Recent Advances and Future Perspectives in the Use of Machine Learning and Mathematical Models in Nephrology
Tài liệu tham khảo
2021
Johansen, 2021, US renal data system 2020 Annual data Report: Epidemiology of kidney disease in the United States, Am J Kidney Dis, 77, A7, 10.1053/j.ajkd.2021.01.002
Kooman, 2020, Wearable health devices and personal area networks: can they improve outcomes in haemodialysis patients?, Nephrol Dial Transplant, 35, ii43, 10.1093/ndt/gfaa015
Mackey, 2015, What has mathematics done for biology?, Bull Math Biol, 77, 735, 10.1007/s11538-015-0065-9
Cohen, 2004, Mathematics is Biology’s next microscope, only better; biology is mathematics’ next physics, only better, Plos Biol, 2, e439, 10.1371/journal.pbio.0020439
Russell, 2020
Rudin, 2019, Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead, Nat Mach Intell, 1, 206, 10.1038/s42256-019-0048-x
Ahmad, 2018, Interpretable machine learning in healthcare, 447
Zech, 2018, Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study, PLoS Med, 15, e1002683, 10.1371/journal.pmed.1002683
Lee, 2021, Deep learning model for real-time prediction of Intradialytic Hypotension, Clin J Am Soc Nephrol, 16, 396, 10.2215/CJN.09280620
Strobl, 2007, Bias in random forest variable importance measures: Illustrations, sources and a solution, BMC Bioinformatics, 8, 25, 10.1186/1471-2105-8-25
Subbaswamy, 2019, From development to deployment: dataset shift, causality, and shift-stable models in health AI, Biostatistics, 21, 345
Guo, 2022, Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine, Sci Rep, 12, 2726, 10.1038/s41598-022-06484-1
Wong, 2021, Quantification of sepsis model alerts in 24 US hospitals before and during the COVID-19 pandemic, JAMA Netw Open, 4, e2135286, 10.1001/jamanetworkopen.2021.35286
Lenert, 2019, Prognostic models will be victims of their own success, unless…, J Am Med Inform Assoc, 26, 1645, 10.1093/jamia/ocz145
Biswas, 2021, Introduction to supervised machine learning, Kidney360, 2, 878, 10.34067/KID.0000182021
Di Paola L. Artificial kidney: a chemical engineering challenge, In: Basile A, Annesini MC, Piemonte V, Charcosset C, eds. Current Trends and Future Developments on (Bio-) Membranes. Elsevier; Amsterdam, The Netherlands; 2020:1–20.
Ursino, 2000, Prediction of solute kinetics, acid-base status, and blood volume changes during profiled hemodialysis, Ann Biomed Eng, 28, 204, 10.1114/1.245
Sargent, 2018, Acid-base homeostasis during hemodialysis: new insights into the mystery of bicarbonate disappearance during treatment, Semin Dial, 31, 468, 10.1111/sdi.12714
Marano, 2019, Frontiers in hemodialysis: solutions and implications of mathematical models for bicarbonate restoring, Biomed Signal Process Control, 52, 321, 10.1016/j.bspc.2019.02.029
Leypoldt, 2020, Acid–base kinetics during hemodialysis using bicarbonate and lactate as dialysate buffer bases based on the H+ mobilization model, Int J Artif Organs, 43, 645, 10.1177/0391398820906524
Cherif, 2020, A mathematical model of the four cardinal acid-base disorders, Math Biosci Eng, 17, 4457, 10.3934/mbe.2020246
Maheshwari, 2020, A model-based analysis of phenytoin and carbamazepine toxicity treatment using binding-competition during hemodialysis, Sci Rep, 10, 1, 10.1038/s41598-020-68333-3
Maheshwari, 2019, In silico comparison of protein-bound uremic toxin removal by hemodialysis, hemodiafiltration, membrane adsorption, and binding competition, Sci Rep, 9, 1, 10.1038/s41598-018-37195-1
Maheshwari, 2021, Removal of protein-bound uremic toxins using binding competitors in hemodialysis: a narrative review, Toxins (Basel), 13, 622, 10.3390/toxins13090622
