Machine Learning and Predicting Clinical Outcomes

Chandrani Kumari

dc.contributor.author	Chandrani Kumari
dc.date.accessioned	2025-04-11T08:32:04Z
dc.date.available	2025-04-11T08:32:04Z
dc.date.issued	2025
dc.date.submitted	2025-02-28
dc.identifier.uri	https://dspace.imsc.res.in/xmlui/handle/123456789/895
dc.description.tableofcontents	1. Introduction – 15 1.1 AI in the Healthcare Sector – 16 1.2 Designing Machine Learning Models – 19 • 1.2.1 Data Preprocessing – 19 • 1.2.2 Model Development – 23 • 1.2.3 Evaluation Metrics – 23 1.3 Progress, Challenges, and Opportunities for AI in Healthcare – 28 • 1.3.1 Challenges for AI in Healthcare – 28 • 1.3.2 Opportunities for AI in Healthcare – 31 2. Machine Learning Algorithms – 33 2.1 Supervised Learning – 34 • 2.1.1 k-Nearest Neighbors (k-NN) – 34 • 2.1.2 Linear Regression – 35 • 2.1.3 Logistic Regression – 35 • 2.1.4 Naive Bayes – 36 • 2.1.5 Decision Tree – 38 • 2.1.6 Support Vector Machine – 39 2.2 Unsupervised Learning – 39 • 2.2.1 K-Means – 40 • 2.2.2 Hierarchical Clustering – 41 • 2.2.3 DBSCAN – 42 • 2.2.4 Gaussian Mixture Model (GMM) – 43 • 2.2.5 Principal Component Analysis (PCA) – 44 • 2.2.6 Other Dimensionality Reduction Techniques – 45 2.3 Ensemble Models – 45 • 2.3.1 Boosting – 46 • 2.3.2 Bagging – 47 2.4 Neural Networks – 48 2.5 Reinforcement Learning – 49 2.6 Clinical Study Design and Evaluation Metrics – 50 3. A Predictive Model of a Growing Fetus: The Gompertz Model – 52 3.1 Introduction – 52 3.2 Materials and Methods – 56 • 3.2.1 Study Design and Participants – 56 • 3.2.2 Published Growth Standards for Fetal Biometry – 58 • 3.2.3 Gompertz Growth Model – 59 • 3.2.4 Quality of Fit – 61 • 3.2.5 Predicting Birth Weight of Fetus – 61 • 3.2.6 Validation Cohort – 63 3.3 Results – 64 • 3.3.1 Gompertz Formula Fits Biometry Measurements (Seethapathy Cohort) – 64 • 3.3.2 Neonatal Complications and Deviations from Gompertz Growth – 65 • 3.3.3 Predicting Final Biometry and Individual Birth Weights – 66 • 3.3.4 Model Performance on Validation Cohort – 70 • 3.3.5 Gompertz Equation and Fetal Growth Standards – 71 3.4 Conclusion – 76 4. MMM and MMMSynth: Clustering and Synthetic Data Generation – 79 4.1 Introduction – 79 • 4.1.1 Existing Clustering Methods – 80 • 4.1.2 Overview of the MMM Algorithm – 81 • 4.1.3 MMMSynth: Generating Synthetic Tabular Data – 82 4.2 Methods – 83 • 4.2.1 MMM: Clustering of Heterogeneous Data – 83 • 4.2.2 Discrete Data, Dirichlet Prior – 83 • 4.2.3 Continuous Data, Normal-Gamma Prior – 85 • 4.2.4 Optimizing Clustering Likelihood (Expectation Maximization) – 87 • 4.2.5 Identifying the Correct K: Marginal Likelihood – 89 • 4.2.6 Assessment of Benchmarking – 95 • 4.2.7 MMMSynth: Generating Synthetic Data with MMM – 96 • 4.2.8 Benchmarks: Real Datasets Used – 97 4.3 Results – 99 • 4.3.1 Clustering Algorithm Performance – 99 4.4 Conclusion – 106 5. Other Machine Learning Projects – 109 5.1 Assessing Vasoplegia Grouping using ML – 109 5.2 DREAM Preterm Birth Prediction Challenge (Transcriptomics) – 116 5.3 RSNA-MICCAI Brain Tumor Radiogenomic Classification – 117 6. Conclusion – 119	en_US
dc.publisher.publisher	IMSc
dc.relation.isbasedon	[1] C. Kumari, G. I. Menon, L. Narlikar, U. Ram, and R. Siddharthan, “Accurate birth weight prediction from fetal biometry using the gompertz model,” European Journal of Obstetrics & Gynecology and Reproductive Biology: X (2024) 100344. [2] C. Kumari and R. Siddharthan, “Mmm and mmmsynth: Clustering of heterogeneous tabular data, and synthetic data generation,” Plos one 19 no. 4, (2024) e0302271. [3] S. Nandi, U. R. Potunuru, C. Kumari, A. A. Nathan, J. Gopal, G. I. Menon, R. Siddharthan, M. Dixit, and P. R. Thangaraj, “Altered kinetics of circulating progenitor cells in cardiopulmonary bypass (cpb) associated vasoplegic patients: A pilot study,” Plos one 15 no. 11, (2020) e0242375. [4] A. L. Tarca, B. A. Pataki, R. Romero, M. Sirota, Y. Guan, R. Kutum, N. Gomez-Lopez, B. Done, G. Bhatti, T. Yu, et al., “Crowdsourcing assessment of maternal blood multi-omics for predicting gestational age and preterm birth,” Cell Reports Medicine 2 no. 6, (2021) . [5] A. M. Rahmani, E. Yousefpoor, M. S. Yousefpoor, Z. Mehmood, A. Haider, M. Hosseinzadeh, and R. Ali Naqvi, “Machine learning (ml) in medicine: Review, applications, and challenges,” Mathematics 9 no. 22, (2021) 2970. 