• US Food and Drug Administration, et al. Real-World Evidence. 2022. https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence. Accessed 1 Sep 2022.

  • Wikipedia. Real world data. 2022. https://en.wikipedia.org/wiki/Real_world_data. Accessed 19 Mar 2022.

  • Powell AA, Power L, Westrop S, McOwat K, Campbell H, Simmons R, et al. Real-world data shows increased reactogenicity in adults after heterologous compared to homologous prime-boost COVID-19 vaccination, March- June 2021, England. Eurosurveillance. 2021;26(28):2100634.

    Article 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Hunter PR, Brainard JS. Estimating the effectiveness of the Pfizer COVID-19 BNT162b2 vaccine after a single dose. A reanalysis of a study of ’real-world’ vaccination outcomes from Israel. medRxiv. 2021.02.01.21250957. https://doi.org/10.1101/2021.02.01.21250957.

  • Henry DA, Jones MA, Stehlik P, Glasziou PP. Effectiveness of COVID-19 vaccines: findings from real world studies. Med J Aust. 2021;215(4):149.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Firth JA, Hellewell J, Klepac P, Kissler S, Kucharski AJ, Spurgin LG. Using a real-world network to model localized COVID-19 control strategies. Nat Med. 2020;26(10):1616–22.

    Article 
    PubMed 
    CAS 

    Google Scholar
     

  • Shapiro A, Marinsek N, Clay I, Bradshaw B, Ramirez E, Min J, et al. Characterizing COVID-19 and influenza illnesses in the real world via person-generated health data. Patterns. 2021;2(1):100188.

    Article 
    PubMed 
    CAS 

    Google Scholar
     

  • Ahrens KF, Neumann RJ, Kollmann B, Plichta MM, Lieb K, Tüscher O, et al. Differential impact of COVID-related lockdown on mental health in Germany. World Psychiatr. 2021;20(1):140.

    Article 

    Google Scholar
     

  • Hernández MA, Stolfo SJ. Real-world data is dirty: Data cleansing and the merge/purge problem. Data Min Knowl Disc. 1998;2(1):9–37.

    Article 

    Google Scholar
     

  • Corrigan-Curay J, Sacks L, Woodcock J. Real-world evidence and real-world data for evaluating drug safety and effectiveness. Jama. 2018;320(9):867–8.

    Article 
    PubMed 

    Google Scholar
     

  • Makady A, de Boer A, Hillege H, Klungel O, Goettsch W, et al. What is real-world data? A review of definitions based on literature and stakeholder interviews. Value Health. 2017;20(7):858–65.

    Article 
    PubMed 

    Google Scholar
     

  • Franklin JM, Schneeweiss S. When and how can real world data analyses substitute for randomized controlled trials? Clin Pharmacol Ther. 2017;102(6):924–33.

    Article 
    PubMed 

    Google Scholar
     

  • Miksad RA, Abernethy AP. Harnessing the power of real-world evidence (RWE): a checklist to ensure regulatory-grade data quality. Clin Pharmacol Ther. 2018;103(2):202–5.

    Article 
    PubMed 

    Google Scholar
     

  • Curtis MD, Griffith SD, Tucker M, Taylor MD, Capra WB, Carrigan G, et al. Development and validation of a high-quality composite real-world mortality endpoint. Health Serv Res. 2018;53(6):4460–76.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Booth CM, Karim S, Mackillop WJ. Real-world data: towards achieving the achievable in cancer care. Nat Rev Clin Oncol. 2019;16(5):312–25.

    Article 
    PubMed 

    Google Scholar
     

  • Swift B, Jain L, White C, Chandrasekaran V, Bhandari A, Hughes DA, et al. Innovation at the intersection of clinical trials and real-world data science to advance patient care. Clin Transl Sci. 2018;11(5):450–60.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Sun W, Cai Z, Li Y, Liu F, Fang S, Wang G. Data processing and text mining technologies on electronic medical records: a review. J Healthc Eng. 2018;2018:4302425. https://doi.org/10.1155/2018/4302425.

  • Wu J, Roy J, Stewart WF. Prediction modeling using EHR data: challenges, strategies, and a comparison of machine learning approaches. Med Care. 2010;48(6 Suppl):S106-13. https://doi.org/10.1097/MLR.0b013e3181de9e17, https://pubmed.ncbi.nlm.nih.gov/20473190/.

