FedICU: a federated learning model for reducing the medication prescription errors in intensive care units
AbstractPatients in the Intensive care unit need remarkable observation. This unit consists of people who are critically ill and may tend to lose their lives anytime. Healthcare professionals in critical care tend to commit prescription errors for many reasons. Since patients in intensive care are s...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/23311916.2023.2301150 |
| _version_ | 1797354003715063808 |
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| author | Vineetha Pais Santhosha Rao Balachandra Muniyal Sheng Yun |
| author_facet | Vineetha Pais Santhosha Rao Balachandra Muniyal Sheng Yun |
| author_sort | Vineetha Pais |
| collection | DOAJ |
| description | AbstractPatients in the Intensive care unit need remarkable observation. This unit consists of people who are critically ill and may tend to lose their lives anytime. Healthcare professionals in critical care tend to commit prescription errors for many reasons. Since patients in intensive care are severely ill and have complicated health issues, mistakes while prescribing medicines can have serious repercussions. In this study, a federated learning model is simulated to reduce the mistakes while prescribing medicines in the intensive care unit, which provides an opportunity for many hospitals to collaborate, keeping their data local to themselves. Local training is performed with Logistic regression, Simple neural network, and Multilayer perceptron in which simple neural network achieves the highest accuracy of 95%. Model weights transferred to a federated server may be vulnerable to data and model poisoning attacks, eavesdropping, and model inversion attacks. So, model weights are encrypted using Paillier homomorphic encryption (PHE), achieving a model accuracy of 93.26% for a key size of 2048. With key size, the effect of encryption and decryption time is observed. The model is also applied with differential privacy, which achieved an accuracy of 94.24% when c = 0.5 and sigma = 0.05. Thus, this privacy-preserving federated learning model can be used to reduce drug prescription errors in critical care. |
| first_indexed | 2024-03-08T13:39:06Z |
| format | Article |
| id | doaj.art-3d254ade78bc4e4daf9bfa34c7591193 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-08T13:39:06Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-3d254ade78bc4e4daf9bfa34c75911932024-01-16T11:53:07ZengTaylor & Francis GroupCogent Engineering2331-19162024-12-0111110.1080/23311916.2023.2301150FedICU: a federated learning model for reducing the medication prescription errors in intensive care unitsVineetha Pais0Santhosha Rao1Balachandra Muniyal2Sheng Yun3Department of Information & Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Information & Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaDepartment of Information & Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, IndiaGraduate School of Arts and Sciences, Fordham University, Bronx, NY, USAAbstractPatients in the Intensive care unit need remarkable observation. This unit consists of people who are critically ill and may tend to lose their lives anytime. Healthcare professionals in critical care tend to commit prescription errors for many reasons. Since patients in intensive care are severely ill and have complicated health issues, mistakes while prescribing medicines can have serious repercussions. In this study, a federated learning model is simulated to reduce the mistakes while prescribing medicines in the intensive care unit, which provides an opportunity for many hospitals to collaborate, keeping their data local to themselves. Local training is performed with Logistic regression, Simple neural network, and Multilayer perceptron in which simple neural network achieves the highest accuracy of 95%. Model weights transferred to a federated server may be vulnerable to data and model poisoning attacks, eavesdropping, and model inversion attacks. So, model weights are encrypted using Paillier homomorphic encryption (PHE), achieving a model accuracy of 93.26% for a key size of 2048. With key size, the effect of encryption and decryption time is observed. The model is also applied with differential privacy, which achieved an accuracy of 94.24% when c = 0.5 and sigma = 0.05. Thus, this privacy-preserving federated learning model can be used to reduce drug prescription errors in critical care.https://www.tandfonline.com/doi/10.1080/23311916.2023.2301150Federated learningmedication prescription errorintensive care unit (ICU)Paillier homomorphic encryptiondifferential privacyChen, Chang Gung University, Taiwan; Marko Robnik-Šikonja, University of Ljubljani, Slovenia |
| spellingShingle | Vineetha Pais Santhosha Rao Balachandra Muniyal Sheng Yun FedICU: a federated learning model for reducing the medication prescription errors in intensive care units Cogent Engineering Federated learning medication prescription error intensive care unit (ICU) Paillier homomorphic encryption differential privacy Chen, Chang Gung University, Taiwan; Marko Robnik-Šikonja, University of Ljubljani, Slovenia |
| title | FedICU: a federated learning model for reducing the medication prescription errors in intensive care units |
| title_full | FedICU: a federated learning model for reducing the medication prescription errors in intensive care units |
| title_fullStr | FedICU: a federated learning model for reducing the medication prescription errors in intensive care units |
| title_full_unstemmed | FedICU: a federated learning model for reducing the medication prescription errors in intensive care units |
| title_short | FedICU: a federated learning model for reducing the medication prescription errors in intensive care units |
| title_sort | fedicu a federated learning model for reducing the medication prescription errors in intensive care units |
| topic | Federated learning medication prescription error intensive care unit (ICU) Paillier homomorphic encryption differential privacy Chen, Chang Gung University, Taiwan; Marko Robnik-Šikonja, University of Ljubljani, Slovenia |
| url | https://www.tandfonline.com/doi/10.1080/23311916.2023.2301150 |
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