121[6] P. Rajpurkar, E. Chen, O. Banerjee, and E. J. Topol, “Ai in health and medicine,” Nature medicine 28 no. 1, (2022) 31–38. [7] P. Pattnayak and A. R. Panda, “Innovation on machine learning in healthcare services—an introduction,” Technical Advancements of Machine Learning in Healthcare (2021) 1–30. [8] T. Mitchell, “Machine learning,” Publisher: McGraw Hill (1997) . [9] E. Elsebakhi, F. Lee, E. Schendel, A. Haque, N. Kathireason, T. Pathare, N. Syed, and R. Al-Ali, “Large-scale machine learning based on functional networks for biomedical big data with high performance computing platforms,” Journal of Computational Science 11 (2015) 69–81. [10] A. M. Rahmani, S. Ali, M. S. Yousefpoor, E. Yousefpoor, R. A. Naqvi, K. Siddique, and M. Hosseinzadeh, “An area coverage scheme based on fuzzy logic and shu%ed frog-leaping algorithm (sfla) in heterogeneous wireless sensor networks,” Mathematics 9 no. 18, (2021) 2251. [11] D. V. Dimitrov, “Medical internet of things and big data in healthcare,” Healthcare informatics research 22 no. 3, (2016) 156–163. [12] M. Rana and M. Bhushan, “Machine learning and deep learning approach for medical image analysis: diagnosis to detection,” Multimedia Tools and Applications (2022) 1–39. [13] S. Hussain, I. Mubeen, N. Ullah, S. S. U. D. Shah, B. A. Khan, M. Zahoor, R. Ullah, F. A. Khan, and M. A. Sultan, “Modern diagnostic imaging technique applications and risk factors in the medical field: A review,” BioMed Research International 2022 (2022) . [14] K. B. Johnson, W.-Q. Wei, D. Weeraratne, M. E. Frisse, K. Misulis, K. Rhee, J. Zhao, and J. L. Snowdon, “Precision medicine, ai, and the future 122of personalized health care,” Clinical and translational science 14 no. 1, (2021) 86–93. [15] H. Fröhlich, R. Balling, N. Beerenwinkel, O. Kohlbacher, S. Kumar, T. Lengauer, M. H. Maathuis, Y. Moreau, S. A. Murphy, T. M. Przytycka, et al., “From hype to reality: data science enabling personalized medicine,” BMC medicine 16 no. 1, (2018) 1–15. [16] K. Kreimeyer, M. Foster, A. Pandey, N. Arya, G. Halford, S. F. Jones, R. Forshee, M. Walderhaug, and T. Botsis, “Natural language processing systems for capturing and standardizing unstructured clinical information: a systematic review,” Journal of biomedical informatics 73 (2017) 14–29. [17] R. Gupta, D. Srivastava, M. Sahu, S. Tiwari, R. K. Ambasta, and P. Kumar, “Artificial intelligence to deep learning: machine intelligence approach for drug discovery,” Molecular diversity 25 (2021) 1315–1360. [18] I. Mandal, “Machine learning algorithms for the creation of clinical healthcare enterprise systems,” Enterprise Information Systems 11 no. 9, (2017) 1374–1400. [19] U. Schmidt-Erfurth, A. Sadeghipour, B. S. Gerendas, S. M. Waldstein, and H. Bogunović, “Artificial intelligence in retina,” Progress in retinal and eye research 67 (2018) 1–29. [20] S. J. Adams, R. D. Henderson, X. Yi, and P. Babyn, “Artificial intelligence solutions for analysis of x-ray images,” Canadian Association of Radiologists Journal 72 no. 1, (2021) 60–72. [21] F. M. De La Vega, S. Chowdhury, B. Moore, E. Frise, J. McCarthy, E. J. Hernandez, T. Wong, K. James, L. Guidugli, P. B. Agrawal, et al., “Artificial intelligence