  • Botsis T, Hartvigsen G, Chen F, Weng C. Secondary use of EHR: data quality issues and informatics opportunities. Summit Transl Bioinforma. 2010;2010:1.


    Google Scholar
     

  • Kawaler E, Cobian A, Peissig P, Cross D, Yale S, Craven M. Learning to predict post-hospitalization VTE risk from EHR data. In: AMIA annual symposium proceedings. vol. 2012. p. 436. American Medical Informatics Association Country United States.

  • Shickel B, Tighe PJ, Bihorac A, Rashidi P. Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE J Biomed Health Inform. 2017;22(5):1589–604.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Poirier C, Hswen Y, Bouzillé G, Cuggia M, Lavenu A, Brownstein JS, et al. Influenza forecasting for French regions combining EHR, web and climatic data sources with a machine learning ensemble approach. PloS ONE. 2021;16(5):e0250890.

    Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Zheng T, Xie W, Xu L, He X, Zhang Y, You M, et al. A machine learning-based framework to identify type 2 diabetes through electronic health records. Int J Med Inform. 2017;97:120–7.

    Article 
    PubMed 

    Google Scholar
     

  • Pivovarov R, Perotte AJ, Grave E, Angiolillo J, Wiggins CH, Elhadad N. Learning probabilistic phenotypes from heterogeneous EHR data. J Biomed Inform. 2015;58:156–65.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhao D, Weng C. Combining PubMed knowledge and EHR data to develop a weighted bayesian network for pancreatic cancer prediction. J Biomed Informa. 2011;44(5):859–68.

    Article 

    Google Scholar
     

  • Veturi Y, Lucas A, Bradford Y, Hui D, Dudek S, Theusch E, et al. A unified framework identifies new links between plasma lipids and diseases from electronic medical records across large-scale cohorts. Nat Genet. 2021;53(7):972–81.

    Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Kwon BC, Choi MJ, Kim JT, Choi E, Kim YB, Kwon S, et al. Retainvis: Visual analytics with interpretable and interactive recurrent neural networks on electronic medical records. IEEE Trans Vis Comput Graph. 2018;25(1):299–309.

    Article 

    Google Scholar
     

  • Mahmoudi E, Kamdar N, Kim N, Gonzales G, Singh K, Waljee AK. Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review. BMJ. 2020;369:m958.

  • Desai RJ, Wang SV, Vaduganathan M, Evers T, Schneeweiss S. Comparison of machine learning methods with traditional models for use of administrative claims with electronic medical records to predict heart failure outcomes. JAMA Netw Open. 2020;3(1):e1918962.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Huang L, Shea AL, Qian H, Masurkar A, Deng H, Liu D. Patient clustering improves efficiency of federated machine learning to predict mortality and hospital stay time using distributed electronic medical records. J Biomed Inform. 2019;99:103291.

    Article 
    PubMed 

    Google Scholar
     

  • Bartlett VL, Dhruva SS, Shah ND, Ryan P, Ross JS. Feasibility of using real-world data to replicate clinical trial evidence. JAMA Netw Open. 2019;2(10):e1912869.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dreyer NA, Garner S. Registries for robust evidence. Jama. 2009;302(7):790–1.

    Article 
    PubMed 
    CAS 

    Google Scholar
     

  • Larsson S, Lawyer P, Garellick G, Lindahl B, Lundström M. Use of 13 disease registries in 5 countries demonstrates the potential to use outcome data to improve health care’s value. Health Affairs. 2012;31(1):220–7.

    Article 
    PubMed 

    Google Scholar
     

  • McGettigan P, Alonso Olmo C, Plueschke K, Castillon M, Nogueras Zondag D, Bahri P, et al. Patient registries: an underused resource for medicines evaluation. Drug Saf. 2019;42(11):1343–51.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Izmirly PM, Parton H, Wang L, McCune WJ, Lim SS, Drenkard C, et al. Prevalence of systemic lupus erythematosus in the United States: estimates from a meta-analysis of the Centers for Disease Control and Prevention National Lupus Registries. Arthritis Rheumatol. 2021;73(6):991–6.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Jansen-Van Der Weide MC, Gaasterland CM, Roes KC, Pontes C, Vives R, Sancho A, et al. Rare disease registries: potential applications towards impact on development of new drug treatments. Orphanet J Rare Dis. 2018;13(1):1–11.


