1.0.1

Corrected list of references:

1   Kontoyiannis DP, Marr KA, Park BJ, Alexander BD, Anaissie EJ, Walsh TJ, et al. Prospective surveillance for invasive fungal infections in hematopoietic stem cell transplant recipients, 2001-2006: overview of the Transplant-Associated Infection Surveillance Network (TRANSNET) Database. Clin Infect Dis. 2010;50(8):1091-100.
2   Donnelly JP, Chen SC, Kauffman CA, Steinbach WJ, Baddley JW, Verweij PE, et al. Revision and Update of the Consensus Definitions of Invasive Fungal Disease From the European Organization for Research and Treatment of Cancer and the Mycoses Study Group Education and Research Consortium. Clin Infect Dis. 2020;71(6):1367-76.
3   Stenetorp P, Pyysalo S, Topić G, Ohta T, Ananiadou S, Tsujii Ji, editors. brat: a Web- based Tool for NLP-Assisted Text Annotation2012 April; Avignon, France: Association for Computational Linguistics.
4   http://brat.nlplab.org/standoff [Accessed 10/16/2023]
5   Rozova V, Khanina A, Teng JC, S K Teh J, Worth LJ, Slavin MA, et al. Detecting evidence of invasive fungal infections in cytology and histopathology reports enriched with concept-level annotations. Journal of Biomedical Informatics. 2023:104293.
6   https://github.com/vlada-rozova/chifir-jbi [Accessed 10/16/2023]

[bemoody]

Project: encode-skin-color/1.0.0

Corrected list of references:

1   Jubran A. Pulse oximetry. Crit Care. 2015;19: 272. doi:10.1186/s13054-015-0984-8
2   Wong A-KI, Charpignon M, Kim H, Josef C, de Hond AAH, Fojas JJ, et al. Analysis of Discrepancies Between Pulse Oximetry and Arterial Oxygen Saturation Measurements by Race and Ethnicity and Association With Organ Dysfunction and Mortality. JAMA Network Open. 2021;4: e2131674–e2131674. doi:10.1001/jamanetworkopen.2021.31674
3   Fawzy A, Wu TD, Wang K, Robinson ML, Farha J, Bradke A, et al. Racial and Ethnic Discrepancy in Pulse Oximetry and Delayed Identification of Treatment Eligibility Among Patients With COVID-19. JAMA Intern Med. 2022;182: 730–738. doi:10.1001/jamainternmed.2022.1906
4   Valbuena VSM, Barbaro RP, Claar D, Valley TS, Dickson RP, Gay SE, et al. Racial Bias in Pulse Oximetry Measurement Among Patients About to Undergo Extracorporeal Membrane Oxygenation in 2019-2020: A Retrospective Cohort Study. Chest. 2022;161: 971–978. doi:10.1016/j.chest.2021.09.025
5   Henry NR, Hanson AC, Schulte PJ, Warner NS, Manento MN, Weister TJ, et al. Disparities in Hypoxemia Detection by Pulse Oximetry Across Self-Identified Racial Groups and Associations With Clinical Outcomes. Crit Care Med. 2022;50: 204–211. doi:10.1097/CCM.0000000000005394
6   Jamali H, Castillo LT, Morgan CC, Coult J, Muhammad JL, Osobamiro OO, et al. Racial Disparity in Oxygen Saturation Measurements by Pulse Oximetry: Evidence and Implications. Ann Am Thorac Soc. 2022;19: 1951–1964. doi:10.1513/AnnalsATS.202203-270CME
7   Chesley CF, Lane-Fall MB, Panchanadam V, Harhay MO, Wani AA, Mikkelsen ME, et al. Racial Disparities in Occult Hypoxemia and Clinically Based Mitigation Strategies to Apply in Advance of Technological Advancements. Respir Care. 2022;67: 1499–1507. doi:10.4187/respcare.09769
8   Ward E, Katz MH. Confronting the Clinical Implications of Racial and Ethnic Discrepancy in Pulse Oximetry. JAMA internal medicine. 2022. p. 858. doi:10.1001/jamainternmed.2022.2581
9   Ketcham SW, Sedhai YR, Miller HC, Bolig TC, Ludwig A, Co I, et al. Causes and characteristics of death in patients with acute hypoxemic respiratory failure and acute respiratory distress syndrome: a retrospective cohort study. Crit Care. 2020;24: 391. doi:10.1186/s13054-020-03108-w
10   Sjoding MW, Dickson RP, Iwashyna TJ, Gay SE, Valley TS. Racial Bias in Pulse Oximetry Measurement. N Engl J Med. 2020;383: 2477–2478. doi:10.1056/NEJMc2029240
11  Bickler PE, Feiner JR, Severinghaus JW. Effects of skin pigmentation on pulse oximeter accuracy at low saturation. Anesthesiology. 2005;102: 715–719. doi:10.1097/00000542-200504000-00004
12  Shi C, Goodall M, Dumville J, Hill J, Norman G, Hamer O, et al. The accuracy of pulse oximetry in measuring oxygen saturation by levels of skin pigmentation: a systematic review and meta-analysis. BMC Med. 2022;20: 267. doi:10.1186/s12916-022-02452-8
13  Hao S, Dempsey K, Matos J, Cox CE, Rotemberg V, Gichoya JW, et al. Utility of skin tone on pulse oximetry in critically ill patients: a prospective cohort study. medRxiv. 2024. doi:10.1101/2024.02.24.24303291
14  Monk E. The Monk Skin Tone Scale. 2023. doi:10.31235/osf.io/pdf4c
15  Wikipedia contributors. Von Luschan’s chromatic scale. In: Wikipedia, The Free Encyclopedia [Internet]. 14 Jun 2024. Available: https://en.wikipedia.org/w/index.php?title=Von_Luschan%27s_chromatic_scale&oldid=1229032312
16  Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap) - a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42: 377–381. doi:10.1016/j.jbi.2008.08.010
17  OMOP CDM v5.4. Available: https://ohdsi.github.io/CommonDataModel/cdm54.html
18  GitHub repository for the ENCoDE project. https://github.com/aiwonglab/ENCoDE_tutorial

