Lightweight federated learning for STIs/HIV prediction
This paper presents a solution that prioritises high privacy protection and improves communication throughput for predicting the risk of sexually transmissible infections/human immunodeficiency virus (STIs/HIV). The approach utilised Federated Learning (FL) to construct a model from multiple clinics...
Saved in:
| Published in | Scientific reports Vol. 14; no. 1; pp. 6560 - 12 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Nature Publishing Group UK
19.03.2024
Nature Publishing Group Nature Portfolio |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | This paper presents a solution that prioritises high privacy protection and improves communication throughput for predicting the risk of sexually transmissible infections/human immunodeficiency virus (STIs/HIV). The approach utilised Federated Learning (FL) to construct a model from multiple clinics and key stakeholders. FL ensured that only models were shared between clinics, minimising the risk of personal information leakage. Additionally, an algorithm was explored on the FL manager side to construct a global model that aligns with the communication status of the system. Our proposed method introduced Random Forest Federated Learning for assessing the risk of STIs/HIV, incorporating a flexible aggregation process that can be adjusted to accommodate the capacious communication system. Experimental results demonstrated the significant potential of a solution for estimating STIs/HIV risk. In comparison with recent studies, our approach yielded superior results in terms of AUC (0.97) and accuracy (
93
%
). Despite these promising findings, a limitation of the study lies in the experiment for man’s data, due to the self-reported nature of the data and sensitive content. which may be subject to participant bias. Future research could check the performance of the proposed framework in partnership with high-risk populations (e.g., men who have sex with men) to provide a more comprehensive understanding of the proposed framework’s impact and ultimately aim to improve health outcomes/health service optimisation. |
|---|---|
| AbstractList | This paper presents a solution that prioritises high privacy protection and improves communication throughput for predicting the risk of sexually transmissible infections/human immunodeficiency virus (STIs/HIV). The approach utilised Federated Learning (FL) to construct a model from multiple clinics and key stakeholders. FL ensured that only models were shared between clinics, minimising the risk of personal information leakage. Additionally, an algorithm was explored on the FL manager side to construct a global model that aligns with the communication status of the system. Our proposed method introduced Random Forest Federated Learning for assessing the risk of STIs/HIV, incorporating a flexible aggregation process that can be adjusted to accommodate the capacious communication system. Experimental results demonstrated the significant potential of a solution for estimating STIs/HIV risk. In comparison with recent studies, our approach yielded superior results in terms of AUC (0.97) and accuracy (
93
%
). Despite these promising findings, a limitation of the study lies in the experiment for man’s data, due to the self-reported nature of the data and sensitive content. which may be subject to participant bias. Future research could check the performance of the proposed framework in partnership with high-risk populations (e.g., men who have sex with men) to provide a more comprehensive understanding of the proposed framework’s impact and ultimately aim to improve health outcomes/health service optimisation. This paper presents a solution that prioritises high privacy protection and improves communication throughput for predicting the risk of sexually transmissible infections/human immunodeficiency virus (STIs/HIV). The approach utilised Federated Learning (FL) to construct a model from multiple clinics and key stakeholders. FL ensured that only models were shared between clinics, minimising the risk of personal information leakage. Additionally, an algorithm was explored on the FL manager side to construct a global model that aligns with the communication status of the system. Our proposed method introduced Random Forest Federated Learning for assessing the risk of STIs/HIV, incorporating a flexible aggregation process that can be adjusted to accommodate the capacious communication system. Experimental results demonstrated the significant potential of a solution for estimating STIs/HIV risk. In comparison with recent studies, our approach yielded superior results in terms of AUC (0.97) and accuracy ( \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$93\%$$\end{document} 93 % ). Despite these promising findings, a limitation of the study lies in the experiment for man’s data, due to the self-reported nature of the data and sensitive content. which may be subject to participant bias. Future research could check the performance of the proposed framework in partnership with high-risk populations (e.g., men who have sex with men) to provide a more comprehensive understanding of the proposed framework’s impact and ultimately aim to improve health outcomes/health service optimisation. This paper presents a solution that prioritises high privacy protection and improves communication throughput for predicting the risk of sexually transmissible infections/human immunodeficiency virus (STIs/HIV). The approach utilised Federated Learning (FL) to construct a model from multiple clinics and key stakeholders. FL ensured that only models were shared between clinics, minimising the risk of personal information leakage. Additionally, an algorithm was explored on the FL manager side to construct a global model that aligns