1. Beck CT. A meta-synthesis of qualitative research. MCN: The American Journal of Maternal/Child Nursing. 2002;27(4):214-21.
2. Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. the Journal of machine Learning research. 2003;3:993-1022.
3. Rowley J, Slack F. Conducting a literature review. Management research news. 2004.
4. Grimmer J. A Bayesian hierarchical topic model for political texts: Measuring expressed agendas in Senate press releases. Political Analysis. 2010;18(1):1-35.
5. Quinn KM, Monroe BL, Colaresi M, Crespin MH, Radev DR. How to analyze political attention with minimal assumptions and costs. American Journal of Political Science. 2010;54(1):209-28.
6. Rozas LW, Klein WC. The value and purpose of the traditional qualitative literature review. Journal of evidence-based social work. 2010;7(5):387-99.
7. Mimno D, Blei D, editors. Bayesian checking for topic models. Proceedings of the 2011 conference on empirical methods in natural language processing; 2011.
8. Blei DM. Probabilistic topic models. Communications of the ACM. 2012;55(4):77-84.
9. DiMaggio P, Nag M, Blei D. Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of US government arts funding. Poetics. 2013;41(6):570-606.
10. Grimmer J, Stewart BM. Text as data: The promise and pitfalls of automatic content analysis methods for political texts. Political analysis. 2013;21(3):267-97.
11. Koltsova O, Koltcov S. Mapping the public agenda with topic modeling: The case of the Russian livejournal. Policy & Internet. 2013;5(2):207-27.
12. Ouhbi S, Idri A, Fernández-Alemán JL, Toval A. Requirements engineering education: a systematic mapping study. Requirements Engineering. 2015;20(2):119-38.
13. Greene D, Cross JP. Exploring the political agenda of the european parliament using a dynamic topic modeling approach. Political Analysis. 2017;25(1):77-94.
14. Székely N, Vom Brocke J. What can we learn from corporate sustainability reporting? Deriving propositions for research and practice from over 9,500 corporate sustainability reports published between 1999 and 2015 using topic modelling technique. PloS one. 2017;12(4):e0174807.
15. Abuhay TM, Kovalchuk SV, Bochenina K, Mbogo G-K, Visheratin AA, Kampis G, et al. Analysis of publication activity of computational science society in 2001–2017 using topic modelling and graph theory. Journal of computational science. 2018;26:193-204.
16. Li S, Wang H. Traditional literature review and research synthesis. The Palgrave handbook of applied linguistics research methodology. 2018:123-44.
17. Maier D, Waldherr A, Miltner P, Wiedemann G, Niekler A, Keinert A, et al. Applying LDA topic modeling in communication research: Toward a valid and reliable methodology. Communication Methods and Measures. 2018;12(2-3):93-118.
18. Behera RK, Bala PK, Dhir A. The emerging role of cognitive computing in healthcare: a systematic literature review. International journal of medical informatics. 2019;129:154-66.
19. Dias R, Torkamani A. Artificial intelligence in clinical and genomic diagnostics. Genome medicine. 2019;11(1):1-12.
20. Magrabi F, Ammenwerth E, McNair JB, De Keizer NF, Hyppönen H, Nykänen P, et al. Artificial intelligence in clinical decision support: challenges for evaluating AI and practical implications. Yearbook of medical informatics. 2019;28(01):128-34.
21. Shah P, Kendall F, Khozin S, Goosen R, Hu J, Laramie J, et al. Artificial intelligence and machine learning in clinical development: a translational perspective. NPJ digital medicine. 2019;2(1):1-5.
22. Mårtensson G, Ferreira D, Granberg T, Cavallin L, Oppedal K, Padovani A, et al. The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study. Medical Image Analysis. 2020;66:101714.
23. Spasic I, Nenadic G. Clinical text data in machine learning: systematic review. JMIR medical informatics. 2020;8(3):e17984.
24. Weng W-H. Machine learning for clinical predictive analytics. Leveraging Data Science for Global Health: Springer, Cham; 2020. p. 199-217.
25. Adlung L, Cohen Y, Mor U, Elinav E. Machine learning in clinical decision making. Med. 2021.
26. Chang C-H, Lin C-H, Lane H-Y. Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer’s Disease. International Journal of Molecular Sciences. 2021;22(5):2761.
27. Connor KL, O’Sullivan ED, Marson LP, Wigmore SJ, Harrison EM. The Future Role of Machine Learning in Clinical Transplantation. Transplantation. 2021;105(4):723-35.
28. Hassan N, Slight R, Weiand D, Vellinga A, Morgan G, Aboushareb F, et al. Preventing sepsis; how can artificial intelligence inform the clinical decision-making process? A systematic review. International Journal of Medical Informatics. 2021:104457.
29. Kushwaha AK, Kar AK, Dwivedi YK. Applications of big data in emerging management disciplines: A literature review using text mining. International Journal of Information Management Data Insights. 2021;1(2):100017.
30. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. Bmj. 2021;372.