[bemoody]

Project: inspire/1.1

Corrected list of references:

1   Tevis, S. E., Cobian, A. G., Truong, H. P., Craven, M. W. & Kennedy, G. D. Implications of Multiple Complications on the Postoperative Recovery of General Surgery Patients. Ann Surg 263, 1213-1218, doi:10.1097/SLA.0000000000001390 (2016).
2   Fink, A. S. et al. The National Surgical Quality Improvement Program in non-veterans administration hospitals: initial demonstration of feasibility. Ann Surg 236, 344-353; discussion 353-344, doi:10.1097/00000658-200209000-00011 (2002).
3   Liau, A., Havidich, J. E., Onega, T. & Dutton, R. P. The National Anesthesia Clinical Outcomes Registry. Anesth Analg 121, 1604-1610, doi:10.1213/ANE.0000000000000895 (2015).
4   Lee, H. C. et al. VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients. Sci Data 9, 279, doi:10.1038/s41597-022-01411-5 (2022).
5   Vistisen, S. T., Pollard, T. J., Enevoldsen, J. & Scheeren, T. W. L. VitalDB: fostering collaboration in anaesthesia research. Br J Anaesth 127, 184-187, doi:10.1016/j.bja.2021.03.011 (2021).
6   Johnson, A. E. et al. MIMIC-III, a freely accessible critical care database. Sci Data 3, 160035, doi:10.1038/sdata.2016.35 (2016).
7   Bektas, M., Tuynman, J. B., Costa Pereira, J., Burchell, G. L. & van der Peet, D. L. Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review. World J Surg 46, 3100-3110, doi:10.1007/s00268-022-06728-1 (2022).
8   Penny-Dimri, J. C. et al. Machine learning to predict adverse outcomes after cardiac surgery: A systematic review and meta-analysis. J Card Surg 37, 3838-3845, doi:10.1111/jocs.16842 (2022).
9   Senanayake, S. et al. Machine learning in predicting graft failure following kidney transplantation: A systematic review of published predictive models. Int J Med Inform 130, 103957, doi:10.1016/j.ijmedinf.2019.103957 (2019).
10  Steyerberg, E. W. & Harrell, F. E., Jr. Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol 69, 245-247, doi:10.1016/j.jclinepi.2015.04.005 (2016).
11  The Centers for Medicare and Medicaid Services and the National Center for Health Statistics, U. S. ICD-10-CM Official Guidelines for Coding and Reporting FY 2022, https://www.cms.gov/files/document/fy-2022-icd-10-cm-coding-guidelines-updated-02012022.pdf (2023).
12  Moon, T. J. Light and shadows of the Korean healthcare system. J Korean Med Sci 27 Suppl, S3-6, doi:10.3346/jkms.2012.27.S.S3 (2012).
13  A full list of the ICD-10-CM codes. https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Publications/ICD10CM/April-1-2023-Update/icd10cm-code%20descriptions-%20April%201%202023.zip
14  Sample code of machine learning model for 30-day mortality after surgery. https://github.com/vitaldb/inspire/blob/main/gbm_mortality.py

