全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用：99美元
投递稿件

查看量下载量

Lithium Associated Chronic Kidney Disease Prediction Using Explainable Machine Learning: A Comprehensive Modelling and Interpretation Framework

DOI: 10.4236/oalib.1114920, PP. 1-36

Subject Areas: Nephrology, Artificial Intelligence

Keywords: Lithium Nephrotoxicity, Chronic Kidney Disease, Explainable AI, SHAP, CatBoost, Clinical Risk Modelling, Machine Learning

Full-Text   Cite this paper   Add to My Lib

Abstract

Lithium remains the most effective long-term treatment for bipolar disorder, yet its therapeutic benefits are offset by a well-established risk of chronic kidney disease (CKD). Anticipating lithium-associated renal impairment is clinically challenging because the underlying mechanisms are subtle, multi-variate, and evolve dynamically with cumulative exposure. In this study, we develop a transparent, end-to-end machine-learning framework for early detection of lithium-induced kidney damage using a comprehensive synthetic cohort that incorporates lithium duration, serum concentrations, renal function biomarkers, comorbidities, metabolic factors, and inflammatory markers. Four supervised classifiers, Random Forest, XGBoost, logistic regression, and CatBoost were evaluated using stratified 5-fold cross-validation. CatBoost achieved the strongest generalization performance (test AUC = 0.785) and was subsequently selected for full explainability analysis. To ensure clinical interpretability, we integrate a multi-layered explanation suite comprising SHAP-based global and local attributions, interaction effect quantification, patient-specific waterfall plots, LIME explanations, bootstrap con-fidence intervals, permutation-based statistical validation, and correlation analyses between feature values and model-derived risk. Across methods, lithium exposure metrics (duration, serum level, dose), renal decline markers (eGFR trajectory, creatinine, proteinuria), hypertension, and key interaction terms (duration × level; age × duration) consistently emerge as dominant predictors of early renal damage. The SHAP-derived risk landscape reveals coherent, monotonic associations between cumulative lithium exposure, deterioration in renal function, and elevated CKD risk, aligning closely with established nephrotoxic pathways. Although the dataset is synthetic, the modelling strategy provides a rigorous blueprint for interpretable AI driven nephrotoxicity surveillance. The framework offers actionable, clinician-ready insights and establishes a foundation for future validation on real-world lithium cohorts, supporting precision monitoring and early intervention in lithium-treated patients.

 Cite this paper

Filippis, R. D. and Foysal, A. A. (2026). Lithium Associated Chronic Kidney Disease Prediction Using Explainable Machine Learning: A Comprehensive Modelling and Interpretation Framework. Open Access Library Journal, 13, e14920. doi: http://dx.doi.org/10.4236/oalib.1114920.

