Machine learning for Forecasting quality of life variations in hemodialysis patients
DOI:
https://doi.org/10.56294/hl2024.395Keywords:
Machine learning (ML), classification tree, quality of life (QoL), hemodialysis, predictionAbstract
Objective: To anticipate changes in quality of life (QoL) evaluations for hemodialysis patients. over the course of the following month and to use ML to establish an early warning system.
Materials and methods: A hospital with a dialysis unit hosted the trial, which lasted one month and included an approaching group. Approximately 78 patients have been enrolled up to this date. Preformed including demographic information MBBS-degree holding medical professionals administered the validated WHO-BREF. It has to be done again on the same patient a month later by the same investigator. R and Orange were used for machine learning, while SPSS version 24 was used to provide basic statistics.
Results: In order to predict whether a patient's WHO-QOL-BREF score would increase or decrease by 5% over the course of a month, two models were developed using ML methods. A 5% or greater loss in QOL scores occurs over the course of the next month as a result of declines in the psychosomatic, substantial, and societal domain scores.
Conclusion: The Dialysis Data Interpretation for Algorithmic-Prediction on QOL early warning system based on ML was developed to identify quickly declining QOL scores in the hemdialysis sample. The model suggested that improving the psychological and ecological domains in exacting could be able to arrest the fall in QOL ratings. If DIAL is used more widely, it should benefit patients by guaranteeing a greater QOL and reducing the long-term cost burden.
References
1. Huang JC, Tsai YC, Wu PY, Lien YH, Chien CY, Kuo CF, Hung JF, Chen SC, Kuo CH. Predictive modeling of blood pressure during hemodialysis: A comparison of linear model, random forest, support vector regression, XGBoost, LASSO regression and ensemble method. Computer methods and programs in biomedicine. 2020 Oct 1;195:105536. https://doi.org/10.1016/j.cmpb.2020.105536
2. Burlacu A, Iftene A, Jugrin D, Popa IV, Lupu PM, Vlad C, Covic A. Using artificial intelligence resources in dialysis and kidney transplant patients: a literature review. BioMed research international. 2020;2020(1):9867872. https://doi.org/10.1155/2020/9867872
3. Qiu Y, Huang Y, Wang Y, Ren L, Jiang H, Zhang L, Dong C. The role of socioeconomic status, family resilience, and social support in predicting psychological resilience among Chinese maintenance hemodialysis patients. Frontiers in Psychiatry. 2021 Sep 30;12:723344. https://doi.org/10.3389/fpsyt.2021.723344
4. Ferguson TW, Whitlock RH, Bamforth RJ, Beaudry A, Darcel J, Di Nella M, Rigatto C, Tangri N, Komenda P. Cost-utility of dialysis in Canada: hemodialysis, peritoneal dialysis, and nondialysis treatment of kidney failure. Kidney medicine. 2021 Jan 1;3(1):20-30. https://doi.org/10.1016/j.xkme.2020.07.011
5. Sukul N, Karaboyas A, Csomor PA, Schaufler T, Wen W, Menzaghi F, Rayner HC, Hasegawa T, Al Salmi I, Al-Ghamdi SM, Guebre-Egziabher F. Self-reported pruritus and clinical, dialysis-related, and patient-reported outcomes in hemodialysis patients. Kidney medicine. 2021 Jan 1;3(1):42-53. https://doi.org/10.1016/j.xkme.2020.08.011
6. Tommel J, Evers AW, van Hamersvelt HW, Jordens R, van Dijk S, Hilbrands LB, van Middendorp H, E-HELD Study Group. Predicting health-related quality of life in dialysis patients: factors related to negative outcome expectancies and social support. Patient Education and Counseling. 2021 Jun 1;104(6):1474-80. https://doi.org/10.1016/j.pec.2020.11.019
7. Savić M, Kurbalija V, Ilić M, Ivanović M, Jakovetić D, Valachis A, Autexier S, Rust J, Kosmidis T. The application of machine learning techniques in prediction of quality of life features for cancer patients. Computer Science and Information Systems. 2023;20(1):381-404. https://doi.org/10.2298/CSIS220227061S
8. Senanayake S, White N, Graves N, Healy H, Baboolal K, Kularatna S. Machine learning in predicting graft failure following kidney transplantation: A systematic review of published predictive models. International journal of medical informatics. 2019 Oct 1;130:103957. https://doi.org/10.1016/j.ijmedinf.2019.103957
9. Senanayake S, White N, Graves N, Healy H, Baboolal K, Kularatna S. Machine learning in predicting graft failure following kidney transplantation: A systematic review of published predictive models. International journal of medical informatics. 2019 Oct 1;130:103957. https://doi.org/10.1016/j.ijmedinf.2019.103957