    Google Scholar
     

  • Lacaze P, Millis N, Fookes M, Zurynski Y, Jaffe A, Bellgard M, et al. Rare disease registries: a call to action. Intern Med J. 2017;47(9):1075–9.

    Article 
    PubMed 

    Google Scholar
     

  • Gliklich RE, Dreyer NA, Leavy MB, editors. Registries for Evaluating Patient Outcomes: A User’s Guide. 3rd ed. Rockville (MD): Agency for Healthcare Research and Quality (US); 2014 Apr. Report No.: 13(14)-EHC111. PMID: 24945055.

  • Svarstad BL, Shireman TI, Sweeney J. Using drug claims data to assess the relationship of medication adherence with hospitalization and costs. Psychiatr Serv. 2001;52(6):805–11.

    Article 
    PubMed 
    CAS 

    Google Scholar
     

  • Izurieta HS, Wu X, Lu Y, Chillarige Y, Wernecke M, Lindaas A, et al. Zostavax vaccine effectiveness among US elderly using real-world evidence: Addressing unmeasured confounders by using multiple imputation after linking beneficiary surveys with Medicare claims. Pharmacoepidemiol Drug Saf. 2019;28(7):993–1001.

    Article 
    PubMed 

    Google Scholar
     

  • Allen AM, Van Houten HK, Sangaralingham LR, Talwalkar JA, McCoy RG. Healthcare cost and utilization in nonalcoholic fatty liver disease: real-world data from a large US claims database. Hepatology. 2018;68(6):2230–8.

    Article 
    PubMed 

    Google Scholar
     

  • Sruamsiri R, Iwasaki K, Tang W, Mahlich J. Persistence rates and medical costs of biological therapies for psoriasis treatment in Japan: a real-world data study using a claims database. BMC Dermatol. 2018;18(1):1–11.

    Article 

    Google Scholar
     

  • Quock TP, Yan T, Chang E, Guthrie S, Broder MS. Epidemiology of AL amyloidosis: a real-world study using US claims data. Blood Adv. 2018;2(10):1046–53.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Herland M, Bauder RA, Khoshgoftaar TM. Medical provider specialty predictions for the detection of anomalous medicare insurance claims. In: 2017 IEEE international conference on information reuse and integration (IRI). New York City: IEEE; 2017. p. 579–88.

  • Momo K, Kobayashi H, Sugiura Y, Yasu T, Koinuma M, Kuroda SI. Prevalence of drug–drug interaction in atrial fibrillation patients based on a large claims data. PLoS ONE. 2019;14(12):e0225297.

    Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Ghiani M, Maywald U, Wilke T, Heeg B. RW1 Bridging The Gap Between Clinical Trials And Real World Data: Evidence On Replicability Of Efficacy Results Using German Claims Data. Value Health. 2020;23:S757–8.

    Article 

    Google Scholar
     

  • Silverman E, Skinner J. Medicare upcoding and hospital ownership. J Health Econ. 2004;23(2):369–89.

    Article 
    PubMed 

    Google Scholar
     

  • Kirlidog M, Asuk C. A fraud detection approach with data mining in health insurance. Procedia-Soc Behav Sci. 2012;62:989–94.

    Article 

    Google Scholar
     

  • Li J, Huang KY, Jin J, Shi J. A survey on statistical methods for health care fraud detection. Health Care Manag Sci. 2008;11(3):275–87.

    Article 
    PubMed 

    Google Scholar
     

  • Viaene S, Dedene G, Derrig RA. Auto claim fraud detection using Bayesian learning neural networks. Expert Syst Appl. 2005;29(3):653–66.

    Article 

    Google Scholar
     

  • Phua C, Lee V, Smith K, Gayler R. A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119. 2010.

  • Roche N, Small M, Broomfield S, Higgins V, Pollard R. Real world COPD: association of morning symptoms with clinical and patient reported outcomes. COPD J Chronic Obstructive Pulm Dis. 2013;10(6):679–86.

    Article 

    Google Scholar
     

  • Small M, Anderson P, Vickers A, Kay S, Fermer S. Importance of inhaler-device satisfaction in asthma treatment: real-world observations of physician-observed compliance and clinical/patient-reported outcomes. Adv Ther. 2011;28(3):202–12.