Sano, 2021, Analytical solutions of a two-compartment model based on the volume-average theory for blood toxin concentration during and after dialysis, Membranes (Basel), 11, 506, 10.3390/membranes11070506
Waniewski, 2020, Impact of solute exchange between erythrocytes and plasma on hemodialyzer clearance, Biocybern Biomed Eng, 40, 265, 10.1016/j.bbe.2019.04.003
Pietribiasi, 2018, Model of fluid and solute shifts during hemodialysis with active transport of sodium and potassium, PLoS One, 13, 1, 10.1371/journal.pone.0209553
Ursino, 2017, Mathematical model of potassium profiling in chronic dialysis, Contrib Nephrol, 190, 134, 10.1159/000468960
Pietribiasi, 2020, Comparison of two single-solute models of potassium kinetics during hemodialysis, Biocybern Biomed Eng, 40, 938, 10.1016/j.bbe.2020.04.001
Poleszczuk, 2016, Phosphate kinetics in hemodialysis: application of delayed pseudo one-compartment model, Blood Purif, 42, 177, 10.1159/000445934
Stecz, 2019, Application of dynamic optimisation for planning a haemodialysis process, BMC Nephrol, 20, 236, 10.1186/s12882-019-1409-8
Leypoldt, 2013, Phosphorus kinetics during hemodiafiltration: analysis using a pseudo-one-compartment model, Blood Purif, 35, 59, 10.1159/000346356
Agar, 2011, Kinetic model of phosphorus mobilization during and after short and conventional hemodialysis, Clin J Am Soc Nephrol, 6, 2854, 10.2215/CJN.03860411
Debowska, 2015, Phosphate kinetics during weekly cycle of hemodialysis sessions: application of mathematical modeling, Artif Organs, 39, 1005, 10.1111/aor.12489
Gotch, 2006, A kinetic model of calcium mass balance during dialysis therapy, Blood Purif, 25, 139, 10.1159/000096891
Pstras, 2020, Transcapillary transport of water, small solutes and proteins during hemodialysis, Sci Rep, 10, 1, 10.1038/s41598-020-75687-1
Pietribiasi, 2016, Modelling transcapillary transport of fluid and proteins in hemodialysis patients, PLoS One, 11, 1, 10.1371/journal.pone.0159748
De Los Reyes, 2016, A physiologically based model of vascular refilling during ultrafiltration in hemodialysis, J Theor Biol, 390, 146, 10.1016/j.jtbi.2015.11.012
Casagrande, 2016, Patient-specific modeling of multicompartmental fluid and mass exchange during dialysis, Int J Artif Organs, 39, 220, 10.5301/ijao.5000504
Joseph, 2021, Using a human circulation mathematical model to simulate the effects of hemodialysis and therapeutic hypothermia, Appl Sci, 12, 307, 10.3390/app12010307
Droog, 2012, Mathematical modeling of thermal and circulatory effects during hemodialysis, Artif Organs, 36, 797, 10.1111/j.1525-1594.2012.01464.x
Leypoldt, 2019, Intradialytic kinetics of middle molecules during hemodialysis and hemodiafiltration, Nephrol Dial Transplant, 34, 870, 10.1093/ndt/gfy304
Maheshwari, 2011, A regional blood flow model for β2-microglobulin kinetics and for simulating intra-dialytic exercise effect, Ann Biomed Eng, 39, 2879, 10.1007/s10439-011-0383-5
Maduell, 2015, Mathematical modeling of different molecule removal on on-line Haemodiafiltration: influence of dialysis duration and infusion flow, Blood Purif, 39, 288, 10.1159/000375287
Rodríguez, 2019, Vancomycin hemodialysis: clearance differences between high-flux hemodialysis and on-line hemodiafiltration, Artif Organs, 43, 261, 10.1111/aor.13368
Samandari, 2017, Variable-volume kinetic model to estimate absolute blood volume in patients on dialysis using dialysate dilution, ASAIO J, 64, 77, 10.1097/MAT.0000000000000608
Schappacher-Tilp, 2019, A mathematical model of parathyroid gland biology, Physiol Rep, 7, 1, 10.14814/phy2.14045
