Privacy-aware early detection of COVID-19 through adversarial training
Early detection of COVID-19 is an ongoing area of research that can help with triage, monitoring and general health assessment of potential patients and may reduce operational strain on hospitals that cope with the coronavirus pandemic. Different machine learning techniques have been used in the lit...
| Main Authors: | , , , , , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
IEEE
2022
|
| _version_ | 1826309955399974912 |
|---|---|
| author | Rohanian, M Kouchaki, S Soltan, A Yang, J Rohanian, M Yang, Y Clifton, DA |
| author_facet | Rohanian, M Kouchaki, S Soltan, A Yang, J Rohanian, M Yang, Y Clifton, DA |
| author_sort | Rohanian, M |
| collection | OXFORD |
| description | Early detection of COVID-19 is an ongoing
area of research that can help with triage, monitoring and
general health assessment of potential patients and may
reduce operational strain on hospitals that cope with the
coronavirus pandemic. Different machine learning techniques have been used in the literature to detect potential
cases of coronavirus using routine clinical data (blood
tests, and vital signs measurements). Data breaches and
information leakage when using these models can bring
reputational damage and cause legal issues for hospitals.
In spite of this, protecting healthcare models against leakage of potentially sensitive information is an understudied
research area. In this study, two machine learning techniques that aim to predict a patient’s COVID-19 status are
examined. Using adversarial training, robust deep learning
architectures are explored with the aim to protect attributes
related to demographic information about the patients. The
two models examined in this work are intended to preserve
sensitive information against adversarial attacks and information leakage. In a series of experiments using datasets
from the Oxford University Hospitals (OUH), Bedfordshire
Hospitals NHS Foundation Trust (BH), University Hospitals
Birmingham NHS Foundation Trust (UHB), and Portsmouth
Hospitals University NHS Trust (PUH), two neural networks
are trained and evaluated. These networks predict PCR test
results using information from basic laboratory blood tests,
and vital signs collected from a patient upon arrival to the
hospital. The level of privacy each one of the models can
provide is assessed and the efficacy and robustness of
the proposed architectures are compared with a relevant
baseline. One of the main contributions in this work is the
particular focus on the development of effective COVID19 detection models with built-in mechanisms in order to
selectively protect sensitive attributes against adversarial
attacks. The results on hold-out test set and external validation confirmed that there was no impact on the generalisibility of the model using adversarial learning. |
| first_indexed | 2024-03-07T07:43:26Z |
| format | Journal article |
| id | oxford-uuid:199195c8-2271-4efa-9a42-bad9e4f18449 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:43:26Z |
| publishDate | 2022 |
| publisher | IEEE |
| record_format | dspace |
| spelling | oxford-uuid:199195c8-2271-4efa-9a42-bad9e4f184492023-05-19T10:03:50ZPrivacy-aware early detection of COVID-19 through adversarial trainingJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:199195c8-2271-4efa-9a42-bad9e4f18449EnglishSymplectic ElementsIEEE2022Rohanian, MKouchaki, SSoltan, AYang, JRohanian, MYang, YClifton, DAEarly detection of COVID-19 is an ongoing area of research that can help with triage, monitoring and general health assessment of potential patients and may reduce operational strain on hospitals that cope with the coronavirus pandemic. Different machine learning techniques have been used in the literature to detect potential cases of coronavirus using routine clinical data (blood tests, and vital signs measurements). Data breaches and information leakage when using these models can bring reputational damage and cause legal issues for hospitals. In spite of this, protecting healthcare models against leakage of potentially sensitive information is an understudied research area. In this study, two machine learning techniques that aim to predict a patient’s COVID-19 status are examined. Using adversarial training, robust deep learning architectures are explored with the aim to protect attributes related to demographic information about the patients. The two models examined in this work are intended to preserve sensitive information against adversarial attacks and information leakage. In a series of experiments using datasets from the Oxford University Hospitals (OUH), Bedfordshire Hospitals NHS Foundation Trust (BH), University Hospitals Birmingham NHS Foundation Trust (UHB), and Portsmouth Hospitals University NHS Trust (PUH), two neural networks are trained and evaluated. These networks predict PCR test results using information from basic laboratory blood tests, and vital signs collected from a patient upon arrival to the hospital. The level of privacy each one of the models can provide is assessed and the efficacy and robustness of the proposed architectures are compared with a relevant baseline. One of the main contributions in this work is the particular focus on the development of effective COVID19 detection models with built-in mechanisms in order to selectively protect sensitive attributes against adversarial attacks. The results on hold-out test set and external validation confirmed that there was no impact on the generalisibility of the model using adversarial learning. |
| spellingShingle | Rohanian, M Kouchaki, S Soltan, A Yang, J Rohanian, M Yang, Y Clifton, DA Privacy-aware early detection of COVID-19 through adversarial training |
| title | Privacy-aware early detection of COVID-19 through adversarial training |
| title_full | Privacy-aware early detection of COVID-19 through adversarial training |
| title_fullStr | Privacy-aware early detection of COVID-19 through adversarial training |
| title_full_unstemmed | Privacy-aware early detection of COVID-19 through adversarial training |
| title_short | Privacy-aware early detection of COVID-19 through adversarial training |
| title_sort | privacy aware early detection of covid 19 through adversarial training |
| work_keys_str_mv | AT rohanianm privacyawareearlydetectionofcovid19throughadversarialtraining AT kouchakis privacyawareearlydetectionofcovid19throughadversarialtraining AT soltana privacyawareearlydetectionofcovid19throughadversarialtraining AT yangj privacyawareearlydetectionofcovid19throughadversarialtraining AT rohanianm privacyawareearlydetectionofcovid19throughadversarialtraining AT yangy privacyawareearlydetectionofcovid19throughadversarialtraining AT cliftonda privacyawareearlydetectionofcovid19throughadversarialtraining |