enables comprehensive genome interpretation and nomination of 123candidate diagnoses for rare genetic diseases,” Genome Medicine 13 (2021) 1–19. [22] M. Moor, O. Banerjee, Z. S. H. Abad, H. M. Krumholz, J. Leskovec, E. J. Topol, and P. Rajpurkar, “Foundation models for generalist medical artificial intelligence,” Nature 616 no. 7956, (2023) 259–265. [23] M. M. Li, K. Huang, and M. Zitnik, “Graph representation learning in biomedicine and healthcare,” Nature Biomedical Engineering 6 no. 12, (2022) 1353–1369. [24] M. W. Berry, A. Mohamed, and B. W. Yap, Supervised and unsupervised learning for data science. Springer, 2019. [25] A. Mucherino, P. J. Papajorgji, P. M. Pardalos, A. Mucherino, P. J. Papajorgji, and P. M. Pardalos, “K-nearest neighbor classification,” Data mining in agriculture (2009) 83–106. [26] M. P. LaValley, “Logistic regression,” Circulation 117 no. 18, (2008) 2395–2399. [27] D. Berrar, “Bayes’ theorem and naive bayes classifier,” Encyclopedia of bioinformatics and computational biology: ABC of bioinformatics 403 (2018) 412. [28] A. J. Myles, R. N. Feudale, Y. Liu, N. A. Woody, and S. D. Brown, “An introduction to decision tree modeling,” Journal of Chemometrics: A Journal of the Chemometrics Society 18 no. 6, (2004) 275–285. [29] L. Breiman, “Random forests,” Machine learning 45 (2001) 5–32. [30] J. H. Friedman, “Stochastic gradient boosting,” Computational statistics & data analysis 38 no. 4, (2002) 367–378. 124[31] S. Suthaharan and S. Suthaharan, “Support vector machine,” Machine learning models and algorithms for big data classification: thinking with examples for e!ective learning (2016) 207–235. [32] W. S. Noble, “What is a support vector machine?” Nature biotechnology 24 no. 12, (2006) 1565–1567. [33] R. Bro and A. K. Smilde, “Principal component analysis,” Analytical methods 6 no. 9, (2014) 2812–2831. [34] M. Mohammed, M. B. Khan, and E. B. M. Bashier, Machine learning: algorithms and applications. Crc Press, 2016. [35] L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.” Journal of machine learning research 9 no. 11, (2008) . [36] L. McInnes, J. Healy, and J. Melville, “Umap: Uniform manifold approximation and projection for dimension reduction,” arXiv preprint arXiv:1802.03426 (2018) . [37] M. E. Celebi and K. Aydin, Unsupervised learning algorithms, vol. 9. Springer, 2016. [38] F. Murtagh and P. Contreras, “Algorithms for hierarchical clustering: an overview,” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2 no. 1, (2012) 86–97. [39] E. Schubert, J. Sander, M. Ester, H. P. Kriegel, and X. Xu, “Dbscan revisited, revisited: why and how you should (still) use dbscan,” ACM Transactions on Database Systems (TODS) 42 no. 3, (2017) 1–21. [40] M. Ester, H.-P. Kriegel, J. Sander, X. Xu, et al., “A density-based algorithm for discovering clusters in large spatial databases with noise,” in kdd, vol. 96, pp. 226–231. 1996. 125[41] D. A. Reynolds et al., “Gaussian mixture models.” Encyclopedia of biometrics 741 no. 659-663, (2009) . [42] T. Chari and L. Pachter, “The specious art of single-cell genomics,” PLOS Computational Biology 19 no. 8, (2023) e1011288. [43] X. Dong, Z. Yu, W. Cao, Y. Shi, and Q. Ma, “A survey on ensemble learning,” Frontiers of Computer Science 14 (2020) 241–258. [44] Y. Freund, R. E. Schapire, et al., “Experiments with a new boosting algorithm,” in icml, vol. 96, pp. 148–156, Citeseer. 1996. [45] R. Maclin and D. Opitz, “An empirical evaluation of bagging and boosting,” AAAI/IAAI 1997 (1997) 546–551. [46] A. Natekin and A. Knoll, “Gradient boosting machines, a tutorial,” Frontiers in neurorobotics 7 (2013) 21. [47] T. Chen and C. Guestrin, “Xgboost: A scalable tree boosting system,” in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785–794. 