with the communication status of the system. Our proposed method introduced Random Forest Federated Learning for assessing the risk of STIs/HIV, incorporating a flexible aggregation process that can be adjusted to accommodate the capacious communication system. Experimental results demonstrated the significant potential of a solution for estimating STIs/HIV risk. In comparison with recent studies, our approach yielded superior results in terms of AUC (0.97) and accuracy ( ). Despite these promising findings, a limitation of the study lies in the experiment for man's data, due to the self-reported nature of the data and sensitive content. which may be subject to participant bias. Future research could check the performance of the proposed framework in partnership with high-risk populations (e.g., men who have sex with men) to provide a more comprehensive understanding of the proposed framework's impact and ultimately aim to improve health outcomes/health service optimisation. This paper presents a solution that prioritises high privacy protection and improves communication throughput for predicting the risk of sexually transmissible infections/human immunodeficiency virus (STIs/HIV). The approach utilised Federated Learning (FL) to construct a model from multiple clinics and key stakeholders. FL ensured that only models were shared between clinics, minimising the risk of personal information leakage. Additionally, an algorithm was explored on the FL manager side to construct a global model that aligns with the communication status of the system. Our proposed method introduced Random Forest Federated Learning for assessing the risk of STIs/HIV, incorporating a flexible aggregation process that can be adjusted to accommodate the capacious communication system. Experimental results demonstrated the significant potential of a solution for estimating STIs/HIV risk. In comparison with recent studies, our approach yielded superior results in terms of AUC (0.97) and accuracy (93%). Despite these promising findings, a limitation of the study lies in the experiment for man’s data, due to the self-reported nature of the data and sensitive content. which may be subject to participant bias. Future research could check the performance of the proposed framework in partnership with high-risk populations (e.g., men who have sex with men) to provide a more comprehensive understanding of the proposed framework’s impact and ultimately aim to improve health outcomes/health service optimisation. Abstract This paper presents a solution that prioritises high privacy protection and improves communication throughput for predicting the risk of sexually transmissible infections/human immunodeficiency virus (STIs/HIV). The approach utilised Federated Learning (FL) to construct a model from multiple clinics and key stakeholders. FL ensured that only models were shared between clinics, minimising the risk of personal information leakage. Additionally, an algorithm was explored on the FL manager side to construct a global model that aligns with the communication status of the system. Our proposed method introduced Random Forest Federated Learning for assessing the risk of STIs/HIV, incorporating a flexible aggregation process that can be adjusted to accommodate the capacious communication system. Experimental results demonstrated the significant potential of a solution for estimating STIs/HIV risk. In comparison with recent studies, our approach yielded superior results in terms of AUC (0.97) and accuracy ( $$93\%$$ 93 % ). Despite these promising findings, a limitation of the study lies in the experiment for man’s data, due to the self-reported nature of the data and sensitive content. which may be subject to participant bias. Future research could check the performance of the proposed framework in partnership with high-risk populations (e.g., men who have sex with men) to provide a more comprehensive understanding of the proposed framework’s impact and ultimately aim to improve health outcomes/health service optimisation. This paper presents a solution that prioritises high privacy protection and improves communication throughput for predicting the risk of sexually transmissible infections/human immunodeficiency virus (STIs/HIV). The approach utilised Federated Learning (FL) to construct a model from multiple clinics and key stakeholders. FL ensured that only models were shared between clinics, minimising the risk of personal information leakage. Additionally, an algorithm was explored on the FL manager side to construct a global model that aligns with the communication status of the system. Our proposed method introduced Random Forest Federated Learning for assessing the risk of STIs/HIV, incorporating a flexible aggregation process that can be adjusted to accommodate the capacious communication system. Experimental results demonstrated the significant potential of a solution for estimating STIs/HIV risk. In comparison with recent studies, our approach yielded superior results in terms of AUC (0.97) and accuracy ( $$93\%$$ 93 % ). Despite these promising findings, a limitation of the study lies in the experiment for man’s data, due to the self-reported nature of the data and sensitive content. which may be subject to participant bias. Future research could check the performance of the proposed framework in partnership with high-risk populations (e.g., men who have sex with men) to provide a more comprehensive understanding of the proposed framework’s impact and ultimately aim to improve health outcomes/health service optimisation. This paper presents a solution that prioritises high privacy protection and improves communication throughput for predicting the risk of sexually transmissible infections/human immunodeficiency virus (STIs/HIV). The approach utilised Federated Learning (FL) to construct a model from multiple clinics and key stakeholders. FL ensured that only models were shared between clinics, minimising the risk of personal information leakage. Additionally, an algorithm was explored on the FL manager side to construct a global model that aligns with the communication status of the system. Our proposed method introduced Random Forest Federated Learning for assessing the risk of STIs/HIV, incorporating a flexible aggregation process that can be adjusted to accommodate the capacious communication system. Experimental results demonstrated the significant potential of a solution for estimating STIs/HIV risk. In comparison with recent studies, our approach yielded superior results in terms of AUC (0.97) and accuracy ( 93 % ). Despite these promising findings, a limitation of the study lies in the experiment for man's data, due to the self-reported nature of the data and sensitive content. which may be subject to participant bias. Future research could check the performance of the proposed framework in partnership with high-risk populations (e.g., men who have sex with men) to provide a more comprehensive understanding of the proposed framework's impact and ultimately aim to improve health outcomes/health service optimisation.This paper presents a solution that prioritises high privacy protection and improves communication throughput for predicting the risk of sexually transmissible infections/human immunodeficiency virus (STIs/HIV). The approach utilised Federated Learning (FL) to construct a model from multiple clinics and key stakeholders. FL ensured that only models were shared between clinics, minimising the risk of personal information leakage. Additionally, an algorithm was explored on the FL manager side to construct a global model that aligns with the communication status of the system. Our proposed method introduced Random Forest Federated Learning for assessing the risk of STIs/HIV, incorporating a flexible aggregation process that can be adjusted to accommodate the capacious communication system. Experimental results demonstrated the significant potential of a solution for estimating STIs/HIV risk. In comparison with recent studies, our approach yielded superior results in terms of AUC (0.97) and accuracy ( 93 % ). Despite these promising findings, a limitation of the study lies in the experiment for man's data, due to the self-reported nature of the data and sensitive content. which may be subject to participant bias. Future research could check the performance of the proposed framework in partnership with high-risk populations (e.g., men who have sex with men) to provide a more comprehensive understanding of the proposed framework's impact and ultimately aim to improve health outcomes/health service optimisation. |
| ArticleNumber | 6560 |
| Author | Yang, Wencheng Dean, Judith A. Li, Yan Xia, Xiaoyu Nguyen, Thi Phuoc Van Mullens, Amy B. Tang, Zhaohui |
| Author_xml | – sequence: 1 givenname: Thi Phuoc Van surname: Nguyen fullname: Nguyen, Thi Phuoc Van email: phuocvan.haui@gmail.com organization: School of Mathematics, Physics and Computing, Centre for Health Research, University of Southern Queensland – sequence: 2 givenname: Wencheng surname: Yang fullname: Yang, Wencheng organization: School of Mathematics, Physics and Computing, Centre for Health Research, University of Southern Queensland – sequence: 3 givenname: Zhaohui surname: Tang fullname: Tang, Zhaohui organization: School of Mathematics, Physics and Computing, Centre for Health Research, University of Southern Queensland – sequence: 4 givenname: Xiaoyu surname: Xia fullname: Xia, Xiaoyu organization: School of Computing Technologies, RMIT University – sequence: 5 givenname: Amy B. surname: Mullens fullname: Mullens, Amy B. organization: School of Psychology and Wellbeing, Institute for Resilient Regions, Centre for Health Research, University of Southern Queensland – sequence: 6 givenname: Judith A. surname: Dean fullname: Dean, Judith A. organization: School of Public Health, Faculty of Medicine, The University of Queensland – sequence: 7 givenname: Yan surname: Li fullname: Li, Yan organization: School of Mathematics, Physics and Computing, Centre for Health Research, University of Southern Queensland |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/38503789$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kk9vFCEYxompsbX2C3gwk3jxMpa_M3AyplG7ySYerF4JAy9TNrOwwqzGby_babXtoRyAwPP88rzwvkRHMUVA6DXB7wlm8rxwIpRsMeWt6AgRLX6GTijmoqWM0qN7-2N0VsoG1yGo4kS9QMdMCsx6qU5Qtw7j9fwbDnPjwUE2M7hmApNjiGPjU26-Xa3K-eXqR7PL4IKdQ4qv0HNvpgJnt-sp-v7509XFZbv--mV18XHdWsHJ3PYM904M2BDluBA1JjPUe4WZ4lJxpZQzShHWyR5zYGDIwDG3rqcGnHUDO0WrheuS2ehdDluT_-hkgr45SHnUJs_BTqCJG0AqaoljwDuPlefGW0aF89Y6ySvrw8La7YdtxUOcs5keQB_exHCtx_RLE6wEll1XCe9uCTn93EOZ9TYUC9NkIqR90VT1tBbSU1Wlbx9JN2mfY32rqupkV6snsqre3I_0L8vd_1SBXAQ2p1IyeG3DbA4_UBOGqUbTh27QSzfo2g36phs0rlb6yHpHf9LEFlOp4jhC_h_7CddfxHjFHA |
| CitedBy_id | crossref_primary_10_3390_a17080362 crossref_primary_10_3390_ai5020031 crossref_primary_10_1016_j_future_2024_107484 crossref_primary_10_3389_fpubh_2024_1511689 |