Schneditz, 2021, Modeling of insulin secretion and insulin mass balance during hemodialysis in patients with and without type 2 diabetes, Biocybern Biomed Eng, 41, 391, 10.1016/j.bbe.2021.02.009
Schneditz, 2013, A regional blood flow model for glucose and insulin kinetics during hemodialysis, ASAIO J, 59, 627, 10.1097/MAT.0000436714.72752.13
Bianchi, 2019, A Bayesian approach for the identification of patient-specific parameters in a dialysis kinetic model, Stat Methods Med Res, 28, 2069, 10.1177/0962280217745572
Fuertinger, 2018, Prediction of hemoglobin levels in individual hemodialysis patients by means of a mathematical model of erythropoiesis, PLoS One, 13, 1, 10.1371/journal.pone.0195918
Fuertinger, 2013, A model of erythropoiesis in adults with sufficient iron availability, J Math Biol, 66, 1209, 10.1007/s00285-012-0530-0
Barbieri, 2015, A new machine learning approach for predicting the response to anemia treatment in a large cohort of end stage renal disease patients undergoing dialysis, Comput Biol Med, 61, 56, 10.1016/j.compbiomed.2015.03.019
Singh, 2015, Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration, J Biomed Inform, 53, 220, 10.1016/j.jbi.2014.11.005
2012, KDIGO clinical practice Guideline for acute kidney injury, Kidney Int Suppl, 2, 1
Soranno, 2021, Artificial Intelligence for AKI!Now: Let’s not await Plato’s utopian republic, Kidney360, 3, 376, 10.34067/KID.0003472021
Liu, 2020, AKI!Now initiative: recommendations for awareness, recognition, and management of AKI, Clin J Am Soc Nephrol, 15, 1838, 10.2215/CJN.15611219
Vaid, 2021, Predictive approaches for acute dialysis requirement and death in COVID-19, Clin J Am Soc Nephrol, 16, 1158, 10.2215/CJN.17311120
Allen, 2017, Risk prediction models for contrast-induced acute kidney injury accompanying cardiac catheterization: systematic review and meta-analysis, Can J Cardiol, 33, 724, 10.1016/j.cjca.2017.01.018
Park, 2018, Predicting acute kidney injury in cancer patients using heterogeneous and irregular data, PLoS One, 13, e0199839, 10.1371/journal.pone.0199839
Chaudhary, 2020, Utilization of deep learning for subphenotype identification in sepsis-associated acute kidney injury, Clin J Am Soc Nephrol, 15, 1557, 10.2215/CJN.09330819
Serif, 2020, Application of 17 contrast-induced acute kidney injury risk prediction models, Cardiorenal Med, 10, 162, 10.1159/000506379
Koyner, 2018, The development of a machine learning inpatient acute kidney injury prediction model∗, Crit Care Med, 46, 1070, 10.1097/CCM.0000000000003123
Koyner, 2016, Development of a multicenter Ward–based AKI prediction model, Clin J Am Soc Nephrol, 11, 1935, 10.2215/CJN.00280116
Simonov, 2019, A simple real-time model for predicting acute kidney injury in hospitalized patients in the US: a descriptive modeling study, PLoS Med, 16, e1002861, 10.1371/journal.pmed.1002861
Churpek, 2020, Internal and external validation of a machine learning risk score for acute kidney injury, JAMA Netw Open, 3, e2012892, 10.1001/jamanetworkopen.2020.12892
Tomašev, 2019, A clinically applicable approach to continuous prediction of future acute kidney injury, Nature, 572, 116, 10.1038/s41586-019-1390-1
Davis, 2017, Calibration drift in regression and machine learning models for acute kidney injury, J Am Med Inform Assoc, 24, 1052, 10.1093/jamia/ocx030
Van Calster, 2019, Calibration: the achilles heel of predictive analytics, BMC Med, 17, 230, 10.1186/s12916-019-1466-7
Wilson, 2021, Electronic health record alerts for acute kidney injury: multicenter, randomized clinical trial, BMJ, 372, m4786, 10.1136/bmj.m4786
Almansour, 2019, Neural network and support vector machine for the prediction of chronic kidney disease: a comparative study, Comput Biol Med, 109, 101, 10.1016/j.compbiomed.2019.04.017