    Article 
    PubMed 
    CAS 

    Google Scholar
     

  • Pinsker JE, Müller L, Constantin A, Leas S, Manning M, McElwee Malloy M, et al. Real-world patient-reported outcomes and glycemic results with initiation of control-IQ technology. Diabetes Technol Ther. 2021;23(2):120–7.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Touma Z, Hoskin B, Atkinson C, Bell D, Massey O, Lofland JH, Berry P, Karyekar CS, Costenbader KH. Systemic lupus erythematosus symptom clusters and their association with Patient‐Reported outcomes and treatment: analysis of Real‐World data. Arthritis Care & Research. 2022;74(7):1079-88.

  • Martinez GJ, Mattingly SM, Mirjafari S, Nepal SK, Campbell AT, Dey AK, et al. On the quality of real-world wearable data in a longitudinal study of information workers. In: 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). New York City: IEEE; 2020. p. 1–6.

  • Christensen JH, Saunders GH, Porsbo M, Pontoppidan NH. The everyday acoustic environment and its association with human heart rate: evidence from real-world data logging with hearing aids and wearables. Royal Soc Open Sci. 2021;8(2):201345.

    Article 

    Google Scholar
     

  • Johnson KT, Picard RW. Advancing neuroscience through wearable devices. Neuron. 2020;108(1):8–12.

    Article 
    PubMed 
    CAS 

    Google Scholar
     

  • Pickham D, Berte N, Pihulic M, Valdez A, Mayer B, Desai M. Effect of a wearable patient sensor on care delivery for preventing pressure injuries in acutely ill adults: A pragmatic randomized clinical trial (LS-HAPI study). Int J Nurs Stud. 2018;80:12–9.

    Article 
    PubMed 

    Google Scholar
     

  • Adams JL, Dinesh K, Snyder CW, Xiong M, Tarolli CG, Sharma S, et al. A real-world study of wearable sensors in Parkinson’s disease. NPJ Park Dis. 2021;7(1):1–8.


    Google Scholar
     

  • Hernán MA, Robins JM, et al. Per-protocol analyses of pragmatic trials. N Engl J Med. 2017;377(14):1391–8.

    Article 
    PubMed 

    Google Scholar
     

  • Murray EJ, Swanson SA, Hernán MA. Guidelines for estimating causal effects in pragmatic randomized trials. arXiv preprint arXiv:1911.06030. 2019.

  • Hernandez AF, Fleurence RL, Rothman RL. The ADAPTABLE Trial and PCORnet: shining light on a new research paradigm. Ann Intern Med. 2015;163(8):635-6.

  • Baigent C. Pragmatic trials-need for ADAPTABLE design. N Engl J Med. 2021;384(21).

  • Hernán MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not available. Am J Epidemiol. 2016;183(8):758–64.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ioannou GN, Locke ER, O’Hare AM, Bohnert AS, Boyko EJ, Hynes DM, et al. COVID-19 vaccination effectiveness against infection or death in a National US Health Care system: a target trial emulation study. Ann Intern Med. 2022;175(3):352–61.

    Article 
    PubMed 

    Google Scholar
     

  • García-Albéniz X, Hsu J, Hernán MA. The value of explicitly emulating a target trial when using real world evidence: an application to colorectal cancer screening. Eur J Epidemiol. 2017;32(6):495–500.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Takeuchi Y, Kumamaru H, Hagiwara Y, Matsui H, Yasunaga H, Miyata H, et al. Sodium-glucose cotransporter-2 inhibitors and the risk of urinary tract infection among diabetic patients in Japan: Target trial emulation using a nationwide administrative claims database. Diabetes Obes Metab. 2021;23(6):1379–88.

    Article 
    PubMed 
    CAS 

    Google Scholar
     

  • Jen EY, Xu Q, Schetter A, Przepiorka D, Shen YL, Roscoe D, et al. FDA approval: blinatumomab for patients with B-cell precursor acute lymphoblastic leukemia in morphologic remission with minimal residual disease. Clin Cancer Res. 2019;25(2):473–7.

    Article 
    PubMed 
    CAS 

    Google Scholar
     

  • Gross AM. Using real world data to support regulatory approval of drugs in rare diseases: A review of opportunities, limitations & a case example. Curr Probl Cancer. 2021;45(4):100769.

    Article 
    PubMed 

    Google Scholar
     

  • Wu J, Wang C, Toh S, Pisa FE, Bauer L. Use of real-world evidence in regulatory decisions for rare diseases in the United States—Current status and future directions. Pharmacoepidemiol Drug Saf. 2020;29(10):1213–8.