2016. [48] G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu, “Lightgbm: A highly e#cient gradient boosting decision tree,” Advances in neural information processing systems 30 (2017) . [49] A. V. Dorogush, V. Ershov, and A. Gulin, “Catboost: gradient boosting with categorical features support,” arXiv preprint arXiv:1810.11363 (2018) . [50] L. Breiman, “Bagging predictors,” Machine learning 24 (1996) 123–140. [51] B. Jenkins and A. Tanguay, “Handbook of neural computing and neural networks,” 1995. 126[52] J. Li, J.-h. Cheng, J.-y. Shi, and F. Huang, “Brief introduction of back propagation (bp) neural network algorithm and its improvement,” in Advances in Computer Science and Information Engineering: Volume 2, pp. 553–558, Springer. 2012. [53] X. Xu, L. Zuo, and Z. Huang, “Reinforcement learning algorithms with function approximation: Recent advances and applications,” Information Sciences 261 (2014) 1–31. [54] A. G. Chidambaram and M. Josephson, “Clinical research study designs: The essentials,” Pediatric investigation 3 no. 04, (2019) 245–252. [55] L. M. McCowan, F. Figueras, and N. H. Anderson, “Evidence-based national guidelines for the management of suspected fetal growth restriction: comparison, consensus, and controversy,” American journal of obstetrics and gynecology 218 no. 2, (2018) S855–S868. [56] S. Chauhan, M. Rice, W. Grobman, J. Bailit, U. Reddy, R. Wapner, M. Varner, J. Thorp Jr, K. Leveno, S. Caritis, et al., “Msce, for the eunice kennedy shriver national institute of child health and human development (nichd) maternal-fetal medicine units (mfmu) network. neonatal morbidity of small-and large-for-gestational-age neonates born at term in uncomplicated pregnancies,” Obstet Gynecol 130 no. 3, (2017) 511–519. [57] A. WEISSMANN-BRENNER, M. J. Simchen, E. Zilberberg, A. Kalter, B. Weisz, R. Achiron, and M. Dulitzky, “Maternal and neonatal outcomes of large for gestational age pregnancies,” Acta obstetricia et gynecologica Scandinavica 91 no. 7, (2012) 844–849. [58] T. Henriksen, “The macrosomic fetus: a challenge in current obstetrics,” Acta obstetricia et gynecologica Scandinavica 87 no. 2, (2008) 134–145. 127[59] T. Kiserud, G. Piaggio, G. Carroli, M. Widmer, J. Carvalho, L. Neerup Jensen, D. Giordano, J. G. Cecatti, H. Abdel Aleem, S. A. Talegawkar, et al., “The world health organization fetal growth charts: a multinational longitudinal study of ultrasound biometric measurements and estimated fetal weight,” PLoS medicine 14 no. 1, (2017) e1002220. [60] T. Kiserud, A. Benachi, K. Hecher, R. G. Perez, J. Carvalho, G. Piaggio, and L. D. Platt, “The world health organization fetal growth charts: concept, findings, interpretation, and application,” American journal of obstetrics and gynecology 218 no. 2, (2018) S619–S629. [61] G. M. B. Louis, J. Grewal, P. S. Albert, A. Sciscione, D. A. Wing, W. A. Grobman, R. B. Newman, R. Wapner, M. E. D’Alton, D. Skupski, et al., “Racial/ethnic standards for fetal growth: the nichd fetal growth studies,” American journal of obstetrics and gynecology 213 no. 4, (2015) 449–e1. [62] K. L. Grantz, M. L. Hediger, D. Liu, and G. M. B. Louis, “Fetal growth standards: the nichd fetal growth study approach in context with intergrowth-21st and the world health organization multicentre growth reference study,” American journal of obstetrics and gynecology 218 no. 2, (2018) S641–S655. [63] T. Todros, E. Ferrazzi, U. Nicolini, C. Groli, S. Zucca, L. Parodi, M. Pavoni, and A. Zorzoli, “Fitting growth curves to head and abdomen measurements of the fetus: a multicentric study,” Journal of clinical ultrasound 15 no. 2, (1987) 95–105. [64] A. T. Papageorghiou, E. O. Ohuma, D. G. Altman, T. Todros, L. C. Ismail, A. Lambert, Y. A. Ja!er, E. Bertino, M. G. Gravett, M. Purwar, et al., “International