Araştırma Makalesi
Yıl 2023,
Cilt: 5 Sayı: 2, 91 - 102, 31.12.2023
Öz
Kaynakça
- World Health Organisation, “14.9 million excess deaths associated with the covid-19 pandemic in 2020 and 2021,” 2022.
- Y. M. Arabi, S. N. Myatra, and S. M. Lobo, “Surging icu during covid-19 pandemic: an overview,” Current Opinion in Critical Care, vol. 28, no. 6, p. 638, 2022.
- G. R. Gristina and M. Piccinni, “Covid-19 pandemic in icu. limited resources for many patients: approaches and criteria for triaging,” Minerva Anestesiologica, vol. 87, 2021.
- G. L. Anesi and M. P. Kerlin, “The impact of resource limitations on care delivery and outcomes: routine variation, the coronavirus disease 2019 pandemic, and persistent shortage,” 2021.
- R.-H. Du, L.-M. Liu, W. Yin, W. Wang, L.-L. Guan, M.-L. Yuan, Y.-L. Li, Y. Hu, X.-Y. Li, B. Sun, et al., “Hospitalization and critical care of 109 decedents with covid-19 pneumonia in wuhan, china,” Annals of the American Thoracic Society, vol. 17, no. 7, pp. 839–846, 2020.
- A. Olivas-Martinez, J. L. C ́ardenas-Fragoso, J. V. Jim ́enez, O. A. Lozano-Cruz, E. Ortiz- Brizuela, V. H. Tovar-M ́endez, C. Medrano-Borromeo, A. Martinez-Valenzuela, C. M. Rom ́an- Montes, B. Martinez-Guerra, et al., “In-hospital mortality from severe covid-19 in a tertiary care center in mexico city; causes of death, risk factors and the impact of hospital saturation,” PloS one, vol. 16, no. 2, p. e0245772, 2021.
- G. French, M. Hulse, D. Nguyen, K. Sobotka, K. Webster, J. Corman, B. Aboagye-Nyame, M. Dion, M. Johnson, B. Zalinger, et al., “Impact of hospital strain on excess deaths during the covid-19 pandemic—united states, july 2020–july 2021,” Morbidity and Mortality Weekly Report, vol. 70, no. 46, p. 1613, 2021.
- P. Eggimann, J. Bille, and O. Marchetti, “Diagnosis of invasive candidiasis in the icu,” Annals of Intensive Care, vol. 1, 2011.
- S. p. Kafantaris and O. Kadda, “Advantages and disadvantages of patients’ hospitalization in intensive care units,” Health amp; Research Journal, vol. 7, p. 155–159, Oct. 2021.
- S. A. Candan, N. Elibol, and A. Abdullahi, “Consideration of prevention and management of long-term consequences of post-acute respiratory distress syndrome in patients with covid- 19,” Physiotherapy Theory and Practice, vol. 36, 2020.
- A. Dauvin, C. Donado, P. Bachtiger, K. C. Huang, C. M. Sauer, D. Ramazzotti, M. Bon- vini, L. A. Celi, and M. J. Douglas, “Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients,” npj Digital Medicine, vol. 2, 2019.
- B. Lee, K. Kim, H. Hwang, Y. S. Kim, E. H. Chung, J. S. Yoon, H. J. Cho, and J. D. Park, “Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission,” Scientific Reports, vol. 11, 2021.
- S. Subudhi, A. Verma, A. B. Patel, C. C. Hardin, M. J. Khandekar, H. Lee, D. McEvoy, T. Stylianopoulos, L. L. Munn, S. Dutta, and R. K. Jain, “Comparing machine learning algorithms for predicting icu admission and mortality in covid-19,” npj Digital Medicine, vol. 4, 2021.
- S. Schmidt, J. K. Dieks, M. Quintel, and O. Moerer, “Critical care echocardiography as a routine procedure for the detection and early treatment of cardiac pathologies,” Diagnostics, vol. 10, 2020.
- R. Sangani, E. Mokaya, H. Mujahid, S. Hadique, S. Culp, L. Constantine, and A. Moss, “Early palliative care intervention reduces icu readmissions in high-risk patients,” Chest, vol. 158, 2020.
- N. Rieke, J. Hancox, W. Li, F. Milletar`ı, H. R. Roth, S. Albarqouni, S. Bakas, M. N. Galtier, B. A. Landman, K. Maier-Hein, S. Ourselin, M. Sheller, R. M. Summers, A. Trask, D. Xu, M. Baust, and M. J. Cardoso, “The future of digital health with federated learning,” npj Digital Medicine, vol. 3, 2020.