References

[1]  Simonetti, A., Koukopoulos, A.E., Kotzalidis, G.D., Janiri, D., De Chiara, L., Janiri, L., et al. (2020) Stabilization Beyond Mood: Stabilizing Patients with Bipolar Disorder in the Various Phases of Life. Frontiers in Psychiatry, 11, Article 247. https://doi.org/10.3389/fpsyt.2020.00247
[2]  Sani, G., Perugi, G. and Ton-do, L. (2017) Treatment of Bipolar Disorder in a Lifetime Perspective: Is Lithi-um Still the Best Choice? Clinical Drug Investigation, 37, 713-727. https://doi.org/10.1007/s40261-017-0531-2
[3]  Severus, E., Bauer, M. and Geddes, J. (2018) Efficacy and Effectiveness of Lithium in the Long-Term Treatment of Bipolar Disorders: An Update 2018. Pharmacopsychiatry, 51, 173-176. https://doi.org/10.1055/a-0627-7489
[4]  Rybakowski, J.K. and Ferensztajn-Rochowiak, E. (2023) Updated Perspectives on How and When Lithium Should Be Used in the Treatment of Mood Disorders. Expert Review of Neurotherapeutics, 23, 157-167. https://doi.org/10.1080/14737175.2023.2181076
[5]  Sarkar, S., Singh, Y.C. and Kaloiya, G.S. (2024) Psychotherapy and Psychotropic Drug Treatment: Neurobiological and Psychodynamic Perspectives. Indian Journal of Psychiatry, 66, S126.
[6]  Wallace, W. and de Moore, G. (2023) Edward Trautner (1890-1978), a Pioneer of Psychopharmacology. Journal of the History of the Neurosciences, 33, 1-56. https://doi.org/10.1080/0964704x.2023.2226710
[7]  Schoretsanitis, G., De Filippis, R., Brady, B.M., Homan, P., Suppes, T. and Kane, J.M. (2022) Preva-lence of Impaired Kidney Function in Patients with Long-Term Lithium Treat-ment: A Systematic Review and Meta-Analysis. Bipolar Disorders, 24, 264-274. https://doi.org/10.1111/bdi.13154
[8]  Łukawska, E., Frankiewicz, D., Izak, M., Woźniak, A., Dworacki, G. and Niemir, Z.I. (2021) Lithium Toxicity and the Kidney with Special Focus on Nephrotic Syndrome Associated with the Acute Kidney Injury: A Case‐based Systematic Analysis. Journal of Applied Toxicology, 41, 1896-1909. https://doi.org/10.1002/jat.4167
[9]  Grünfeld, J. and Ros-sier, B.C. (2009) Lithium Nephrotoxicity Revisited. Nature Reviews Nephrology, 5, 270-276. https://doi.org/10.1038/nrneph.2009.43
[10]  Gupta, S. and Khastgir, U. (2017) Drug Information Update. Lithium and Chronic Kidney Disease: Debates and Dilemmas. BJPsych Bulletin, 41, 216-220. https://doi.org/10.1192/pb.bp.116.054031
[11]  Presne, C., Fakhouri, F., Noël, L., Stengel, B., Even, C., Kreis, H., et al. (2003) Lithium-induced Nephrop-athy: Rate of Progression and Prognostic Factors. Kidney International, 64, 585-592. https://doi.org/10.1046/j.1523-1755.2003.00096.x
[12]  Kishore, B.K. and Ecelbarger, C.M. (2013) Lithium: A Versatile Tool for Understanding Renal Physiology. American Journal of Physiology-Renal Physiology, 304, F1139-F1149. https://doi.org/10.1152/ajprenal.00718.2012
[13]  Dineen, R., Bogdanet, D., Thompson, D., Thompson, C.J., Behan, L.A., McKay, A.P., et al. (2017) Endocrinopathies and Renal Outcomes in Lithium Therapy: Impact of Lithium Toxicity. QJM: An International Journal of Medicine, 110, 821-827. https://doi.org/10.1093/qjmed/hcx171
[14]  Deloei, S.R., Sorouri, S., Nikkhoo, I., Elahabadi, G. and Fazli, B. (2025) The Effect of Vitamin D Deficiency on Liver Transplant Recipients. Indian Journal of Transplantation, 19, 10-20. https://doi.org/10.4103/ijot.ijot_148_23
[15]  Lee, H., Park, M., Lee, S. and Hong, S. (2022) Clinical Effect of Preoperative 25-Oh-Vitamin D3 Level in Liver Transplant Recipients. Transplantation Proceedings, 54, 2301-2306. https://doi.org/10.1016/j.transproceed.2022.08.025