Misir, 2017, A reduced set of features for chronic kidney disease prediction, J Pathol Inform, 8, 24, 10.4103/jpi.jpi_88_16
Rashed-Al-Mahfuz, 2021, Clinically applicable machine learning approaches to identify attributes of chronic kidney disease (CKD) for Use in low-cost diagnostic screening, IEEE J Transl Eng Heal Med, 9, 1
Polat, 2017, Diagnosis of chronic kidney disease based on support vector machine by feature selection methods, J Med Syst, 41, 55, 10.1007/s10916-017-0703-x
Qin, 2020, A machine learning methodology for diagnosing chronic kidney disease, IEEE Access, 8, 20991, 10.1109/ACCESS.2019.2963053
Roy, 2021, Comparative analysis of machine learning methods to detect chronic kidney disease, J Phys Conf Ser, 1911, 012005, 10.1088/1742-6596/1911/1/012005
Chen, 2016, Clinical risk assessment of patients with chronic kidney disease by using clinical data and multivariate models, Int Urol Nephrol, 48, 2069, 10.1007/s11255-016-1346-4
Dua
Sobrinho, 2020, Computer-aided diagnosis of chronic kidney disease in developing countries: a comparative analysis of machine learning techniques, IEEE Access, 8, 25407, 10.1109/ACCESS.2020.2971208
Ma, 2021, Prediction of chronic kidney disease risk using multimodal data, 20
Xiao, 2019, Comparison and development of machine learning tools in the prediction of chronic kidney disease progression, J Transl Med, 17, 119, 10.1186/s12967-019-1860-0
Norouzi, 2016, Predicting renal failure progression in chronic kidney disease using integrated intelligent Fuzzy Expert system, Comput Math Methods Med, 2016, 1, 10.1155/2016/6080814
Christodoulou, 2019, A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models, J Clin Epidemiol, 110, 12, 10.1016/j.jclinepi.2019.02.004
Mezzatesta, 2019, A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis, Comput Methods Programs Biomed, 177, 9, 10.1016/j.cmpb.2019.05.005
Chan, 2020, Natural language processing of electronic health records is superior to billing codes to identify symptom burden in hemodialysis patients, Kidney Int, 97, 383, 10.1016/j.kint.2019.10.023
Zheng, 2021, Deep-learning–driven quantification of interstitial fibrosis in digitized kidney Biopsies, Am J Pathol, 191, 1442, 10.1016/j.ajpath.2021.05.005
Ginley, 2021, Automated computational detection of interstitial fibrosis, tubular atrophy, and glomerulosclerosis, J Am Soc Nephrol, 32, 837, 10.1681/ASN.2020050652
Ginley, 2019, Computational segmentation and classification of diabetic glomerulosclerosis, J Am Soc Nephrol, 30, 1953, 10.1681/ASN.2018121259
Marsh, 2021, Development and validation of a deep learning model to quantify glomerulosclerosis in kidney biopsy Specimens, JAMA Netw Open, 4, 1, 10.1001/jamanetworkopen.2020.30939
Bouteldja, 2021, Deep learning-based segmentation and quantification in experimental kidney histopathology, J Am Soc Nephrol, 32, 52, 10.1681/ASN.2020050597
Hermsen, 2019, Deep learning–based histopathologic assessment of kidney tissue, J Am Soc Nephrol, 30, 1968, 10.1681/ASN.2019020144
Jayapandian, 2021, Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains, Kidney Int, 99, 86, 10.1016/j.kint.2020.07.044
de Haan, 2021, Deep learning-based transformation of H&E stained tissues into special stains, Nat Commun, 12, 1, 10.1038/s41467-021-25221-2
Kolachalama, 2018, Association of Pathological fibrosis with renal survival using deep neural networks, Kidney Int Rep, 3, 464, 10.1016/j.ekir.2017.11.002
de Silva, 2020, PySINDy: a Python package for the sparse identification of nonlinear dynamical systems from data, J Open Source Softw, 5, 2104, 10.21105/joss.02104