- B. Liu, M. Ding, S. Shaham, W. Rahayu, F. Farokhi, and Z. Lin, “When machine learning meets privacy: A survey and outlook,” 2021.
- T. Yang, G. Andrew, H. Eichner, H. Sun, W. Li, N. Kong, D. Ramage, and F. Beaufays, “Applied federated learning: Improving google keyboard query suggestions,” arXiv preprint arXiv:1812.02903, 2018.
- M. J. Sheller, G. A. Reina, B. Edwards, J. Martin, and S. Bakas, “Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmenta- tion,” in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part I 4, pp. 92–104, Springer, 2019.
- SALUD, “Informaci ́on referente a casos covid-19 en m ́exico,” 2023.
- 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, vol. 18, no. 6, pp. 275–285, 2004.
- G. Biau and E. Scornet, “A random forest guided tour,” Test, vol. 25, pp. 197–227, 2016.
- P. Langley, W. Iba, K. Thompson, et al., “An analysis of bayesian classifiers,” in Aaai, vol. 90, pp. 223–228, Citeseer, 1992.
- S. Suthaharan and S. Suthaharan, “Support vector machine,” Machine learning models and algorithms for big data classification: thinking with examples for effective learning, pp. 207– 235, 2016.
- Y. LeCun, Y. Bengio, et al., “Convolutional networks for images, speech, and time series,” The handbook of brain theory and neural networks, vol. 3361, no. 10, p. 1995, 1995.
- J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities.,” Proceedings of the national academy of sciences, vol. 79, no. 8, pp. 2554–2558, 1982.
- E. R ̈o ̈osli, S. Bozkurt, and T. Hernandez-Boussard, “Peeking into a black box, the fairness and generalizability of a mimic-iii benchmarking model,” Scientific Data, vol. 9, 2022.
- R. Poulain, M. F. B. Tarek, and R. Beheshti, “Improving fairness in ai models on electronic health records: The case for federated learning methods,” 2023.
- J. Hong, Z. Zhu, S. Yu, Z. Wang, H. H. Dodge, and J. Zhou, “Federated adversarial debiasing for fair and transferable representations,” 2021.
- J. W. Bos, K. Lauter, and M. Naehrig, “Private predictive analysis on encrypted medical data,” Journal of Biomedical Informatics, vol. 50, 2014.
- E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov, “How to backdoor federated learning,” vol. 108, 2020.
- Z. Wang, M. Song, Z. Zhang, Y. Song, Q. Wang, and H. Qi, “Beyond inferring class repre- sentatives: User-level privacy leakage from federated learning,” vol. 2019-April, 2019.
- A. Barros, D. Ros ́ario, E. Cerqueira, and N. L. da Fonseca, “A strategy to the reduction of communication overhead and overfitting in federated learning,” in Anais do XXVI Workshop de Gerˆencia e Opera ̧c ̃ao de Redes e Servi ̧cos, pp. 1–13, SBC, 2021.
- L. Lyu, X. Xu, Q. Wang, and H. Yu, “Collaborative fairness in federated learning,” Federated Learning: Privacy and Incentive, pp. 189–204, 2020.
- X. Zhang, Y. Li, W. Li, K. Guo, and Y. Shao, “Personalized federated learning via variational bayesian inference,” in International Conference on Machine Learning, pp. 26293–26310, PMLR, 2022.
- A. Li, L. Zhang, J. Tan, Y. Qin, J. Wang, and X. Y. Li, “Sample-level data selection for federated learning,” vol. 2021-May, 2021.
- Q. Zhang, Z. Bu, K. Chen, and Q. Long, “Differentially private bayesian neural networks on accuracy, privacy and reliability,” vol. 13716 LNAI, 2023
Decentralized Machine Learning Approach on ICU Admission Prediction for Enhanced Patient Care Using COVID-19 Data
Öz
The Intensive Care Unit (ICU) represents a constrained healthcare resource, involving invasive procedures and high costs, with significant psychological effects on patients and their families. The traditional approach to ICU admissions relies on observable behavioral indicators like breathing patterns and consciousness levels, which may lead to delayed critical care due to deteriorating conditions. Therefore, in the ever-evolving healthcare landscape, predicting whether patients will require admission to the ICU plays a pivotal role in optimizing resource allocation, improving patient outcomes, and reducing healthcare costs. Essentially, in the context of the post-COVID-19 pandemic, aside from many other diseases, this prediction not only forecasts the likelihood of ICU admission but also identifies patients at an earlier stage, allowing for timely interventions that can potentially mitigate the need for ICU care, thereby improving overall patient outcomes and healthcare resource utilization. However, this task usually requires a lot of diverse data from different healthcare institutions for a good predictive model, leading to concerns regarding sensitive data privacy. This paper aims to build a decentralized model using deep learning techniques while maintaining data privacy among different institutions to address these challenges.