[16]  Chaney, A., Heck-man, M.G., Diehl, N.N., Meek, S. and Keaveny, A.P. (2015) Effectiveness and Outcomes of Current Practice in Treating Vitamin D Deficiency in Patients Listed for Liver Transplantation. Endocrine Practice, 21, 761-769. https://doi.org/10.4158/ep14416.or
[17]  Sharma, V., Ali, I., van der Veer, S., Martin, G., Ainsworth, J. and Augustine, T. (2021) Adoption of Clinical Risk Prediction Tools Is Limited by a Lack of Integration with Electronic Health Rec-ords. BMJ Health & Care Informatics, 28, e100253. https://doi.org/10.1136/bmjhci-2020-100253
[18]  Lee, T.C., Shah, N.U., Haack, A. and Baxter, S.L. (2020) Clinical Implementation of Predictive Models Embedded within Electronic Health Record Systems: A Systematic Review. In-formatics, 7, Article 25. https://doi.org/10.3390/informatics7030025
[19]  Watson, J., Hutyra, C.A., Clancy, S.M., Chandiramani, A., Bedoya, A., Ilangovan, K., et al. (2020) Over-coming Barriers to the Adoption and Implementation of Predictive Modeling and Machine Learning in Clinical Care: What Can We Learn from US Academic Medical Centers? JAMIA Open, 3, 167-172. https://doi.org/10.1093/jamiaopen/ooz046
[20]  Castaneda, C., Nalley, K., Mannion, C., Bhattacharyya, P., Blake, P., Pecora, A., et al. (2015) Clinical Deci-sion Support Systems for Improving Diagnostic Accuracy and Achieving Preci-sion Medicine. Journal of Clinical Bioinformatics, 5, Article No. 137. https://doi.org/10.1186/s13336-015-0019-3
[21]  Amarasingham, R., Patzer, R.E., Huesch, M., Nguyen, N.Q. and Xie, B. (2014) Implementing Electronic Health Care Predictive Analytics: Considerations and Challenges. Health Affairs, 33, 1148-1154. https://doi.org/10.1377/hlthaff.2014.0352
[22]  Zikos, D. and DeLellis, N. (2018) CDSS-RM: A Clinical Decision Support System Reference Model. BMC Medical Research Methodology, 18, Article No. 137. https://doi.org/10.1186/s12874-018-0587-6
[23]  Huckvale, K., Venkatesh, S. and Christensen, H. (2019) Toward Clinical Digital Phenotyping: A Timely Op-portunity to Consider Purpose, Quality, and Safety. npj Digital Medicine, 2, Ar-ticle No. 88. https://doi.org/10.1038/s41746-019-0166-1
[24]  Afrifa‐Yamoah, E., Adua, E., Peprah‐Yamoah, E., Anto, E.O., Opoku‐Yamoah, V., Acheampong, E., et al. (2024) Pathways to Chronic Disease Detection and Pre-diction: Mapping the Potential of Machine Learning to the Pathophysiological Processes While Navigating Ethical Challenges. Chronic Diseases and Transla-tional Medicine, 11, 1-21. https://doi.org/10.1002/cdt3.137
[25]  Foluke Ekundayo, (2024) Machine Learning for Chronic Kidney Disease Progression Modelling: Leveraging Data Science to Optimize Patient Management. World Journal of Advanced Research and Reviews, 24, 453-475. https://doi.org/10.30574/wjarr.2024.24.3.3730
[26]  Sperling, J., Welsh, W., Haseley, E., Quenstedt, S., Muhigaba, P.B., Brown, A., et al. (2024) Machine Learning-Based Prediction Models in Medical Decision-Making in Kidney Dis-ease: Patient, Caregiver, and Clinician Perspectives on Trust and Appropriate Use. Journal of the American Medical Informatics Association, 32, 51-62. https://doi.org/10.1093/jamia/ocae255
[27]  Delrue, C. and Speeckaert, M.M. (2024) Decoding Kidney Pathophysiology: Omics-Driven Approaches in Preci-sion Medicine. Journal of Personalized Medicine, 14, Article 1157. https://doi.org/10.3390/jpm14121157
[28]  Pawuś, D., Porażko, T. and Paszkiel, S. (2024) Automation and Decision Support in the Area of Nephrology Using Numerical Algorithms, Artificial Intelligence, and Expert Approach: Re-view of the Current State of Knowledge. IEEE Access, 12, 86043-86066. https://doi.org/10.1109/access.2024.3413595