| Cites_doi | 10.1093/cid/ciz1096 10.3390/atmos12020238 10.1071/SH21044 10.3390/s20092533 10.1080/14639220903470205 10.1109/TNSE.2021.3053588 10.1007/s10461-022-03672-y 10.3390/jcm11071818 10.1213/ANE.0000000000002864 10.2196/37850 10.1186/s12889-023-15418-z 10.1016/j.jinf.2020.11.007 10.52339/tjet.v41i3.845 10.1109/COMST.2021.3075439 10.1109/JIOT.2020.3030072 10.3390/ijerph191912213 10.1016/j.ress.2023.109130 10.1177/2055102915581562 10.1080/09540121.2020.1822502 10.1038/s41746-021-00489-2 10.1016/j.eswa.2017.07.026 10.1038/s41598-020-69250-1 10.1177/14759217211036880 10.1186/s12939-018-0772-6 10.1200/CCI.19.00047 10.1186/s12874-022-01625-6 10.4097/kja.21209 10.1111/rssc.12600 10.1007/s41666-020-00082-4 10.1038/s41437-020-0303-2 10.1016/j.eclinm.2019.10.016 10.1109/COMST.2021.3058573 10.1145/3501813 10.1038/s41746-020-00323-1 10.1080/13691058.2016.1184316 10.1371/journal.pone.0230706 10.1109/MC.2022.3142151 10.1016/B978-0-12-800284-1.00003-5 10.1109/ICC.2019.8761381 10.18653/v1/W19-5030 10.1145/3286490.3286559 10.1007/978-3-030-32692-0_16 10.1109/CVPR.2019.00220 10.1109/HPCA47549.2020.00049 10.1142/9789812836939_0049 10.1109/BIBE.2019.00169 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2024 2024. The Author(s). The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: The Author(s) 2024 – notice: 2024. The Author(s). – notice: The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM 3V. 7X7 7XB 88A 88E 88I 8FE 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA AZQEC BBNVY BENPR BHPHI CCPQU DWQXO FYUFA GHDGH GNUQQ HCIFZ K9. LK8 M0S M1P M2P M7P PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI Q9U 7X8 5PM DOA |
| DOI | 10.1038/s41598-024-56115-0 |
| DatabaseName | SpringerOpen Free (Free internet resource, activated by CARLI) CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed ProQuest Central (Corporate) Health & Medical Collection ProQuest Central (purchase pre-March 2016) Biology Database (Alumni Edition) Medical Database (Alumni Edition) Science Database (Alumni Edition) ProQuest SciTech Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection ProQuest Central Natural Science Collection ProQuest One ProQuest Central Korea Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) Biological Sciences Health & Medical Collection (Alumni) Medical Database Science Database Biological Science Database ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Biology Journals (Alumni Edition) ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Health & Medical Research Collection Health Research Premium Collection Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) ProQuest Science Journals (Alumni Edition) ProQuest Biological Science Collection ProQuest Central Basic ProQuest Science Journals ProQuest One Academic Eastern Edition ProQuest Hospital Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE Publicly Available Content Database CrossRef MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: C6C name: SpringerOpen Free (Free internet resource, activated by CARLI) url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 5 dbid: BENPR name: ProQuest Central url: https://www.proquest.com/central sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 2045-2322 |
| EndPage | 12 |
| ExternalDocumentID | oai_doaj_org_article_1dbe892c1d3e46f09f4afc325dfccd84 PMC10950866 38503789 10_1038_s41598_024_56115_0 |
| Genre | Journal Article |
| GroupedDBID | 0R~ 3V. 4.4 53G 5VS 7X7 88A 88E 88I 8FE 8FH 8FI 8FJ AAFWJ AAJSJ AAKDD ABDBF ABUWG ACGFS ACSMW ACUHS ADBBV ADRAZ AENEX AEUYN AFKRA AJTQC ALIPV ALMA_UNASSIGNED_HOLDINGS AOIJS AZQEC BAWUL BBNVY BCNDV BENPR BHPHI BPHCQ BVXVI C6C CCPQU DIK DWQXO EBD EBLON EBS ESX FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HH5 HMCUK HYE KQ8 LK8 M0L M1P M2P M48 M7P M~E NAO OK1 PIMPY PQQKQ PROAC PSQYO RNT RNTTT RPM SNYQT UKHRP AASML AAYXX AFPKN CITATION PHGZM PHGZT CGR CUY CVF ECM EIF NPM PJZUB PPXIY PQGLB 7XB 8FK AARCD K9. PKEHL PQEST PQUKI Q9U 7X8 5PM PUEGO |
| ID | FETCH-LOGICAL-c541t-7307d5b0a19d4551153a2ff90394894999da991368704e3ea1b404cd72aedcdb3 |
| IEDL.DBID | M48 |
| ISSN | 2045-2322 |
| IngestDate | Wed Aug 27 01:27:57 EDT 2025 Thu Aug 21 18:34:45 EDT 2025 Fri Jul 11 02:20:27 EDT 2025 Wed Aug 13 05:30:23 EDT 2025 Mon Jul 21 05:55:03 EDT 2025 Thu Apr 24 23:08:33 EDT 2025 Tue Jul 01 00:51:37 EDT 2025 Fri Feb 21 02:40:00 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | 2024. The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c541t-7307d5b0a19d4551153a2ff90394894999da991368704e3ea1b404cd72aedcdb3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.1038/s41598-024-56115-0 |
| PMID | 38503789 |
| PQID | 2968689418 |
| PQPubID | 2041939 |
| PageCount | 12 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_1dbe892c1d3e46f09f4afc325dfccd84 pubmedcentral_primary_oai_pubmedcentral_nih_gov_10950866 proquest_miscellaneous_2972704729 proquest_journals_2968689418 pubmed_primary_38503789 crossref_citationtrail_10_1038_s41598_024_56115_0 crossref_primary_10_1038_s41598_024_56115_0 springer_journals_10_1038_s41598_024_56115_0 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2024-03-19 |
| PublicationDateYYYYMMDD | 2024-03-19 |
| PublicationDate_xml | – month: 03 year: 2024 text: 2024-03-19 day: 19 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Scientific reports |
| PublicationTitleAbbrev | Sci Rep |
| PublicationTitleAlternate | Sci Rep |
| PublicationYear | 2024 |
| Publisher | Nature Publishing Group UK Nature Publishing Group Nature Portfolio |
| Publisher_xml | – name: Nature Publishing Group UK – name: Nature Publishing Group – name: Nature Portfolio |
| References | Xu (CR7) 2022; 11 Probst, Wright, Boulesteix (CR49) 2019; 9 AbdulRahman (CR35) 2020; 8 Kimovski, Ristov, Prodan (CR12) 2022; 55 CR39 Zerka (CR30) 2020; 4 Kamei, Taghipour (CR38) 2023; 233 CR34 Antunes, André da Costa, Küderle, Yari, Eskofier (CR14) 2022; 13 CR33 CR31 Nahm (CR51) 2022; 75 Bao (CR8) 2021; 82 Nguyen (CR40) 2021; 23 Merenda, Porcaro, Iero (CR28) 2020; 20 CR2 Agrawal, Prabakaran (CR36) 2020; 124 Wang (CR43) 2022; 2022 Vujcich (CR22) 2022; 19 Soogun, Kharsany, Zewotir, North, Ogunsakin (CR4) 2022; 22 Xu (CR9) 2022; 24 Krennmair, Schmid (CR5) 2022; 71 Contreras, Orellana-Alvear, Muñoz, Bendix, Célleri (CR50) 2021; 12 Dean, Mitchell, Stewart, Debattista (CR16) 2017; 19 Schober, Boer, Schwarte (CR45) 2018; 126 Mhaisen, Abdellatif, Mohamed, Erbad, Guizani (CR41) 2021; 9 Ridgway (CR3) 2022; 26 Young, Weckman, Holland (CR47) 2011; 12 Engstrom (CR20) 2023; 23 Rieke (CR15) 2020; 3 Mullens (CR18) 2018; 17 Fisher, Fisher, Shuper (CR23) 2014; 50 Malekloo, Ozer, AlHamaydeh, Girolami (CR6) 2022; 21 Sadilek (CR13) 2021; 4 CR10 Sheller (CR25) 2020; 10 Ahlström, Ronit, Omland, Vedel, Obel (CR52) 2019; 17 Mundie, Lazarou, Mullens, Gu, Dean (CR17) 2021; 18 Garciarena, Santana (CR48) 2017; 89 Xu, Huang, Xu, Zhang (CR1) 2007; 45 Kaladharan, Daken, Mullens, Durham (CR19) 2021; 33 Wahab, Mourad, Otrok, Taleb (CR37) 2021; 23 CR29 CR27 Huang (CR32) 2020; 15 Nisa, Mahmood, Ujager, Malik (CR44) 2023; 24 CR24 Dankar, El Emam (CR11) 2013; 6 Strodl, Stewart, Mullens, Deb (CR21) 2015; 2 Xu (CR26) 2021; 5 Chaula, Justo (CR42) 2022; 41 Balzer (CR46) 2020; 71 56115_CR33 S Kamei (56115_CR38) 2023; 233 56115_CR34 56115_CR31 B Wang (56115_CR43) 2022; 2022 U Garciarena (56115_CR48) 2017; 89 D Vujcich (56115_CR22) 2022; 19 AB Mullens (56115_CR18) 2018; 17 SU Nisa (56115_CR44) 2023; 24 N Mhaisen (56115_CR41) 2021; 9 M Merenda (56115_CR28) 2020; 20 FK Dankar (56115_CR11) 2013; 6 A Sadilek (56115_CR13) 2021; 4 S Xu (56115_CR1) 2007; 45 56115_CR29 MJ Sheller (56115_CR25) 2020; 10 RS Antunes (56115_CR14) 2022; 13 P Contreras (56115_CR50) 2021; 12 J Dean (56115_CR16) 2017; 19 LB Balzer (56115_CR46) 2020; 71 S AbdulRahman (56115_CR35) 2020; 8 DC Nguyen (56115_CR40) 2021; 23 S Kaladharan (56115_CR19) 2021; 33 F Zerka (56115_CR30) 2020; 4 P Schober (56115_CR45) 2018; 126 L Huang (56115_CR32) 2020; 15 T Engstrom (56115_CR20) 2023; 23 56115_CR39 P Krennmair (56115_CR5) 2022; 71 56115_CR10 R Chaula (56115_CR42) 2022; 41 E Strodl (56115_CR21) 2015; 2 56115_CR2 D Kimovski (56115_CR12) 2022; 55 N Rieke (56115_CR15) 2020; 3 A Mundie (56115_CR17) 2021; 18 MG Ahlström (56115_CR52) 2019; 17 X Xu (56115_CR9) 2022; 24 OA Wahab (56115_CR37) 2021; 23 R Agrawal (56115_CR36) 2020; 124 A Malekloo (56115_CR6) 2022; 21 X Xu (56115_CR7) 2022; 11 56115_CR27 56115_CR24 WA Fisher (56115_CR23) 2014; 50 P Probst (56115_CR49) 2019; 9 W Young (56115_CR47) 2011; 12 AO Soogun (56115_CR4) 2022; 22 Y Bao (56115_CR8) 2021; 82 JP Ridgway (56115_CR3) 2022; 26 FS Nahm (56115_CR51) 2022; 75 J Xu (56115_CR26) 2021; 5 |
| References_xml | – volume: 71 start-page: 2326 year: 2020 end-page: 2333 ident: CR46 article-title: Machine learning to identify persons at high-risk of human immunodeficiency virus acquisition in rural kenya and uganda publication-title: Clin. Infect. Dis. doi: 10.1093/cid/ciz1096 – volume: 12 start-page: 238 year: 2021 ident: CR50 article-title: Influence of random forest hyperparameterization on short-term runoff forecasting in an andean mountain catchment publication-title: Atmosphere doi: 10.3390/atmos12020238 – ident: CR39 – volume: 18 start-page: 294 year: 2021 end-page: 302 ident: CR17 article-title: Sexual and reproductive health knowledge, attitudes and behaviours of Chinese international students studying abroad (in Australia, the UK and the US): A scoping review publication-title: Sexual Health doi: 10.1071/SH21044 – volume: 20 start-page: 2533 year: 2020 ident: CR28 article-title: Edge machine learning for ai-enabled iot devices: A review publication-title: Sensors doi: 10.3390/s20092533 – ident: CR29 – volume: 9 start-page: e1301 year: 2019 ident: CR49 article-title: Hyperparameters and tuning strategies for random forest publication-title: Wiley Interdiscipl. Rev.: Data Mining Knowl. Discov. – volume: 12 start-page: 15 year: 2011 end-page: 43 ident: CR47 article-title: A survey of methodologies for the treatment of missing values within datasets: Limitations and benefits publication-title: Theor. Issues Ergon. Sci. doi: 10.1080/14639220903470205 – volume: 9 start-page: 55 year: 2021 end-page: 66 ident: CR41 article-title: Optimal user-edge assignment in hierarchical federated learning based on statistical properties and network topology constraints publication-title: IEEE Trans. Netw. Sci. Eng. doi: 10.1109/TNSE.2021.3053588 – volume: 24 start-page: 107 year: 2023 end-page: 115 ident: CR44 article-title: Hiv/aids predictive model using random forest based on socio-demographical, biological and behavioral data publication-title: Egypt. Inf. J. – volume: 26 start-page: 3279 year: 2022 end-page: 3288 ident: CR3 article-title: Multicenter development and validation of a model for predicting retention in care among people with hiv publication-title: AIDS Behav. doi: 10.1007/s10461-022-03672-y – volume: 11 start-page: 1818 year: 2022 ident: CR7 article-title: A machine-learning-based risk-prediction tool for hiv and sexually transmitted infections acquisition over the next 12 months publication-title: J. Clin. Med. doi: 10.3390/jcm11071818 – volume: 126 start-page: 1763 year: 2018 end-page: 1768 ident: CR45 article-title: Correlation coefficients: Appropriate use and interpretation publication-title: Anesthes. Analges. doi: 10.1213/ANE.0000000000002864 – volume: 24 start-page: e37850 year: 2022 ident: CR9 article-title: Web-based risk prediction tool for an individual’s risk of hiv and sexually transmitted infections using machine learning algorithms: Development and external validation study publication-title: J. Med. Internet Res. doi: 10.2196/37850 – volume: 23 start-page: 1 year: 2023 end-page: 11 ident: CR20 article-title: Sti and hiv testing: Examining factors that influence uptake among domestic Australian-born, domestic overseas-born and international tertiary students studying in Australia publication-title: BMC Public Health doi: 10.1186/s12889-023-15418-z – volume: 82 start-page: 48 year: 2021 end-page: 59 ident: CR8 article-title: Predicting the diagnosis of hiv and sexually transmitted infections among men who have sex with men using machine learning approaches publication-title: J. Infect. doi: 10.1016/j.jinf.2020.11.007 – volume: 41 start-page: 64 year: 2022 end-page: 70 ident: CR42 article-title: A robust random forest prediction model for mother-to-child hiv transmission based on individual medical