Anahtar Kelimeler
Machine Learning Federated Learning Data Privacy Biomedical Data Science
Kaynakça
- World Health Organisation, “14.9 million excess deaths associated with the covid-19 pandemic in 2020 and 2021,” 2022.
- Y. M. Arabi, S. N. Myatra, and S. M. Lobo, “Surging icu during covid-19 pandemic: an overview,” Current Opinion in Critical Care, vol. 28, no. 6, p. 638, 2022.
- G. R. Gristina and M. Piccinni, “Covid-19 pandemic in icu. limited resources for many patients: approaches and criteria for triaging,” Minerva Anestesiologica, vol. 87, 2021.
- G. L. Anesi and M. P. Kerlin, “The impact of resource limitations on care delivery and outcomes: routine variation, the coronavirus disease 2019 pandemic, and persistent shortage,” 2021.
- R.-H. Du, L.-M. Liu, W. Yin, W. Wang, L.-L. Guan, M.-L. Yuan, Y.-L. Li, Y. Hu, X.-Y. Li, B. Sun, et al., “Hospitalization and critical care of 109 decedents with covid-19 pneumonia in wuhan, china,” Annals of the American Thoracic Society, vol. 17, no. 7, pp. 839–846, 2020.
- A. Olivas-Martinez, J. L. C ́ardenas-Fragoso, J. V. Jim ́enez, O. A. Lozano-Cruz, E. Ortiz- Brizuela, V. H. Tovar-M ́endez, C. Medrano-Borromeo, A. Martinez-Valenzuela, C. M. Rom ́an- Montes, B. Martinez-Guerra, et al., “In-hospital mortality from severe covid-19 in a tertiary care center in mexico city; causes of death, risk factors and the impact of hospital saturation,” PloS one, vol. 16, no. 2, p. e0245772, 2021.
- G. French, M. Hulse, D. Nguyen, K. Sobotka, K. Webster, J. Corman, B. Aboagye-Nyame, M. Dion, M. Johnson, B. Zalinger, et al., “Impact of hospital strain on excess deaths during the covid-19 pandemic—united states, july 2020–july 2021,” Morbidity and Mortality Weekly Report, vol. 70, no. 46, p. 1613, 2021.
- P. Eggimann, J. Bille, and O. Marchetti, “Diagnosis of invasive candidiasis in the icu,” Annals of Intensive Care, vol. 1, 2011.
- S. p. Kafantaris and O. Kadda, “Advantages and disadvantages of patients’ hospitalization in intensive care units,” Health amp; Research Journal, vol. 7, p. 155–159, Oct. 2021.
- S. A. Candan, N. Elibol, and A. Abdullahi, “Consideration of prevention and management of long-term consequences of post-acute respiratory distress syndrome in patients with covid- 19,” Physiotherapy Theory and Practice, vol. 36, 2020.
- A. Dauvin, C. Donado, P. Bachtiger, K. C. Huang, C. M. Sauer, D. Ramazzotti, M. Bon- vini, L. A. Celi, and M. J. Douglas, “Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients,” npj Digital Medicine, vol. 2, 2019.
- B. Lee, K. Kim, H. Hwang, Y. S. Kim, E. H. Chung, J. S. Yoon, H. J. Cho, and J. D. Park, “Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission,” Scientific Reports, vol. 11, 2021.
- S. Subudhi, A. Verma, A. B. Patel, C. C. Hardin, M. J. Khandekar, H. Lee, D. McEvoy, T. Stylianopoulos, L. L. Munn, S. Dutta, and R. K. Jain, “Comparing machine learning algorithms for predicting icu admission and mortality in covid-19,” npj Digital Medicine, vol. 4, 2021.
- S. Schmidt, J. K. Dieks, M. Quintel, and O. Moerer, “Critical care echocardiography as a routine procedure for the detection and early treatment of cardiac pathologies,” Diagnostics, vol. 10, 2020.
- R. Sangani, E. Mokaya, H. Mujahid, S. Hadique, S. Culp, L. Constantine, and A. Moss, “Early palliative care intervention reduces icu readmissions in high-risk patients,” Chest, vol. 158, 2020.
- N. Rieke, J. Hancox, W. Li, F. Milletar`ı, H. R. Roth, S. Albarqouni, S. Bakas, M. N. Galtier, B. A. Landman, K. Maier-Hein, S. Ourselin, M. Sheller, R. M. Summers, A. Trask, D. Xu, M. Baust, and M. J. Cardoso, “The future of digital health with federated learning,” npj Digital Medicine, vol. 3, 2020.