[29]  Xu, L., Li, C., Gao, S., Zhao, L., Guan, C., Shen, X., et al. (2024) Personalized Prediction of Long-Term Renal Function Prognosis Following Nephrectomy Using Interpretable Machine Learning Algorithms: Case-Control Study. JMIR Medical Informatics, 12, e52837. https://doi.org/10.2196/52837
[30]  Karalis, V.D. (2024) The Inte-gration of Artificial Intelligence into Clinical Practice. Applied Biosciences, 3, 14-44. https://doi.org/10.3390/applbiosci3010002
[31]  Penman, I.D., Ral-ston, S.H., Strachan, M.W.J. and Hobson, R. (2022) Davidson’s Principles and Practice of Medicine E-Book: Davidson’s Principles and Practice of Medicine E-Book. Elsevier.
[32]  Antoniadi, A.M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B.A., et al. (2021) Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Re-view. Applied Sciences, 11, Article 5088. https://doi.org/10.3390/app11115088
[33]  Hassija, V., Chamola, V., Maha-patra, A., Singal, A., Goel, D., Huang, K., et al. (2023) Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence. Cognitive Computation, 16, 45-74. https://doi.org/10.1007/s12559-023-10179-8
[34]  Kim, S.Y., Kim, D.H., Kim, M.J., Ko, H.J. and Jeong, O.R. (2024) Xai-Based Clinical Decision Support Systems: A Systematic Review. Applied Sciences, 14, Article 6638. https://doi.org/10.3390/app14156638
[35]  Chinnaraju, A. (2025) Explaina-ble AI (XAI) for Trustworthy and Transparent Decision-Making: A Theoretical Framework for AI Interpretability. World Journal of Advanced Engineering Technology and Sciences, 14, 170-207. https://doi.org/10.30574/wjaets.2025.14.3.0106
[36]  Rudin, C. (2019) Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nature Machine Intelligence, 1, 206-215. https://doi.org/10.1038/s42256-019-0048-x
[37]  Sweet, M.M.R., Ahmed, M.P., Akter, S. and Tisha, S.A. (2025) Integrating Deep Learning and Inter-pretable Regression Models for Transparent Decision Support in Healthcare Di-agnostics. Journal of Medical and Health Studies, 6, 17-38. https://doi.org/10.32996/jmhs.2025.6.5.4
[38]  Alqudah, A.M. and Moussavi, Z. (2025) Bridging Signal Intelligence and Clinical Insight: A Comprehensive Review of Feature Engineering, Model Interpretability, and Machine Learning in Biomedical Signal Analysis. Applied Sciences, 15, Article 12036. https://doi.org/10.3390/app152212036
[39]  Hettikankanamage, N., Shafia-bady, N., Chatteur, F., Wu, R.M.X., Ud Din, F. and Zhou, J. (2025) Explainable Artificial Intelligence (XAI): A Systematic Review for Unveiling the Black Box Models and Their Relevance to Biomedical Imaging and Sensing. Sensors, 25, Article 6649. https://doi.org/10.3390/s25216649
[40]  Kaur, N., Bhattachar-ya, S. and Butte, A.J. (2021) Big Data in Nephrology. Nature Reviews Nephrolo-gy, 17, 676-687. https://doi.org/10.1038/s41581-021-00439-x
[41]  Tangri, N., Chadban, S., Cabrera, C., Retat, L. and Sánchez, J.J.G. (2022) Projecting the Epidemiological and Economic Impact of Chronic Kidney Disease Using Pa-tient-Level Microsimulation Modelling: Rationale and Methods of Inside CKD. Advances in Therapy, 40, 265-281. https://doi.org/10.1007/s12325-022-02353-5
[42]  Fasseeh, A.N., Ashmawy, R., Hren, R., ElFass, K., Imre, A., Németh, B., et al. (2025) Generating Realistic Synthetic Patient Cohorts: Enforcing Statistical Distributions, Correlations, and Logical Constraints. Algorithms, 18, Article 475. https://doi.org/10.3390/a18080475