    Article 
    PubMed 

    Google Scholar
     

  • Hayeems RZ, Michaels-Igbokwe C, Venkataramanan V, Hartley T, Acker M, Gillespie M, et al. The complexity of diagnosing rare disease: An organizing framework for outcomes research and health economics based on real-world evidence. Genet Med. 2022;24(3):694–702.

    Article 
    PubMed 

    Google Scholar
     

  • Hernán MA, Robins JM. Causal inference. Boca Raton: CRC; 2010.


    Google Scholar
     

  • Ho M, van der Laan M, Lee H, Chen J, Lee K, Fang Y, et al. The current landscape in biostatistics of real-world data and evidence: Causal inference frameworks for study design and analysis. Stat Biopharm Res. 2021. https://www.tandfonline.com/doi/abs/10.1080/19466315.2021.1883475.

  • Crown WH. Real-world evidence, causal inference, and machine learning. Value Health. 2019;22(5):587–92.

    Article 
    PubMed 

    Google Scholar
     

  • Cui P, Shen Z, Li S, Yao L, Li Y, Chu Z, et al. Causal inference meets machine learning. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York: Association for Computing Machinery; 2020. p. 3527–3528.

  • Xiong HY, Alipanahi B, Lee LJ, Bretschneider H, Merico D, Yuen RK, Hua Y, Gueroussov S, Najafabadi HS, Hughes TR, Morris Q. The human splicing code reveals new insights into the genetic determinants of disease. Science. 2015;347(6218):1254806.

  • Quang D, Chen Y, Xie X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics. 2015;31(5):761–3.

    Article 
    PubMed 
    CAS 

    Google Scholar
     

  • Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S. Lung pattern classification for interstitial lung diseases using a deep convolutional neural network. IEEE Trans Med Imaging. 2016;35(5):1207–16.

    Article 
    PubMed 

    Google Scholar
     

  • Van Grinsven MJ, van Ginneken B, Hoyng CB, Theelen T, Sánchez CI. Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images. IEEE Trans Med Imaging. 2016;35(5):1273–84.

    Article 
    PubMed 

    Google Scholar
     

  • Kleesiek J, Urban G, Hubert A, Schwarz D, Maier-Hein K, Bendszus M, et al. Deep MRI brain extraction: A 3D convolutional neural network for skull stripping. NeuroImage. 2016;129:460–9.

    Article 
    PubMed 

    Google Scholar
     

  • Gibson E, Li W, Sudre C, Fidon L, Shakir DI, Wang G, et al. NiftyNet: a deep-learning platform for medical imaging. Comput Methods Prog Biomed. 2018;158:113–22.

    Article 

    Google Scholar
     

  • Coccia M. Deep learning technology for improving cancer care in society: New directions in cancer imaging driven by artificial intelligence. Technol Soc. 2020;60:101198.

    Article 

    Google Scholar
     

  • Bien N, Rajpurkar P, Ball RL, Irvin J, Park A, Jones E, et al. Deep-learning-assisted diagnosis for knee magnetic resonance imaging: development and retrospective validation of MRNet. PLoS Med. 2018;15(11):e1002699.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Johansson FD, Collins JE, Yau V, Guan H, Kim SC, Losina E, et al. Predicting response to tocilizumab monotherapy in rheumatoid arthritis: a real-world data analysis using machine learning. J Rheumatol. 2021;48(9):1364–70.

    Article 
    PubMed 
    CAS 

    Google Scholar
     

  • Ravì D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, et al. Deep learning for health informatics. IEEE J Biomed Health Informa. 2016;21(1):4–21.

    Article 

    Google Scholar
     

  • Suzuki K. Overview of deep learning in medical imaging. Radiol Phys Technol. 2017;10(3):257–73.

    Article 
    PubMed 

    Google Scholar
     

  • Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2017;19:221–48.

    Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88.

    Article 
    PubMed 

    Google Scholar
     

  • Lee JG, Jun S, Cho YW, Lee H, Kim GB, Seo JB, et al. Deep learning in medical imaging: general overview. Korean J Radiol. 2017;18(4):570–84.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Amyar A, Modzelewski R, Li H, Ruan S. Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation. Comput Biol Med. 2020;126:104037.

    Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Oh Y, Park S, Ye JC. Deep learning covid-19 features on cxr using limited training data sets. IEEE Trans Med Imaging. 2020;39(8):2688–700.