history publication-title: Tanzania J. Eng. Technol. doi: 10.52339/tjet.v41i3.845 – volume: 23 start-page: 1622 year: 2021 end-page: 1658 ident: CR40 article-title: Federated learning for internet of things: A comprehensive survey publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2021.3075439 – volume: 8 start-page: 5476 year: 2020 end-page: 5497 ident: CR35 article-title: A survey on federated learning: The journey from centralized to distributed on-site learning and beyond publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2020.3030072 – volume: 19 start-page: 12213 year: 2022 ident: CR22 article-title: Strategies for recruiting migrants to participate in a sexual health survey: Methods, results, and lessons publication-title: Int. J. Environ. Res. Public Health doi: 10.3390/ijerph191912213 – volume: 233 start-page: 109130 year: 2023 ident: CR38 article-title: A comparison study of centralized and decentralized federated learning approaches utilizing the transformer architecture for estimating remaining useful life publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2023.109130 – volume: 2 start-page: 2055102915581562 year: 2015 ident: CR21 article-title: Metacognitions mediate hiv stigma and depression/anxiety in men who have sex with men living with hiv publication-title: Health Psychol. Open doi: 10.1177/2055102915581562 – volume: 33 start-page: 1500 year: 2021 end-page: 1506 ident: CR19 article-title: Tools to measure hiv knowledge, attitudes & practices (kaps) in healthcare providers: A systematic review publication-title: AIDS Care doi: 10.1080/09540121.2020.1822502 – ident: CR2 – ident: CR10 – volume: 4 start-page: 132 year: 2021 ident: CR13 article-title: Privacy-first health research with federated learning publication-title: NPJ Digital Med. doi: 10.1038/s41746-021-00489-2 – ident: CR33 – volume: 89 start-page: 52 year: 2017 end-page: 65 ident: CR48 article-title: An extensive analysis of the interaction between missing data types, imputation methods, and supervised classifiers publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2017.07.026 – volume: 10 start-page: 1 year: 2020 end-page: 12 ident: CR25 article-title: Federated learning in medicine: Facilitating multi-institutional collaborations without sharing patient data publication-title: Sci. Rep. doi: 10.1038/s41598-020-69250-1 – volume: 21 start-page: 1906 year: 2022 end-page: 1955 ident: CR6 article-title: Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights publication-title: Struct. Health Monit. doi: 10.1177/14759217211036880 – volume: 17 start-page: 1 year: 2018 end-page: 12 ident: CR18 article-title: Exploring hiv risks, testing and prevention among sub-Saharan African community members in Australia publication-title: Int. J. Equity Health doi: 10.1186/s12939-018-0772-6 – volume: 4 start-page: 184 year: 2020 end-page: 200 ident: CR30 article-title: Systematic review of privacy-preserving distributed machine learning from federated databases in health care publication-title: JCO Clin. Cancer Inf. doi: 10.1200/CCI.19.00047 – volume: 22 start-page: 174 year: 2022 ident: CR4 article-title: Identifying potential factors associated with high hiv viral load in kwazulu-natal, south africa using multiple correspondence analysis and random forest analysis publication-title: BMC Med. Res. Methodol. doi: 10.1186/s12874-022-01625-6 – ident: CR27 – volume: 45 start-page: 441 year: 2007 end-page: 446 ident: CR1 article-title: Improved prediction of coreceptor usage and phenotype of hiv-1 based on combined features of v3 loop sequence using random forest publication-title: J. Microbiol. – volume: 75 start-page: 25 year: 2022 end-page: 36 ident: CR51 article-title: Receiver operating characteristic curve: Overview and practical use for clinicians publication-title: Korean J. Anesthesiol. doi: 10.4097/kja.21209 – volume: 71 start-page: 1865 year: 2022 end-page: 1894 ident: CR5 article-title: Flexible domain prediction using mixed effects random forests publication-title: J. R. Stat. Soc.: Ser. C: Appl. Stat. doi: 10.1111/rssc.12600 – volume: 6 start-page: 35 year: 2013 end-page: 67 ident: CR11 article-title: Practicing differential privacy in health care: A review publication-title: Trans. Data Priv. – volume: 5 start-page: 1 year: 2021 end-page: 19 ident: CR26 article-title: Federated learning for healthcare informatics publication-title: J. Healthcare Inf. Res. doi: 10.1007/s41666-020-00082-4 – volume: 124 start-page: 525 year: 2020 end-page: 534 ident: CR36 article-title: Big data in digital healthcare: Lessons learnt and recommendations for general practice publication-title: Heredity doi: 10.1038/s41437-020-0303-2 – volume: 17 start-page: 100203 year: 2019 ident: CR52 article-title: Algorithmic prediction of hiv status using nation-wide electronic registry data publication-title: EClinicalMedicine doi: 10.1016/j.eclinm.2019.10.016 – volume: 23 start-page: 1342 year: 2021 end-page: 1397 ident: CR37 article-title: Federated machine learning: Survey, multi-level classification, desirable criteria and future directions in communication and networking systems publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2021.3058573 – volume: 13 start-page: 1 year: 2022 end-page: 23 ident: CR14 article-title: Federated learning for healthcare, systematic review and architecture proposal publication-title: ACM Trans. Intell. Syst. Technol. (TIST) doi: 10.1145/3501813 – ident: CR31 – volume: 3 start-page: 119 year: 2020 ident: CR15 article-title: The future of digital health with federated learning publication-title: NPJ Digital Med. doi: 10.1038/s41746-020-00323-1 – volume: 19 start-page: 17 year: 2017 end-page: 31 ident: CR16 article-title: Intergenerational variation in sexual health attitudes and beliefs among sudanese refugee communities in Australia publication-title: Cult. Health Sexuality doi: 10.1080/13691058.2016.1184316 – volume: 15 start-page: e0230706 year: 2020 ident: CR32 article-title: Loadaboost: Loss-based adaboost federated machine learning with reduced computational complexity on iid and non-iid intensive care data publication-title: PLoS ONE doi: 10.1371/journal.pone.0230706 – ident: CR34 – volume: 55 start-page: 55 year: 2022 end-page: 65 ident: CR12 article-title: Decentralized machine learning for intelligent health care systems on the computing continuum publication-title: Computer doi: 10.1109/MC.2022.3142151 – volume: 50 start-page: 105 year: 2014 end-page: 193 ident: CR23 article-title: Social psychology and the fight against aids: An information-motivation-behavioral skills model for the prediction and promotion of health behavior change publication-title: Adv. Exp. Soc. Psychol. doi: 10.1016/B978-0-12-800284-1.00003-5 – ident: CR24 – volume: 2022 start-page: 1 year: 2022 end-page: 11 ident: CR43 article-title: Predicting adolescent intervention non-responsiveness for precision hiv prevention using machine learning publication-title: AIDS Behav. – volume: 26 start-page: 3279 year: 2022 ident: 56115_CR3 publication-title: AIDS Behav. doi: 10.1007/s10461-022-03672-y – volume: 17 start-page: 100203 year: 2019 ident: 56115_CR52 publication-title: EClinicalMedicine doi: 10.1016/j.eclinm.2019.10.016 – volume: 2022 start-page: 1 year: 2022 ident: 56115_CR43 publication-title: AIDS Behav. – volume: 75 start-page: 25 year: 2022 ident: 56115_CR51 publication-title: Korean J. Anesthesiol. doi: 10.4097/kja.21209 – volume: 11 start-page: 1818 year: 2022 ident: 56115_CR7 publication-title: J. Clin. Med. doi: 10.3390/jcm11071818 – volume: 124 start-page: 525 year: 2020 ident: 56115_CR36 publication-title: Heredity doi: 10.1038/s41437-020-0303-2 – volume: 23 start-page: 1 year: 2023 ident: 56115_CR20 publication-title: BMC Public Health doi: 10.1186/s12889-023-15418-z – volume: 71 start-page: 2326 year: 2020 ident: 56115_CR46 publication-title: Clin. Infect. Dis. doi: 10.1093/cid/ciz1096 – volume: 19 start-page: 12213 year: 2022 ident: 56115_CR22 publication-title: Int. J. Environ. Res. Public Health doi: 10.3390/ijerph191912213 – ident: 56115_CR24 doi: 10.1109/ICC.2019.8761381 – ident: 56115_CR29 doi: 10.18653/v1/W19-5030 – volume: 89 start-page: 52 year: 2017 ident: 56115_CR48 publication-title: Expert Syst. Appl. doi: 10.1016/j.eswa.2017.07.026 – ident: 56115_CR39 doi: 10.1145/3286490.3286559 – volume: 13 start-page: 1 year: 2022 ident: 56115_CR14 publication-title: ACM Trans. Intell. Syst. Technol. (TIST) doi: 10.1145/3501813 – ident: 56115_CR33 doi: 10.1007/978-3-030-32692-0_16 – ident: 56115_CR34 doi: 10.1109/CVPR.2019.00220 – ident: 56115_CR27 doi: 10.1109/HPCA47549.2020.00049 – volume: 3 start-page: 119 year: 2020 ident: 56115_CR15 publication-title: NPJ Digital Med. doi: 10.1038/s41746-020-00323-1 – volume: 22 start-page: 174 year: 2022 ident: 56115_CR4 publication-title: BMC Med. Res. Methodol. doi: 10.1186/s12874-022-01625-6 – volume: 10 start-page: 1 year: 2020 ident: 56115_CR25 publication-title: Sci. Rep. doi: 10.1038/s41598-020-69250-1 – volume: 5 start-page: 1 year: 2021 ident: 56115_CR26 publication-title: J. Healthcare Inf. Res. doi: 10.1007/s41666-020-00082-4 – volume: 20 start-page: 2533 year: 2020 ident: 56115_CR28 publication-title: Sensors doi: 10.3390/s20092533 – volume: 71 start-page: 1865 year: 2022 ident: 56115_CR5 publication-title: J. R. Stat. Soc.: Ser. C: Appl. Stat. doi: 10.1111/rssc.12600 – volume: 12 start-page: 238 year: 2021 ident: 56115_CR50 publication-title: Atmosphere doi: 10.3390/atmos12020238 – volume: 233 start-page: 109130 year: 2023 ident: 56115_CR38 publication-title: Reliab. Eng. Syst. Saf. doi: 10.1016/j.ress.2023.109130 – volume: 18 start-page: 294 year: 2021 ident: 56115_CR17 publication-title: Sexual Health doi: 10.1071/SH21044 – volume: 33 start-page: 1500 year: 2021 ident: 56115_CR19 publication-title: AIDS Care doi: 10.1080/09540121.2020.1822502 – volume: 41 start-page: 64 year: 2022 ident: 56115_CR42 publication-title: Tanzania J. Eng. Technol. doi: 10.52339/tjet.v41i3.845 – ident: 56115_CR2 doi: 10.1142/9789812836939_0049 – volume: 55 start-page: 55 year: 2022 ident: 56115_CR12 publication-title: Computer doi: 10.1109/MC.2022.3142151 – volume: 23 start-page: 1342 year: 2021 ident: 56115_CR37 publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2021.3058573 – volume: 24 start-page: 107 year: 2023 ident: 56115_CR44 publication-title: Egypt. Inf. J. – volume: 45 start-page: 441 year: 2007 ident: 56115_CR1 publication-title: J. Microbiol. – ident: 56115_CR31 – volume: 21 start-page: 1906 year: 2022 ident: 56115_CR6 publication-title: Struct. Health Monit. doi: 10.1177/14759217211036880 – volume: 50 start-page: 105 year: 2014 ident: 56115_CR23 publication-title: Adv. Exp. Soc. Psychol. doi: 10.1016/B978-0-12-800284-1.00003-5 – volume: 23 start-page: 1622 year: 2021 ident: 56115_CR40 publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2021.3075439 – volume: 15 start-page: e0230706 year: 2020 ident: 56115_CR32 publication-title: PLoS ONE doi: 10.1371/journal.pone.0230706 – volume: 2 start-page: 205510291558156 year: 2015 ident: 56115_CR21 publication-title: Health Psychol. Open doi: 10.1177/2055102915581562 – volume: 12 start-page: 15 year: 2011 ident: 56115_CR47 publication-title: Theor. Issues Ergon. Sci. doi: 10.1080/14639220903470205 – volume: 24 start-page: e37850 year: 2022 ident: 56115_CR9 publication-title: J. Med. Internet Res. doi: 10.2196/37850 – volume: 9 start-page: e1301 year: 2019 ident: 56115_CR49 publication-title: Wiley Interdiscipl. Rev.: Data Mining Knowl. Discov. – volume: 4 start-page: 184 year: 2020 ident: 56115_CR30 publication-title: JCO Clin. Cancer Inf. doi: 10.1200/CCI.19.00047 – volume: 82 start-page: 48 year: 2021 