[43]  Lin, H. and Lyu, J. (2025) A Holistic Framework for Intradialytic Hypotension Prediction Using Generative Adver-sarial Networks-Based Data Balancing. BMC Medical Informatics and Decision Making, 25, Article No. 257. https://doi.org/10.1186/s12911-025-03094-5
[44]  Sakagianni, A., Kou-fopoulou, C., Koufopoulos, P., Feretzakis, G., Kalles, D., Paxinou, E., et al. (2024) The Synergy of Machine Learning and Epidemiology in Addressing Carbapenem Resistance: A Comprehensive Review. Antibiotics, 13, Article 996. https://doi.org/10.3390/antibiotics13100996
[45]  Grandjean, E.M. and Au-bry, J.-M. (2009) Lithium: Updated Human Knowledge Using an Evi-dence-Based Approach: Part II: Clinical Pharmacology and Therapeutic Moni-toring. CNS Drugs, 23, 331-349.
[46]  Shine, B., McKnight, R.F., Leaver, L. and Geddes, J.R. (2015) Long-term Effects of Lithium on Renal, Thyroid, and Para-thyroid Function: A Retrospective Analysis of Laboratory Data. The Lancet, 386, 461-468. https://doi.org/10.1016/s0140-6736(14)61842-0
[47]  Davis, J., Desmond, M. and Berk, M. (2018) Lithium and Nephrotoxicity: A Literature Re-view of Approaches to Clinical Management and Risk Stratification. BMC Neph-rology, 19, Article No. 305. https://doi.org/10.1186/s12882-018-1101-4
[48]  Goodwin, G., Haddad, P., Ferrier, I., Aronson, J., Barnes, T., Cipriani, A., et al. (2016) Evidence-based Guidelines for Treating Bipolar Disorder: Revised Third Edition Recommenda-tions from the British Association for Psychopharmacology. Journal of Psycho-pharmacology, 30, 495-553. https://doi.org/10.1177/0269881116636545
[49]  Dunn, W.B., Broadhurst, D.I., Atherton, H.J., Goodacre, R. and Griffin, J.L. (2011) Systems Level Studies of Mammalian Metabolomes: The Roles of Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy. Chemical Society Reviews, 40, 387-426. https://doi.org/10.1039/b906712b
[50]  Zhang, Y., Lee, G., Li, S., Hu, Z., Zhao, K. and Rogers, J.A. (2023) Advances in Bioresorbable Materials and Electronics. Chemical Reviews, 123, 11722-11773. https://doi.org/10.1021/acs.chemrev.3c00408
[51]  Chan, B.S., Cheng, S., Isoardi, K.Z., Chiew, A., Siu, W., Shulruf, B., et al. (2020) Effect of Age on the Severity of Chronic Lithium Poisoning. Clinical Toxicology, 58, 1023-1027. https://doi.org/10.1080/15563650.2020.1726376
[52]  Rej, S., Elie, D., Muc-si, I., Looper, K.J. and Segal, M. (2014) Chronic Kidney Disease in Lithi-um-Treated Older Adults: A Review of Epidemiology, Mechanisms, and Implica-tions for the Treatment of Late-Life Mood Disorders. Drugs & Aging, 32, 31-42. https://doi.org/10.1007/s40266-014-0234-9
[53]  McKnight, R.F., Adida, M., Budge, K., Stockton, S., Goodwin, G.M. and Geddes, J.R. (2012) Lithium Toxicity Profile: A Systematic Review and Meta-Analysis. The Lancet, 379, 721-728. https://doi.org/10.1016/s0140-6736(11)61516-x
[54]  Werneke, U., Ott, M., Renberg, E.S., Taylor, D. and Stegmayr, B. (2012) A Decision Analysis of Long-Term Lithium Treatment and the Risk of Renal Failure. Acta Psychiatrica Scandinavica, 126, 186-197. https://doi.org/10.1111/j.1600-0447.2012.01847.x
[55]  Fries, G.R., Bauer, I.E., Scaini, G., Wu, M., Kazimi, I.F., Valvassori, S.S., et al. (2017) Accelerated Epigenetic Aging and Mitochondrial DNA Copy Number in Bipolar Disorder. Translational Psychiatry, 7, Article No. 1283. https://doi.org/10.1038/s41398-017-0048-8
[56]  Aron, L., Ngian, Z.K., Qiu, C., Choi, J., Liang, M., Drake, D.M., et al. (2025) Lithium Deficiency and the On-set of Alzheimer’s Disease. Nature, 645, 712-721. https://doi.org/10.1038/s41586-025-09335-x