[bemoody]

Project: inspire/1.2

Corrected list of references:

1   Tevis, S. E., Cobian, A. G., Truong, H. P., Craven, M. W. & Kennedy, G. D. Implications of Multiple Complications on the Postoperative Recovery of General Surgery Patients. Ann Surg 263, 1213-1218, doi:10.1097/SLA.0000000000001390 (2016).
2   Fink, A. S. et al. The National Surgical Quality Improvement Program in non-veterans administration hospitals: initial demonstration of feasibility. Ann Surg 236, 344-353; discussion 353-344, doi:10.1097/00000658-200209000-00011 (2002).
3   Liau, A., Havidich, J. E., Onega, T. & Dutton, R. P. The National Anesthesia Clinical Outcomes Registry. Anesth Analg 121, 1604-1610, doi:10.1213/ANE.0000000000000895 (2015).
4   Lee, H. C. et al. VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients. Sci Data 9, 279, doi:10.1038/s41597-022-01411-5 (2022).
5   Vistisen, S. T., Pollard, T. J., Enevoldsen, J. & Scheeren, T. W. L. VitalDB: fostering collaboration in anaesthesia research. Br J Anaesth 127, 184-187, doi:10.1016/j.bja.2021.03.011 (2021).
6   Johnson, A. E. et al. MIMIC-III, a freely accessible critical care database. Sci Data 3, 160035, doi:10.1038/sdata.2016.35 (2016).
7   Bektas, M., Tuynman, J. B., Costa Pereira, J., Burchell, G. L. & van der Peet, D. L. Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review. World J Surg 46, 3100-3110, doi:10.1007/s00268-022-06728-1 (2022).
8   Penny-Dimri, J. C. et al. Machine learning to predict adverse outcomes after cardiac surgery: A systematic review and meta-analysis. J Card Surg 37, 3838-3845, doi:10.1111/jocs.16842 (2022).
9   Senanayake, S. et al. Machine learning in predicting graft failure following kidney transplantation: A systematic review of published predictive models. Int J Med Inform 130, 103957, doi:10.1016/j.ijmedinf.2019.103957 (2019).
10  Steyerberg, E. W. & Harrell, F. E., Jr. Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol 69, 245-247, doi:10.1016/j.jclinepi.2015.04.005 (2016).
11  The Centers for Medicare and Medicaid Services and the National Center for Health Statistics, U. S. ICD-10-CM Official Guidelines for Coding and Reporting FY 2022, https://www.cms.gov/files/document/fy-2022-icd-10-cm-coding-guidelines-updated-02012022.pdf (2023).
12  Moon, T. J. Light and shadows of the Korean healthcare system. J Korean Med Sci 27 Suppl, S3-6, doi:10.3346/jkms.2012.27.S.S3 (2012).
13  A full list of the ICD-10-CM codes. https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Publications/ICD10CM/April-1-2023-Update/icd10cm-code%20descriptions-%20April%201%202023.zip
14  Sample code of machine learning model for 30-day mortality after surgery. https://github.com/vitaldb/inspire/blob/main/gbm_mortality.py