    Article 
    PubMed 

    Google Scholar
     

  • Hemdan EED, Shouman MA, Karar ME. Covidx-net: A framework of deep learning classifiers to diagnose covid-19 in x-ray images. arXiv preprint arXiv:2003.11055. 2020.

  • Wang S, Zha Y, Li W, Wu Q, Li X, Niu M, et al. A fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis. Eur Respir J. 2020;56(2).

  • Ardakani AA, Kanafi AR, Acharya UR, Khadem N, Mohammadi A. Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: Results of 10 convolutional neural networks. Comput Biol Med. 2020;121:103795.

    Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • Food U, Administration D. Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device(SaMD) – Discussion Paper and Request for Feedback. 2019. https://www.fda.gov/files/medical%20devices/published/US-FDA-Artificial-Intelligence-and-Machine-Learning-Discussion-Paper.pdf. Accessed 24 Mar 2022.

  • Food U, Administration D. Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan. 2021. https://www.fda.gov/media/145022/download. Accessed 24 March 2022.

  • of Medicines Regulatory Authorities IC. Informal Innovation Network Horizon Scanning Assessment Report – Artificial Intelligence. 2021. https://www.icmra.info/drupal/sites/default/files/2021-08/horizon_scanning_report_artificial_intelligence.pdf. Accessed 24 March 2022.

  • Agency EM. Artificial intelligence in medicine regulation. 2021. https://www.ema.europa.eu/en/news/artificial-intelligence-medicine-regulation. Accessed 24 Mar 2022.

  • Van der Laan MJ, Rose S. Targeted learning: causal inference for observational and experimental data. 2011. Springer-Verlag New York Inc., United States.

  • Van der Laan MJ, Rose S. Targeted learning in data science. Causal Inference for Complex Longitudinal Studies 2018. Cham: Springer.

  • van der Laan MJ, Luedtke AR. Targeted learning of the mean outcome under an optimal dynamic treatment rule. J Causal Infer. 2015;3(1):61–95.

    Article 

    Google Scholar
     

  • Sofrygin O, Zhu Z, Schmittdiel JA, Adams AS, Grant RW, van der Laan MJ, et al. Targeted learning with daily EHR data. Stat Med. 2019;38(16):3073–90.

    Article 
    PubMed 

    Google Scholar
     

  • Chakravarti P, Wilson A, Krikov S, Shao N, van der Laan M. PIN68 Estimating Effects in Observational Real-World Data, From Target Trials to Targeted Learning: Example of Treating COVID-Hospitalized Patients. Value Health. 2021;24:S118.

    Article 

    Google Scholar
     

  • Eichler HG, Koenig F, Arlett P, Enzmann H, Humphreys A, Pétavy F, et al. Are novel, nonrandomized analytic methods fit for decision making? The need for prospective, controlled, and transparent validation. Clin Pharmacol Ther. 2020;107(4):773–9.

    Article 
    PubMed 

    Google Scholar
     

  • Chakraborty S, Tomsett R, Raghavendra R, Harborne D, Alzantot M, Cerutti F, et al. Interpretability of deep learning models: A survey of results. In: 2017 IEEE smartworld, ubiquitous intelligence & computing, advanced & trusted computed, scalable computing & communications, cloud & big data computing, Internet of people and smart city innovation. New York City: IEEE; 2017. p. 1–6.

  • Zhang Q, Zhu SC. Visual interpretability for deep learning: a survey. arXiv preprint arXiv:1802.00614. 2018.

  • Hohman F, Park H, Robinson C, Chau DHP. Summit: Scaling deep learning interpretability by visualizing activation and attribution summarizations. IEEE Trans Vis Comput Graph. 2019;26(1):1096–106.

    Article 
    PubMed 

    Google Scholar
     

  • Ghoshal B, Tucker A. Estimating uncertainty and interpretability in deep learning for coronavirus (COVID-19) detection. arXiv preprint arXiv:2003.10769. 2020.

  • Raghu M, Gilmer J, Yosinski J, Sohl-Dickstein J. Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability. 2017; 31st Conference on Neural Information Processing Systems (NIPS 2017). Long Beach: NEURAL INFO PROCESS SYS F, LA JOLLA; 2017. ISBN: 9781510860964.