[57]  Tsao, H., Lai, T., Chou, Y., Lin, S. and Chen, Y. (2023) Predialysis Trajectories of Estimated GFR and Con-current Trends of Chronic Kidney Disease-Relevant Biomarkers. Therapeutic Advances in Chronic Disease, 14. https://doi.org/10.1177/20406223231177291
[58]  Wen, Y., Xu, L., Melchinger, I., Thiessen-Philbrook, H., Moledina, D.G., Coca, S.G., et al. (2023) Longitudinal Biomarkers and Kidney Disease Progression after Acute Kidney Injury. JCI Insight, 8, e167731. https://doi.org/10.1172/jci.insight.167731
[59]  Rodríguez-Ortiz, M.E., Pon-tillo, C., Rodríguez, M., Zürbig, P., Mischak, H. and Ortiz, A. (2018) Novel Uri-nary Biomarkers for Improved Prediction of Progressive EGFR Loss in Early Chronic Kidney Disease Stages and in High Risk Individuals without Chronic Kidney Disease. Scientific Reports, 8, Article No. 15940. https://doi.org/10.1038/s41598-018-34386-8
[60]  Zhang, T., Widdop, R.E. and Ricardo, S.D. (2024) Transition from Acute Kidney Injury to Chronic Kid-ney Disease: Mechanisms, Models, and Biomarkers. American Journal of Physi-ology-Renal Physiology, 327, F788-F805. https://doi.org/10.1152/ajprenal.00184.2024
[61]  Qiao, L., Khalilimeybodi, A., Linden-Santangeli, N.J. and Rangamani, P. (2025) The Evolution of Systems Biology and Systems Medicine: From Mechanistic Models to Uncertainty Quanti-fication. Annual Review of Biomedical Engineering, 27, 425-447. https://doi.org/10.1146/annurev-bioeng-102723-065309
[62]  Campagner, A., Biganzoli, E.M., Balsano, C., Cereda, C. and Cabitza, F. (2025) Modeling Un-knowns: A Vision for Uncertainty-Aware Machine Learning in Healthcare. In-ternational Journal of Medical Informatics, 203, Article ID: 106014. https://doi.org/10.1016/j.ijmedinf.2025.106014
[63]  Atf, Z., Ahmad Safa-vi-Naini, S.A., Lewis, P.R., Mahjoubfar, A., Naderi, N., Savage, T.R. and Soroush, A. (2025) The Challenge of Uncertainty Quantification of Large Language Mod-els in Medicine. arXiv: 2504.05278.
[64]  Schoups, G., van de Giesen, N.C. and Savenije, H.H.G. (2008) Model Complexity Control for Hydrologic Prediction. Water Resources Research, 44, W00B03. https://doi.org/10.1029/2008wr006836
[65]  Lacaze, X., Palanque, P., Na-varre, D. and Bastide, R. (2002) Performance Evaluation as a Tool for Quantita-tive Assessment of Complexity of Interactive Systems. In: Forbrig, P., Limbourg, Q., Vanderdonckt, J. and Urban, B., Eds., Interactive Systems: Design, Specifica-tion, and Verification, Springer, 208-222. https://doi.org/10.1007/3-540-36235-5_16
[66]  Courcelles, E., Boissel, J., Massol, J., Klingmann, I., Kahoul, R., Hommel, M., et al. (2022) Solving the Evi-dence Interpretability Crisis in Health Technology Assessment: A Role for Mechanistic Models? Frontiers in Medical Technology, 4, Article 810315. https://doi.org/10.3389/fmedt.2022.810315
[67]  Gorin, G., Vastola, J.J., Fang, M. and Pachter, L. (2022) Interpretable and Tractable Models of Tran-scriptional Noise for the Rational Design of Single-Molecule Quantification Ex-periments. Nature Communications, 13, Article No. 7620. https://doi.org/10.1038/s41467-022-34857-7
[68]  Gundersen, O.E. (2021) The Fundamental Principles of Reproducibility. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 379, Ar-ticle ID: 20200210. https://doi.org/10.1098/rsta.2020.0210
[69]  Semmelrock, H., Ross‐Hellauer, T., Kopeinik, S., Theiler, D., Haberl, A., Thalmann, S., et al. (2025) Reproduci-bility in Machine-Learning-Based Research: Overview, Barriers, and Drivers. AI Magazine, 46, e70002. https://doi.org/10.1002/aaai.70002
[70]  Desai, A., Abdelhamid, M. and Padalkar, N.R. (2025) What Is Reproducibility in Artificial Intelligence and Machine Learning Research? AI Magazine, 46, e70004. https://doi.org/10.1002/aaai.70004