standards for fetal growth based on serial ultrasound 128measurements: the fetal growth longitudinal study of the intergrowth-21st project,” The Lancet 384 no. 9946, (2014) 869–879. [65] W. Wosilait, R. Luecke, and J. Young, “A mathematical analysis of human embryonic and fetal growth data.” Growth, development, and aging: GDA 56 no. 4, (1992) 249–257. [66] B. Gompertz, “Xxiv. on the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies. in a letter to francis baily, esq. frs &c,” Philosophical transactions of the Royal Society of London no. 115, (1825) 513–583. [67] A. K. Laird, “Dynamics of tumour growth,” British journal of cancer 18 no. 3, (1964) 490. [68] C. Frenzen and J. Murray, “A cell kinetics justification for gompertz’equation,” SIAM Journal on Applied Mathematics 46 no. 4, (1986) 614–629. [69] K. M. Tjørve and E. Tjørve, “The use of gompertz models in growth analyses, and new gompertz-model approach: An addition to the unified-richards family,” PloS one 12 no. 6, (2017) e0178691. [70] R. H. Luecke, W. D. Wosilait, and J. F. Young, “Mathematical modeling of human embryonic and fetal growth rates.” Growth, development, and aging: GDA 63 no. 1-2, (1999) 49–59. [71] M. J. Shepard, V. A. Richards, R. L. Berkowitz, S. L. Warsof, and J. C. Hobbins, “An evaluation of two equations for predicting fetal weight by ultrasound,” American journal of obstetrics and gynecology 142 no. 1, (1982) 47–54. 129[72] F. P. Hadlock, R. Harrist, R. S. Sharman, R. L. Deter, and S. K. Park, “Estimation of fetal weight with the use of head, body, and femur measurements—a prospective study,” American journal of obstetrics and gynecology 151 no. 3, (1985) 333–337. [73] J. Villar, L. Cheikh Ismail, C. G. Victora, E. O. Ohuma, E. Bertino, D. G. Altman, A. Lambert, A. T. Papageorghiou, M. Carvalho, Y. A. Ja!er, et al., “International fetal and newborn growth consortium for the 21st century (intergrowth-21st). international standards for newborn weight, length, and head circumference by gestational age and sex: the newborn cross-sectional study of the intergrowth-21st project,” Lancet 384 no. 9946, (2014) 857–68. [74] J. Milner and J. Arezina, “The accuracy of ultrasound estimation of fetal weight in comparison to birth weight: A systematic review,” Ultrasound 26 no. 1, (2018) 32–41. [75] C. W. Kong and W. W. K. To, “Comparison of the accuracy of intergrowth-21 formula with other ultrasound formulae in fetal weight estimation,” Taiwanese Journal of Obstetrics and Gynecology 58 no. 2, (2019) 273–277. [76] S. Hiwale, H. Misra, and S. Ulman, “Fetal weight estimation by ultrasound: development of indian population-based models,” Ultrasonography 38 no. 1, (2019) 50. [77] J. Tao, Z. Yuan, L. Sun, K. Yu, and Z. Zhang, “Fetal birth weight prediction with measured data by a temporal machine learning method,” BMC Medical Informatics and Decision Making 21 no. 1, (2021) 1–10. [78] R. L. Deter, W. Lee, J. Kingdom, and R. Romero, “Second trimester growth velocities: assessment of fetal growth potential in sga singletons,” The Journal of Maternal-Fetalw & Neonatal Medicine 32 no. 6, (2019) 939–946. 130[79] I. K. Rossavik and R. L. Deter, “Mathematical modeling of fetal growth: I. basic principles,” Journal of Clinical Ultrasound 12 no. 9, (1984) 529–533. [80] L. Xu, M. Skoularidou, A. Cuesta-Infante, and K. Veeramachaneni, “Modeling tabular data using conditional gan,” Advances in neural information processing systems 32 (2019) . [81] Z. Li, Y. Zhao, and J. Fu, “Sync: A copula based framework for generating synthetic data from aggregated sources,” in 2020 International Conference on Data Mining Workshops (ICDMW), pp. 571–578, IEEE. 2020. [82] L. Xu et al., Synthesizing tabular data using conditional GAN. PhD thesis, Massachusetts Institute of