[bemoody]

Project: inspire/1.3

Corrected list of references:

1   Tevis, S. E., Cobian, A. G., Truong, H. P., Craven, M. W. & Kennedy, G. D. Implications of Multiple Complications on the Postoperative Recovery of General Surgery Patients. Ann Surg 263, 1213-1218, doi:10.1097/SLA.0000000000001390 (2016).
2   Fink, A. S. et al. The National Surgical Quality Improvement Program in non-veterans administration hospitals: initial demonstration of feasibility. Ann Surg 236, 344-353; discussion 353-344, doi:10.1097/00000658-200209000-00011 (2002).
3   Liau, A., Havidich, J. E., Onega, T. & Dutton, R. P. The National Anesthesia Clinical Outcomes Registry. Anesth Analg 121, 1604-1610, doi:10.1213/ANE.0000000000000895 (2015).
4   Lee, H. C. et al. VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients. Sci Data 9, 279, doi:10.1038/s41597-022-01411-5 (2022).
5   Vistisen, S. T., Pollard, T. J., Enevoldsen, J. & Scheeren, T. W. L. VitalDB: fostering collaboration in anaesthesia research. Br J Anaesth 127, 184-187, doi:10.1016/j.bja.2021.03.011 (2021).
6   Johnson, A. E. et al. MIMIC-III, a freely accessible critical care database. Sci Data 3, 160035, doi:10.1038/sdata.2016.35 (2016).
7   Bektas, M., Tuynman, J. B., Costa Pereira, J., Burchell, G. L. & van der Peet, D. L. Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review. World J Surg 46, 3100-3110, doi:10.1007/s00268-022-06728-1 (2022).
8   Penny-Dimri, J. C. et al. Machine learning to predict adverse outcomes after cardiac surgery: A systematic review and meta-analysis. J Card Surg 37, 3838-3845, doi:10.1111/jocs.16842 (2022).
9   Senanayake, S. et al. Machine learning in predicting graft failure following kidney transplantation: A systematic review of published predictive models. Int J Med Inform 130, 103957, doi:10.1016/j.ijmedinf.2019.103957 (2019).
10  Steyerberg, E. W. & Harrell, F. E., Jr. Prediction models need appropriate internal, internal-external, and external validation. J Clin Epidemiol 69, 245-247, doi:10.1016/j.jclinepi.2015.04.005 (2016).
11  The Centers for Medicare and Medicaid Services and the National Center for Health Statistics, U. S. ICD-10-CM Official Guidelines for Coding and Reporting FY 2022, https://www.cms.gov/files/document/fy-2022-icd-10-cm-coding-guidelines-updated-02012022.pdf (2023).
12  Moon, T. J. Light and shadows of the Korean healthcare system. J Korean Med Sci 27 Suppl, S3-6, doi:10.3346/jkms.2012.27.S.S3 (2012).
13  A full list of the ICD-10-CM codes. https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Publications/ICD10CM/April-1-2023-Update/icd10cm-code%20descriptions-%20April%201%202023.zip
14  Sample code of machine learning model for 30-day mortality after surgery. https://github.com/vitaldb/inspire/blob/main/gbm_mortality.py
15  Information of the Plasma solution A. https://www.health.kr/searchDrug/result_drug.asp?drug_cd=A11A1160A0270

[bemoody]

Project: mimic-iv-fhir/1.0/

Corrected list of references:

