Page 57 - AIH-1-3
P. 57

Artificial Intelligence in Health                                  Predicting mortality in COVID-19 using ML



            12.  Zhou H, Ji J, Chen X,  et al. Identification of novel bat   23.  Goumenou M, Sarigiannis D, Tsatsakis A, et al. COVID19 in
               coronaviruses sheds light on the evolutionary origins of SARS-  Northern Italy: An integrative overview of factors possibly
               CoV-2 and related viruses. Cell. 2021;184(17):4380-4391.e14.  influencing the sharp increase of the outbreak (Review). Mol
                                                                  Med Rep. 2020;22:20-32.
               doi: 10.1016/j.cell.2021.06.008
                                                                  doi: 10.3892/mmr.2020.11079
            13.  Wacharapluesadee S, Tan CW, Maneeorn P, et al. Evidence for
               SARS-CoV-2 related coronaviruses circulating in bats and   24.  Brake SJ, Barnsley  K, Lu  W, McAlinden KD, Eapen MS,
               pangolins in Southeast Asia. Nat Commun. 2021;12(1):972.  Sohal SS. smoking upregulates angiotensin-converting enzyme-2
                                                                  receptor: A potential adhesion site for novel coronavirus SARS-
               doi: 10.1038/s41467-021-21240-1
                                                                  CoV-2 (Covid-19). J Clin Med. 2020;9(3):841.
            14.  Mitchell  TM.  Machine  Learning.  McGraw-Hill  Science/
               Engineering/Math;  1997. Available  from: https://www.     doi: 10.3390/jcm9030841
               cin.ufpe.br/~cavmj/Machine%20-%20Learning%20-%20  25.  Lewis T. Smoking or Vaping May Increase the Risk of a Severe
               Tom%20Mitchell.pdf [Last accessed on 2023 Dec 18].  Coronavirus Infection. Scientific American; 2020. Available
                                                                  from: https://www.scientificamerican.com/article/smoking-
            15.  Zoabi Y, Deri-Rozov S, Shomron N. Machine learning-based
               prediction of COVID-19 diagnosis based on symptoms. NPJ   or-vaping-may-increase-the-risk-of-a-severe-coronavirus-
               Digit Med. 2021;4(1):3.                            infection1 [Last accessed on 2023 Dec 18].
                                                               26.  Datos Abiertos Dirección General de Epidemiología.
               doi: 10.1038/s41746-020-00372-6
                                                                  Secretaría de Salud. Gobierno. Cobierno de Mexico; 2023.
            16.  Aljameel  SS,  Khan  IU,  Aslam  N,  Aljabri  M,  Alsulmi  ES.   Available from: https://www.gob.mx/salud/documentos/
               Machine learning-based model to predict the disease   datos-abiertos-152127 [Last accessed on 2023 Dec 18].
               severity and outcome in COVID-19 patients. Sci Program.   27.  Cramer JS. The origins of logistic regression. SSRN Electron
               2021;2021:1-10.
                                                                  J. 2005.
               doi: 10.1155/2021/5587188
                                                                  doi: 10.2139/ssrn.360300
            17.  Mullick B, Magar R, Jhunjhunwala A, Barati Farimani A.
               Understanding mutation hotspots for the SARS-CoV-2 spike   28.  Logistic Regression in Machine Learning  -  Javatpoint; 2021.
               protein  using  Shannon Entropy and  K-means  clustering.   Available from: https://www.javatpoint.com/logistic-regression-
               Comput Biol Med. 2021;138:104915.                  in-machine-learning [Last accessed on 2023 Dec 18].
                                                               29.  Utgoff  PE.  Incremental  induction  of  decision  trees.  Mach
               doi: 10.1016/j.compbiomed.2021.104915
                                                                  Learn. 1989;4(2):161-186.
            18.  Ozger ZB, Cihan P. A  novel ensemble fuzzy classification
               model in SARS-CoV-2 B-cell epitope identification for   30.  Kotsiantis S. Decision trees: A recent overview. Artif Intell
               development of protein-based vaccine.  Appl Soft Comput.   Rev. 2013;39(4):261-283.
               2022;116:108280.                                   doi: 10.1007/s10462-011-9272-4
               doi: 10.1016/j.asoc.2021.108280                 31.  Machine Learning Random Forest Algorithm  -  Javatpoint;
                                                                  2021.    https://www.javatpoint.com/machine-learning-
            19.  People with Certain Medical Conditions. Centers for Disease
               Control and Prevention; 2023. Available from: https://www.  random-forest-algorithm [Last accessed on 2023 Dec 18].
               cdc.gov/coronavirus/2019-ncov/need-extra-precautions/  32.  Chen T, Guestrin C. XGBoost: A  Scalable Tree Boosting
               people-with-medical-conditions.html  [Last  accessed  on   System. In:  Proceedings of the 22   ACM SIGKDD
                                                                                               nd
               2023 Dec 18].                                      International Conference on Knowledge Discovery and Data
                                                                  Mining. ACM; 2016. p. 785-794.
            20.  Yang X, Yu Y, Xu J,  et al. Clinical course and outcomes
               of critically ill  patients with  SARS-CoV-2  pneumonia      doi: 10.1145/2939672.2939785
               in  Wuhan,  China:  A  single-centered,  retrospective,   33.  Beale R, Jackson T.  Neural Computing: An Introduction.
               observational study. Lancet Respir Med. 2020;8(5):475-481.
                                                                  England: Adam Hilger; 1990.
               doi: 10.1016/S2213-2600(20)30079-5
                                                                  doi: 10.1887/0852742622
            21.  Guan WJ, Ni ZY, Hu Y,  et al. Clinical characteristics   34.  Bezdek JC. On the relationship between neural networks,
               of coronavirus disease 2019 in China.  N  Engl J Med.   pattern recognition and intelligence.  Int J Approx Reason.
               2020;382(18):1708-1720.
                                                                  1992;6(2):85-107.
               doi: 10.1056/NEJMoa2002032
                                                                  doi: 10.1016/0888-613X(92)90013-P
            22.  Cakir Edis E. Chronic pulmonary diseases and COVID-19.   35.  Fix E, Hodges JL. Discriminatory analysis. Nonparametric
               Turk Thorac J. 2020;21(5):345-349.
                                                                  discrimination: Consistency properties. Int Stat Rev/Rev Int
               doi: 10.5152/TurkThoracJ.2020.20091                Stat. 1989;57(3):238.


            Volume 1 Issue 3 (2024)                         51                               doi: 10.36922/aih.2591
   52   53   54   55   56   57   58   59   60   61   62