- B. Liu, M. Ding, S. Shaham, W. Rahayu, F. Farokhi, and Z. Lin, “When machine learning meets privacy: A survey and outlook,” 2021.
- T. Yang, G. Andrew, H. Eichner, H. Sun, W. Li, N. Kong, D. Ramage, and F. Beaufays, “Applied federated learning: Improving google keyboard query suggestions,” arXiv preprint arXiv:1812.02903, 2018.
- M. J. Sheller, G. A. Reina, B. Edwards, J. Martin, and S. Bakas, “Multi-institutional deep learning modeling without sharing patient data: A feasibility study on brain tumor segmenta- tion,” in Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries: 4th International Workshop, BrainLes 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Revised Selected Papers, Part I 4, pp. 92–104, Springer, 2019.
- SALUD, “Informaci ́on referente a casos covid-19 en m ́exico,” 2023.
- 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, vol. 18, no. 6, pp. 275–285, 2004.
- G. Biau and E. Scornet, “A random forest guided tour,” Test, vol. 25, pp. 197–227, 2016.
- P. Langley, W. Iba, K. Thompson, et al., “An analysis of bayesian classifiers,” in Aaai, vol. 90, pp. 223–228, Citeseer, 1992.
- S. Suthaharan and S. Suthaharan, “Support vector machine,” Machine learning models and algorithms for big data classification: thinking with examples for effective learning, pp. 207– 235, 2016.
- Y. LeCun, Y. Bengio, et al., “Convolutional networks for images, speech, and time series,” The handbook of brain theory and neural networks, vol. 3361, no. 10, p. 1995, 1995.
- J. J. Hopfield, “Neural networks and physical systems with emergent collective computational abilities.,” Proceedings of the national academy of sciences, vol. 79, no. 8, pp. 2554–2558, 1982.
- E. R ̈o ̈osli, S. Bozkurt, and T. Hernandez-Boussard, “Peeking into a black box, the fairness and generalizability of a mimic-iii benchmarking model,” Scientific Data, vol. 9, 2022.
- R. Poulain, M. F. B. Tarek, and R. Beheshti, “Improving fairness in ai models on electronic health records: The case for federated learning methods,” 2023.
- J. Hong, Z. Zhu, S. Yu, Z. Wang, H. H. Dodge, and J. Zhou, “Federated adversarial debiasing for fair and transferable representations,” 2021.
- J. W. Bos, K. Lauter, and M. Naehrig, “Private predictive analysis on encrypted medical data,” Journal of Biomedical Informatics, vol. 50, 2014.
- E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov, “How to backdoor federated learning,” vol. 108, 2020.
- Z. Wang, M. Song, Z. Zhang, Y. Song, Q. Wang, and H. Qi, “Beyond inferring class repre- sentatives: User-level privacy leakage from federated learning,” vol. 2019-April, 2019.
- A. Barros, D. Ros ́ario, E. Cerqueira, and N. L. da Fonseca, “A strategy to the reduction of communication overhead and overfitting in federated learning,” in Anais do XXVI Workshop de Gerˆencia e Opera ̧c ̃ao de Redes e Servi ̧cos, pp. 1–13, SBC, 2021.
- L. Lyu, X. Xu, Q. Wang, and H. Yu, “Collaborative fairness in federated learning,” Federated Learning: Privacy and Incentive, pp. 189–204, 2020.
- X. Zhang, Y. Li, W. Li, K. Guo, and Y. Shao, “Personalized federated learning via variational bayesian inference,” in International Conference on Machine Learning, pp. 26293–26310, PMLR, 2022.
- A. Li, L. Zhang, J. Tan, Y. Qin, J. Wang, and X. Y. Li, “Sample-level data selection for federated learning,” vol. 2021-May, 2021.
- Q. Zhang, Z. Bu, K. Chen, and Q. Long, “Differentially private bayesian neural networks on accuracy, privacy and reliability,” vol. 13716 LNAI, 2023
Toplam 37 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Uygulamalı Matematik (Diğer) |
| Bölüm | Articles |
| Yazarlar | |
| Erken Görünüm Tarihi | 29 Aralık 2023 |
| Yayımlanma Tarihi | 31 Aralık 2023 |
| Gönderilme Tarihi | 14 Kasım 2023 |
| Kabul Tarihi | 7 Aralık 2023 |
| Yayımlandığı Sayı | Yıl 2023 Cilt: 5 Sayı: 2 |
The published articles in PIMS are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.