Technology, 2020. [83] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al., “Scikit-learn: Machine learning in python,” the Journal of machine Learning research 12 (2011) 2825–2830. [84] L. Mouselimis, ClusterR: Gaussian Mixture Models, K-Means, Mini-Batch-Kmeans, K-Medoids and A”nity Propagation Clustering, 2023. https://CRAN.R-project.org/package=ClusterR. R package version 1.3.1. [85] A. Stukalov and D. Lin, “Clustering. jl,” Julia Statistics. Available online at: https://github. com/JuliaStats/Clustering. jl (accessed September 30, 2021) (2021) . [86] D. J. MacKay, “Bayesian interpolation,” Neural computation 4 no. 3, (1992) 415–447. [87] A. A. Neath and J. E. Cavanaugh, “The bayesian information criterion: background, derivation, and applications,” Wiley Interdisciplinary Reviews: Computational Statistics 4 no. 2, (2012) 199–203. 131[88] A. Gelman and X.-L. Meng, “Simulating normalizing constants: From importance sampling to bridge sampling to path sampling,” Statistical science (1998) 163–185. [89] N. Lartillot and H. Philippe, “Computing bayes factors using thermodynamic integration,” Systematic biology 55 no. 2, (2006) 195–207. [90] M. A. Newton and A. E. Raftery, “Approximate bayesian inference with the weighted likelihood bootstrap,” Journal of the Royal Statistical Society Series B: Statistical Methodology 56 no. 1, (1994) 3–26. [91] W. Xie, P. O. Lewis, Y. Fan, L. Kuo, and M.-H. Chen, “Improving marginal likelihood estimation for bayesian phylogenetic model selection,” Systematic biology 60 no. 2, (2011) 150–160. [92] S. Van Buuren and C. G. Oudshoorn, “Multivariate imputation by chained equations,” 2000. [93] K. P. Murphy, “Conjugate bayesian analysis of the gaussian distribution.” https://www.cs.ubc.ca/~murphyk/Papers/bayesGauss.pdf. Accessed: 2023-10-05. [94] L. Hubert and P. Arabie, “Comparing partitions,” Journal of classification 2 (1985) 193–218. [95] M. Gagolewski, “genieclust: Fast and robust hierarchical clustering,” SoftwareX 15 (2021) 100722. [96] N. Patki, R. Wedge, and K. Veeramachaneni, “The synthetic data vault,” in 2016 IEEE international conference on data science and advanced analytics (DSAA), pp. 399–410, IEEE. 2016. [97] D. Dua, C. Gra!, et al., “Uci machine learning repository, 2017,” URL http: // archive. ics. uci. edu/ ml 7 no. 1, (2017) 62. 132[98] M. Gagolewski, “A framework for benchmarking clustering algorithms,” SoftwareX 20 (2022) 101270. [99] M. A. Levin, H.-M. Lin, J. G. Castillo, D. H. Adams, D. L. Reich, and G. W. Fischer, “Early on–cardiopulmonary bypass hypotension and other factors associated with vasoplegic syndrome,” Circulation 120 no. 17, (2009) 1664–1671. [100] S. Omar, A. Zedan, and K. Nugent, “Cardiac vasoplegia syndrome: pathophysiology, risk factors and treatment,” The American journal of the medical sciences 349 no. 1, (2015) 80–88. [101] L. Buitinck, G. Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, V. Niculae, P. Prettenhofer, A. Gramfort, J. Grobler, et al., “Api design for machine learning software: experiences from the scikit-learn project,” arXiv preprint arXiv:1309.0238 (2013) . [102] J. H. Friedman, “Greedy function approximation: a gradient boosting machine,” Annals of statistics (2001) 1189–1232. [103] P. Meyer and J. Saez-Rodriguez, “Advances in systems biology modeling: 10 years of crowdsourcing dream challenges,” Cell Systems 12 no. 6, (2021) 636–653.	en_US
dc.subject	1 Predictive Analytics, 2 Medical Data Mining, 3 Clinical outcomes	en_US
dc.title	Machine Learning and Predicting Clinical Outcomes	en_US
dc.type.degree	Ph.D	en_US
dc.date.updated	2025
dc.type.institution	HBNI	en_US
dc.description.advisor	Rahul Siddharthan
dc.description.pages	133p.	en_US
dc.type.mainsub	Computational Biology	en_US
dc.type.hbnibos	Life Sciences	en_US