[71]  Sagi, O. and Rokach, L. (2021) Ap-proximating XGBoost with an Interpretable Decision Tree. Information Sciences, 572, 522-542. https://doi.org/10.1016/j.ins.2021.05.055
[72]  Luo, M., Wang, Y., Xie, Y., Zhou, L., Qiao, J., Qiu, S., et al. (2021) Combination of Feature Selection and CatBoost for Prediction: The First Application to the Estimation of Aboveground Biomass. Forests, 12, Article 216. https://doi.org/10.3390/f12020216
[73]  Huang, G., Wu, L., Ma, X., Zhang, W., Fan, J., Yu, X., et al. (2019) Evaluation of CatBoost Method for Prediction of Reference Evapotranspiration in Humid Regions. Journal of Hydrology, 574, 1029-1041. https://doi.org/10.1016/j.jhydrol.2019.04.085
[74]  Bottle, A., Gaudoin, R., Goudie, R., Jones, S. and Aylin, P. (2014) Can Valid and Practical Risk-Prediction or Casemix Adjustment Models, Including Adjustment for Comorbidity, Be Generated from English Hospital Administrative Data (hospital Episode Statistics)? A National Observational Study. Health Services and Deliv-ery Research, 2, 1-48. https://doi.org/10.3310/hsdr02400
[75]  Cerna, A.E.U., Pattichis, M., VanMaanen, D.P., Jing, L.Y., Patel, A.A., Stough, J.V., Haggerty, C.M. and Fornwalt, B.K. (2019) A Large-Scale Multimodal Study for Predicting Mortality Risk Using Minimal and Low Parameter Models and Sepa-rable Risk Assessment. arXiv: 1901.08125.
[76]  Hancock, J.T. and Khoshgof-taar, T.M. (2020) CatBoost for Big Data: An Interdisciplinary Review. Journal of Big Data, 7, Article No. 94. https://doi.org/10.1186/s40537-020-00369-8
[77]  Joshi, A., Saggar, P., Jain, R., Sharma, M., Gupta, D. and Khanna, A. (2021) CatBoost—An Ensemble Ma-chine Learning Model for Prediction and Classification of Student Academic Performance. Advances in Data Science and Adaptive Analysis, 13, Article ID: 2141002. https://doi.org/10.1142/s2424922x21410023
[78]  Ghosh, S.K. and Khandoker, A.H. (2024) Investigation on Explainable Machine Learning Models to Predict Chronic Kidney Diseases. Scientific Reports, 14, Article No. 3687. https://doi.org/10.1038/s41598-024-54375-4
[79]  Chen, H., Wang, M. and Li, J. (2024) Exploring the Association between Two Groups of Metals with Potentially Opposing Renal Effects and Renal Function in Middle-Aged and Older Adults: Evidence from an Explainable Machine Learning Method. Ecotox-icology and Environmental Safety, 269, Article ID: 115812. https://doi.org/10.1016/j.ecoenv.2023.115812
[80]  Simeri, A., Pezzi, G., Arena, R., Papalia, G., Szili-Torok, T., Greco, R., et al. (2024) Artificial Intelli-gence in Chronic Kidney Diseases: Methodology and Potential Applications. In-ternational Urology and Nephrology, 57, 159-168. https://doi.org/10.1007/s11255-024-04165-8
[81]  Rockenschaub, P., Hil-bert, A., Kossen, T., von Dincklage, F., Madai, V.I. and Frey, D. (2023) From Sin-gle-Hospital to Multi-Centre Applications: Enhancing the Generalisability of Deep Learning Models for Adverse Event Prediction in the ICU. arXiv: 2303.15354.
[82]  Moor, M., Bennett, N., Plečko, D., Horn, M., Rieck, B., Meinshausen, N., et al. (2023) Predicting Sepsis Using Deep Learning across International Sites: A Retrospective Development and Validation Study. eClini-calMedicine, 62, Article ID: 102124. https://doi.org/10.1016/j.eclinm.2023.102124

Full-Text


Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133




©2008-2020 OALib, Inc | Public user content licensed CC-BY 4.0 unless otherwise specified. OALib ISSN Online: 2333-9721 OALib ISSN Print: 2333-9705