Division in Unity: Towards Efficient and Privacy-Preserving Learning of Healthcare Data
Source abstract
Abstract
The isolation of healthcare data among worldwide hospitals and institutes forms barriers for fully realizing the data-hungry artificial intelligence (AI) models promises in renewing medical services. To overcome this, privacy... | Find, read and cite all the research you need on Tech Science Press
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Research context
OpenAlex topics: Privacy-Preserving Technologies in Data · AI in cancer detection · Machine Learning in Healthcare
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (42)
Show source-provided references
1. Health disparities and health equity: concepts and measurement10.1146/annurev.publhealth.27.021405.102103 ↗
2. Federated machine learning in healthcare: a systematic review on clinical applications and technical architecture
3. Leveraging heuristic client selection for enhanced secure federated submodel learning10.1016/j.ipm.2022.103211 ↗
4. Seeing is believing: towards interactive visual exploration of data privacy in federated learning10.1016/j.ipm.2022.103162 ↗
5. The Internet of Things under federated learning: a review of the latest advances and applications
6. Seqcare: sequential training with external medical knowledge graph for diagnosis prediction in healthcare data
7. VecoCare: visit sequences–clinical notes joint learning for diagnosis prediction in healthcare data
8. Cross-hospital sepsis early detection via semi-supervised optimal transport with self-paced ensemble10.1109/JBHI.2023.3253208 ↗
9. The third international consensus definitions for sepsis and septic shock (Sepsis-3)10.1001/jama.2016.0287 ↗
10. Data heterogeneity in federated learning with Electronic Health Records: case studies of risk prediction for acute kidney injury and sepsis diseases in critical care10.1371/journal.pdig.0000117 ↗
11. FedCPS: a dual optimization model for federated learning based on clustering and personalization strategy
12. Understanding clinical collaborations through federated classifier selection
13. Vepakomma P, Gupta O, Swedish T, Raskar R. Split learning for health: distributed deep learning without sharing raw patient data. arXiv:1812.00564. 2018.
14. Splitfed: when federated learning meets split learning
15. A survey on transfer learning
16. Using transfer learning for improved mortality prediction in a data-scarce hospital setting10.1177/1178222617712994 ↗
17. Ensembling neural networks for improved prediction and privacy in early diagnosis of sepsis
18. Early sepsis prediction using ensemble learning with deep features and artificial features extracted from clinical electronic health records10.1097/CCM.0000000000004644 ↗
19. Bi-directional gated recurrent unit based ensemble model for the early detection of sepsis
20. Prediction of sepsis mortality risk based on ensemble learning algorithm FBTV
21. Sepsis prediction in intensive care unit based on genetic feature optimization and stacked deep ensemble learning10.1007/s00521-021-06631-1 ↗
22. Enhancing predictions with a stacking ensemble model for ICU mortality risk in patients with sepsis-associated encephalopathy10.1177/03000605241239013 ↗
23. The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit10.1016/j.ijmedinf.2020.104176 ↗
24. Comparison of different machine learning algorithms to classify patients suspected of having sepsis infection in the intensive care unit10.1016/j.imu.2023.101236 ↗
25. A machine learning model for accurate prediction of sepsis in ICU patients10.3389/fpubh.2021.754348 ↗
26. Early sepsis detection in critical care patients using multiscale blood pressure and heart rate dynamics10.1016/j.jelectrocard.2017.08.013 ↗
27. A data-driven approach to predicting septic shock in the intensive care unit10.1177/1178222619885147 ↗
28. Machine learning for early prediction of sepsis in Intensive Care Unit (ICU) Patients10.3390/medicina59071276 ↗
29. Exploring a global interpretation mechanism for deep learning networks when predicting sepsis10.1038/s41598-023-30091-3 ↗
30. Candidate-heuristic in-context learning: a new framework for enhancing medical visual question answering with LLMs10.1016/j.ipm.2024.103805 ↗
31. Clinical applications of machine learning in the survival prediction and classification of sepsis: coagulation and heparin usage matter10.1186/s12967-022-03469-6 ↗
32. Sepsis prediction using CNNBDLSTM and temporal derivatives feature extraction in the IoT medical environment
33. An efficient and private ECG classification system using split and semi-supervised learning10.1109/JBHI.2023.3281977 ↗
34. Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare10.1038/s41467-021-20910-4 ↗
35. Moor M, Bennet N, Plecko D, Horn M, Rieck B, Meinshausen N, et al. Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning. arXiv:2107.05230. 2021.
36. Adams S, Choudhary C, De Cock M, Dowsley R, Melanson D, Nascimento AC, et al. Privacy-preserving training of tree ensembles over continuous data. arXiv:2106.02769. 2021.10.2478/popets-2022-0042 ↗
37. Federated ensemble-learning for transport mode detection in vehicular edge network10.1016/j.future.2023.07.022 ↗
38. Enhancing IoT security and healthcare data protection in the metaverse: a dynamic adaptive security mechanism10.1016/j.eij.2025.100670 ↗
39. Blockchain-enabled EHR access auditing: enhancing healthcare data security10.1016/j.heliyon.2024.e34407 ↗
40. Early prediction of sepsis from clinical data: the PhysioNet/Computing in Cardiology Challenge 2019
41. Advanced composition theorems for differential obliviousness
42. Is the qSOFA more reliable than SIRS in detecting post-operative PCNL patients requiring escalation of care?10.1177/2051415819854889 ↗