1   HL7 FHIR Documentation. https://www.hl7.org/fhir/ [Accessed: 30 January 2024]
2   US Core HL7 FHIR Implementation Guide. https://www.hl7.org/fhir/us/core/ [Accessed: 6 June 2022]
3   Johnson, A., Bulgarelli, L., Pollard, T., Horng, S., Celi, L. A., & Mark, R. (2023). MIMIC-IV (version 2.2). PhysioNet. https://doi.org/10.13026/6mm1-ek67.
4   Johnson, A., Bulgarelli, L., Pollard, T., Celi, L. A., Mark, R., & Horng, S. (2023). MIMIC-IV-ED (version 2.2). PhysioNet. https://doi.org/10.13026/5ntk-km72.
5   Ververs, S., Ulrich, H., Kock, A.-K., & Ingenerf, J. (2018). Konvertierung von MIMIC-III-Daten zu FHIR. Jahrestagung Der Deutschen Gesellschaft Für Medizinische Informatik, Biometrie Und Epidemiologie E.V. (GDMS). https://doi.org/10.3205/18gmds018
6   Ulrich, H., Behrend, P., Wiedekopf, J., Drenkhahn, C., Kock-Schoppenhauer, A.-K., & Ingenerf, J. (2021). Hands on the Medical Informatics Initiative Core Data Set — lessons learned from converting the mimic-IV. Studies in Health Technology and Informatics. https://doi.org/10.3233/shti210549
7   MIMIC-IV-on-FHIR Code on GitHub. https://github.com/kind-lab/mimic-fhir [Accessed: 16 Nov 2023]
8   Bennett AM; Johnson AJ. (2022). kind-lab/mimic-fhir: MIMIC-IV-on-FHIR v1.0 (v1.0). Zenodo. https://doi.org/10.5281/zenodo.6547592
9   MIMIC-FHIR Tutorial. https://github.com/kind-lab/mimic-fhir/blob/main/tutorial/mimic-fhir-tutorial-pathling.ipynb [Accessed: 6 June 2022]
10  Pathling: Advanced FHIR Analytics Server. https://pathling.csiro.au/ [Accessed: 6 June 2022]
11  MIMIC Implementation Guide. https://kind-lab.github.io/mimic-fhir/ [Accessed: 2 Jan 2024]
12  mimic-profiles on GitHub. https://github.com/kind-lab/mimic-profiles [Accessed: 2 Jan 2024]

[bemoody]

Project: orchid/2.0.0

Corrected list of references:

1   Su, X. & Zenios, S. A. Recipient Choice Can Address the Efficiency-Equity Trade-off in Kidney Transplantation: A Mechanism Design Model. Manage. Sci. 52, 1647–1660 (2006).
2   Agarwal, N., Ashlagi, I., Rees, M., Somaini, P. & Waldinger, D. Equilibrium Allocations under Alternative Waitlist Designs: Evidence from Deceased Donor Kidneys Kidneys. Econometrica 89, 37–76 (2021).
3   Berrevoets, J., Jordon, J., Bica, I., & van der Schaar, M. OrganITE: Optimal transplant donor organ offering using an individual treatment effect. Advances in neural information processing systems, 33, 20037-20050 (2020).
4   Rosenberg, P., Ciccarone, M., Seeman, B., Washer, D., Martin, G., Tse, J., & Lezama, E. Transforming Organ Donation in America. Bridgespan (2020).
5   Based on OPTN data as of January 16, 2023.
6   Alberto IR, Alberto NR, Ghosh AK, Jain B, Jayakumar S, Martinez-Martin N, McCague N, Moukheiber D, Moukheiber L, Moukheiber M, Moukheiber S. The impact of commercial health datasets on medical research and health-care algorithms. The Lancet Digital Health. 2023 May 1;5(5):e288-94.
7   Organ Procurement Organizations. Conditions for Coverage: Revisions to the Outcome Measure Requirements for Organ Procurement Organizations; Final rule. In. 42 CFR Part 486, Washington, DC, 2020.
8   Centers for Disease Control and Prevention, National Center for Health Statistics. Underlying Cause of Death 1999–2020 on CDC WONDER Online Database. http://wonder.cdc.gov/ucd-icd10.html, released in 2021. Accessed June 17, 2022.
9   Zook, M., Barocas, S., Boyd, D., Crawford, K., Keller, E., Gangadharan, S. P., ... & Pasquale, F. Ten simple rules for responsible big data research. PLoS computational biology, 13(3), e1005399 (2017).