  • Cruz-Roa AA, Ovalle JEA, Madabhushi A, Osorio FAG. A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham; Springer; 2013. p. 403–410.

  • Barba LA. Terminologies for reproducible research. arXiv preprint arXiv:1802.03311. 2018.

  • Stupple A, Singerman D, Celi LA. The reproducibility crisis in the age of digital medicine. NPJ Digit Med. 2019;2(1):1–3.


    Google Scholar
     

  • Carter RE, Attia ZI, Lopez-Jimenez F, Friedman PA. Pragmatic considerations for fostering reproducible research in artificial intelligence. NPJ Digit Med. 2019;2(1):1–3.

    Article 

    Google Scholar
     

  • Liu C, Gao C, Xia X, Lo D, Grundy J, Yang X. On the replicability and reproducibility of deep learning in software engineering. ACM Transactions on Software Engineering and Methodology. 2021;31(1):1–46.

  • Springate DA, Kontopantelis E, Ashcroft DM, Olier I, Parisi R, Chamapiwa E, et al. ClinicalCodes: an online clinical codes repository to improve the validity and reproducibility of research using electronic medical records. PloS ONE. 2014;9(6):e99825.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wang SV, Schneeweiss S, Berger ML, Brown J, de Vries F, Douglas I, et al. Reporting to improve reproducibility and facilitate validity assessment for healthcare database studies V1. 0. Value health. 2017;20(8):1009–22.

    Article 
    PubMed 

    Google Scholar
     

  • Panagiotou OA, Heller R. Inferential challenges for real-world evidence in the era of routinely collected health data: many researchers, many more hypotheses, a single database. JAMA Oncol. 2021;7(11):1605–7.

    Article 
    PubMed 

    Google Scholar
     

  • Belbasis L, Panagiotou OA. Reproducibility of prediction models in health services research. BMC Res Notes. 2022;15(1):1–5.

    Article 

    Google Scholar
     

  • Dwork C, McSherry F, Nissim K, Smith A. Calibrating noise to sensitivity in private data analysis. In: Theory of cryptography conference. Springer; 2006. p. 265–284.

  • Konečnỳ J, McMahan B, Ramage D. Federated optimization: Distributed optimization beyond the datacenter. arXiv preprint arXiv:1511.03575. 2015.

  • Konečnỳ J, McMahan HB, Yu FX, Richtárik P, Suresh AT, Bacon D. Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492. 2016.

  • McCradden MD, Joshi S, Mazwi M, Anderson JA. Ethical limitations of algorithmic fairness solutions in health care machine learning. Lancet Digit Health. 2020;2(5):e221–3.

    Article 
    PubMed 

    Google Scholar
     

  • Mitchell S, Potash E, Barocas S, D’Amour A, Lum K. Algorithmic fairness: Choices, assumptions, and definitions. Ann Rev Stat Appl. 2021;8:141–63.

    Article 

    Google Scholar
     

  • Mhasawade V, Zhao Y, Chunara R. Machine learning and algorithmic fairness in public and population health. Nat Mach Intell. 2021;3(8):659–66.

    Article 

    Google Scholar
     

  • Wong PH. Democratizing algorithmic fairness. Philos Technol. 2020;33(2):225–44.

    Article 

    Google Scholar
     

  • Paulus JK, Kent DM. Predictably unequal: understanding and addressing concerns that algorithmic clinical prediction may increase health disparities. NPJ Digit Med. 2020;3(1):1–8.

    Article 

    Google Scholar
     

  • Orsini LS, Berger M, Crown W, Daniel G, Eichler HG, Goettsch W, et al. Improving transparency to build trust in real-world secondary data studies for hypothesis testing—why, what, and how: recommendations and a road map from the real-world evidence transparency initiative. Value Health. 2020;23(9):1128–36.

    Article 
    PubMed 

    Google Scholar
     

  • Patorno E, Schneeweiss S, Wang SV. Transparency in real-world evidence (RWE) studies to build confidence for decision-making: reporting RWE research in diabetes. Diabetes Obes Metab. 2020;22:45–59.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • White R. Building trust in real-world evidence and comparative effectiveness research: the need for transparency. Future Med. 2017;6(1):5–7.


    Google Scholar
     

  • Rodriguez-Villa E, Torous J. Regulating digital health technologies with transparency: the case for dynamic and multi-stakeholder evaluation. BMC Med. 2019;17(1):1–5.

    Article 

    Google Scholar
     

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