10. Chaudhuri S, Han H, Usvyat L, Jiao Y, Sweet D, Vinson A, Steinberg SJ, Maddux D, Belmonte K, Brzozowski J, Bucci B. Machine learning directed interventions associate with decreased hospitalization rates in hemodialysis patients. International Journal of Medical Informatics. 2021 Sep 1;153:104541. https://doi.org/10.1016/j.ijmedinf.2021.104541
11. Baumbach L, List M, Grønne DT, Skou ST, Roos EM. Individualized predictions of changes in knee pain, quality of life and walking speed following patient education and exercise therapy in patients with knee osteoarthritis–A prognostic model study. Osteoarthritis and Cartilage. 2020 Sep 1;28(9):1191-201. https://doi.org/10.1016/j.joca.2020.05.014
12. Iivanainen S, Ekstrom J, Virtanen H, Kataja VV, Koivunen JP. Electronic patient-reported outcomes and machine learning in predicting immune-related adverse events of immune checkpoint inhibitor therapies. BMC Medical Informatics and Decision Making. 2021 Dec;21:1-8. https://doi.org/10.1186/s12911-021-01564-0
13. Chen Y, Huang S, Chen T, Liang D, Yang J, Zeng C, Li X, Xie G, Liu Z. Machine learning for prediction and risk stratification of lupus nephritis renal flare. American Journal of Nephrology. 2021 Jul 12;52(2):152-60. https://doi.org/10.1159/000513566
14. Deng Y, Lu L, Aponte L, Angelidi AM, Novak V, Karniadakis GE, Mantzoros CS. Deep transfer learning and data augmentation improve glucose levels prediction in type 2 diabetes patients. NPJ Digital Medicine. 2021 Jul 14;4(1):109. https://doi.org/10.1038/s41746-021-00480-x
15. Lee R, Choi H, Park KY, Kim JM, Seok JW. Prediction of post-stroke cognitive impairment using brain FDG PET: deep learning-based approach. European Journal of Nuclear Medicine and Molecular Imaging. 2022 Mar 1:1-9. https://doi.org/10.1007/s00259-021-05556-0
16. Yang X, Zhao D, Yu F, Heidari AA, Bano Y, Ibrohimov A, Liu Y, Cai Z, Chen H, Chen X. An optimized machine learning framework for predicting intradialytic hypotension using indexes of chronic kidney disease-mineral and bone disorders. Computers in Biology and Medicine. 2022 Jun 1;145:105510. https://doi.org/10.1016/j.compbiomed.2022.105510
17. Hu J, Liu Y, Heidari AA, Bano Y, Ibrohimov A, Liang G, Chen H, Chen X, Zaguia A, Turabieh H. An effective model for predicting serum albumin level in hemodialysis patients. Computers in Biology and Medicine. 2022 Jan 1;140:105054. https://doi.org/10.1016/j.compbiomed.2021.105054
18. Barbieri C, Cattinelli I, Neri L, Mari F, Ramos R, Brancaccio D, Canaud B, Stuard S. Development of an artificial intelligence model to guide the management of blood pressure, fluid volume, and dialysis dose in end-stage kidney disease patients: proof of concept and first clinical assessment. Kidney diseases. 2019 Feb 1;5(1):28-33. https://doi.org/10.1159/000493479
19. Hiramatsu R, Ubara Y, Sawa N, Sakai A. Hypocalcemia and bone mineral changes in hemodialysis patients with low bone mass treated with denosumab: a 2-year observational study. Nephrology Dialysis Transplantation. 2021 Oct 1;36(10):1900-7. https://doi.org/10.1093/ndt/gfaa359
20. Liu YS, Yang CY, Chiu PF, Lin HC, Lo CC, Lai AS, Chang CC, Lee OK. Machine learning analysis of time-dependent features for predicting adverse events during hemodialysis therapy: Model development and validation study. Journal of Medical Internet Research. 2021 Sep 7;23(9):e27098. https://doi.org/10.2196/27098
21. Saberi-Karimian M, Khorasanchi Z, Ghazizadeh H, Tayefi M, Saffar S, Ferns GA, Ghayour-Mobarhan M. Potential value and impact of data mining and machine learning in clinical diagnostics. Critical reviews in clinical laboratory sciences. 2021 May 19;58(4):275-96. https://doi.org/10.1080/10408363.2020.1857681
22. Greenwood SA, Koufaki P, Macdonald JH, Bhandari S, Burton JO, Dasgupta I, Farrington K, Ford I, Kalra PA, Kean S, Kumwenda M. Randomized trial—prescription of intradialytic exercise to improve quality of life in patients receiving hemodialysis. Kidney International Reports. 2021 Aug 1;6(8):2159-70. https://doi.org/10.1016/j.ekir.2021.05.034
23. Tran NK, Sen S, Palmieri TL, Lima K, Falwell S, Wajda J, Rashidi HH. Artificial intelligence and machine learning for predicting acute kidney injury in severely burned patients: a proof of concept. Burns. 2019 Sep 1;45(6):1350-8. https://doi.org/10.1016/j.burns.2019.03.021
24. Moghadas-Dastjerdi H, Sha-E-Tallat HR, Sannachi L, Sadeghi-Naini A, Czarnota GJ. A priori prediction of tumour response to neoadjuvant chemotherapy in breast cancer patients using quantitative CT and machine learning. Scientific Reports. 2020 Jul 2;10(1):10936. https://doi.org/10.1038/s41598-020-67823-8.
Published
Issue
Section
License
Copyright (c) 2024 Jamuna KV, Uma Bhardwaj, Jagdish Gohil, Jitendriya Biswal, Nimesh Raj, Lovish Dhingra, V. C. Patil (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