ident: 56115_CR8 publication-title: J. Infect. doi: 10.1016/j.jinf.2020.11.007 – volume: 4 start-page: 132 year: 2021 ident: 56115_CR13 publication-title: NPJ Digital Med. doi: 10.1038/s41746-021-00489-2 – volume: 6 start-page: 35 year: 2013 ident: 56115_CR11 publication-title: Trans. Data Priv. – volume: 17 start-page: 1 year: 2018 ident: 56115_CR18 publication-title: Int. J. Equity Health doi: 10.1186/s12939-018-0772-6 – volume: 9 start-page: 55 year: 2021 ident: 56115_CR41 publication-title: IEEE Trans. Netw. Sci. Eng. doi: 10.1109/TNSE.2021.3053588 – volume: 8 start-page: 5476 year: 2020 ident: 56115_CR35 publication-title: IEEE Internet Things J. doi: 10.1109/JIOT.2020.3030072 – volume: 126 start-page: 1763 year: 2018 ident: 56115_CR45 publication-title: Anesthes. Analges. doi: 10.1213/ANE.0000000000002864 – volume: 19 start-page: 17 year: 2017 ident: 56115_CR16 publication-title: Cult. Health Sexuality doi: 10.1080/13691058.2016.1184316 – ident: 56115_CR10 doi: 10.1109/BIBE.2019.00169 |
| SSID | ssj0000529419 |
| Score | 2.4515772 |
| Snippet | This paper presents a solution that prioritises high privacy protection and improves communication throughput for predicting the risk of sexually transmissible... Abstract This paper presents a solution that prioritises high privacy protection and improves communication throughput for predicting the risk of sexually... |
| SourceID | doaj pubmedcentral proquest pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 6560 |
| SubjectTerms | 631/114 631/61 692/700 Communication Communications systems Health risks HIV HIV Infections - epidemiology Homosexuality, Male Human immunodeficiency virus Humanities and Social Sciences Humans Learning Male multidisciplinary Risk assessment Science Science (multidisciplinary) Sexual and Gender Minorities Sexually transmitted diseases Sexually Transmitted Diseases - epidemiology STD |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1NT9wwELUQUiUuFVBa0gJKJW4QrR1_xD5CBVqq0kuh4mY5sQ1IKIvYRYh_z4yTXVgo5cI1dqTx80z8nLHfELIdJKc-GAikCOEmnGKFcUwVFefC1XWsZIV3h49_q-Gp-Hkmz56U-sIzYZ08cAfcgPk6aFM2zPMgVKQmChcbXkofm8brpAQKa96TzVSn6l0awUx_S4ZyPRjDSoW3yUpRAGVgsqBzK1ES7P8Xy3x5WPJZxjQtRIfL5GPPIPO9zvIVshDaVfKhqyl5_4moX7jdvkt_PPOIUhHAJn3eV4c4z4Gk5n9OjsaD4dHf_PoG8zQ4N2vk9PDg5Mew6IsjFI0UbAJg0srLmjpmvADaA18uV8ZoKDdCo-KM8Q64H1cQkCLw4FgtqGh8VToYi6_5Z7LYjtqwTvLAYKMaPXIvL-oQjJENYEuldClzmBE2Bco2vXI4FrC4simDzbXtwLUArk3gWpqRndk7151uxn977yP-s56oeZ0egCfY3hPsW56QkY3p7Nk-EMe2NEorwIPBKL7PmiGEMC_i2jC6xT5A4lA102TkSzfZM0u4lpRXGlr0nBvMmTrf0l5eJJluRrHCrlIZ2Z16zKNdr2Px9T2w-EaWSnR1PHhoNsji5OY2bAJ7mtRbKVAeAHAtFFQ priority: 102 providerName: Directory of Open Access Journals – databaseName: Health & Medical Collection dbid: 7X7 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1LixQxEA66IngR37au0oI3bSbpPDo5iYrLrKgXd2VuIZ3HKkjPODOL-O-tSmd6GR977aQhqUfypSr5ipDnUXIaogFHSuBuwinWGMdU03EuXN-nTnb4dvjjJzU_Fe8XclECbptyrXK3JuaFOiw9xshnrVFaaSOYfrX60WDVKMyulhIaV8k1pC7DK13doptiLJjFEsyUtzKU69kG9it8U9aKBoADkw3d248ybf-_sObfVyb_yJvm7ejoFrlZcGT9elT8bXIlDnfI9bGy5K-7RH3AQ_fPHPesExJGAKYMdakRcVYDVK0_nxxvZvPjL_Vqjdka1NA9cnr07uTtvCklEhovBduCSGkXZE8dM0EA-IH1y7UpGcqN0Mg7Y4IDBMgVuKWIPDrWCyp86FoHcwk9v08OhuUQH5I6MjiupoAILIg-RmOk9z5QKV3OH1aE7QRlfeEPxzIW323OY3NtR-FaEK7NwrW0Ii-mf1Yje8alvd-g_KeeyHydPyzXZ7Y4kmWhj9q0ngUehUrUJOGS560MCUarRUUOd9qzxR039sJ4KvJsagZHwuyIG-LyHPsAlEPuTFORB6Oyp5FwLSnvNLToPTPYG-p-y_DtaybrZhTr7CpVkZc7i7kY1_9l8ejyaTwmN1o0YrxYaA7JwXZ9Hp8AOtr2T7ML_AYewgpd priority: 102 providerName: ProQuest – databaseName: SpringerOpen Free (Free internet resource, activated by CARLI) dbid: C6C link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3daxQxEB9KRfBF_Ha1ygq-6dJk87HJox6Wq6gvttK3kN0ktSB75e6K-N87k_2Q0yr4uklgMpnJ_LKT_AbgZVSChWjRkRK6m_SaV9ZzXTVCSN-2qVENvR3--EkvT-X7M3W2B_X0FiZf2s-Ulnmbnm6HHW4w0NBjsFpWGPG5qvCYfoOo28mqF3ox_1ehzJXkdnwfw4S5ZuhODMpU_dfhyz-vSf6WK80h6OgO3B6xY_lmkPYu7MX-Htwcqkn-uA_6Ax20v-d_nWUikgjEkaEc60KclwhPy88nx5vD5fGX8nJNGRpalQdwevTuZLGsxrIIVack36IaWRNUyzy3QSLgwT3L1ylZJqw0xDVjg0fUJzS6oowiet5KJrvQ1B7nElrxEPb7VR8fQxk5HlFTINQVZBujtarrusCU8jlnWACfFOW6kTOcSld8czl3LYwblOtQuS4r17ECXs1jLgfGjH_2fkv6n3sS23X-sFqfu3H1HQ9tNLbueBBR6sRskj51olYhobRGFnAwrZ4bXXDjaquNRn1wnMWLuRmdhzIivo-rK-qD8I34Mm0Bj4bFniURRjHRGGwxO2awI-puS3_xNRN0c0a1dbUu4PVkMb_k-rsunvxf96dwqyajpsuF9gD2t-ur-AwR0rZ9nl3iJyfzB_0 priority: 102 providerName: Springer Nature |
| Title | Lightweight federated learning for STIs/HIV prediction |
| URI | https://link.springer.com/article/10.1038/s41598-024-56115-0 https://www.ncbi.nlm.nih.gov/pubmed/38503789 https://www.proquest.com/docview/2968689418 https://www.proquest.com/docview/2972704729 https://pubmed.ncbi.nlm.nih.gov/PMC10950866 https://doaj.org/article/1dbe892c1d3e46f09f4afc325dfccd84 |
| Volume | 14 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1db9MwFLXGJiReEN9kjCpIvEGYHX8kfkCoqzZ1FZsQW1HfLCe2B9KUbm0n2L_nXictKhQkniLZTuQc3xsf59rnEvLaS06d1-BIAdxNWMUybZnKCs6FrapQyALPDp-cquFYjCZyskWW6Y46AOcbl3aYT2o8u3z34_r2Azj8-_bIeLk_h0kID4rlIgM2wGQGS_gdmJkKdNSTju63Wt-5FjHXB4qwZ0Am8u4czebHrM1VUdJ_Ew_9czvlbzHVOFUdPSD3O46Z9lujeEi2fPOI3G2zTt4-JuojLsi_x3-iaUAxCeCbLu3yR1ykQGPTs_Pj-f7w-Et6NcNIDo7eEzI-OjwfDLMufUJWS8EWADctnKyoZdoJIEbwbbN5CJpyLUrUpNHOAjvkClxWeO4tqwQVtStyC-_iKv6UbDfTxj8nqWewlA0O2ZkTlfday7quHZXSxthiQtgSKFN32uKY4uLSxBg3L00LrgFwTQTX0IS8Wd1z1Spr_LP1AeK_aomq2LFgOrswnZMZ5ipf6rxmjnuhAtVB2FDzXLoAvS1FQvaWo2eWlmZyrUoFeDB4i1eranAyjJzYxk9vsA3QPNTV1Al51g72qie8lJQXJdSUa2aw1tX1mubb1yjkzSjm4FUqIW-XFvOrX3_HYve_kHtB7uVo07gHUe-R7cXsxr8EIrWoeuROMSl6ZKffH52N4HpwePrpM5QO1KAXf070ov_8BKfCGNw |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwED-NTgheEN8EBgQJniCqHduJ_YAQg00t6yoEG9qbcWJnIKG2tJ2m_VP8jdzlo1P52NteYye6nH9n_-zz3QE8D0owHwwaUoXmJl3GE-N4luRCSFcUVa5yih3eH2eDQ_nhSB1twK8uFoauVXZzYj1R-2lJZ-T91GQ600Zy_Wb2M6GqUeRd7UpoNLDYC2enuGVbvB6-x_F9kaa7OwfvBklbVSApleRLlILlXhXMceMl8gU0eZdWlWHCSE2pWox3SJpEhkiWQQTHC8lk6fPUBV_6QuB3r8CmFLiV6cHm9s7446fVqQ75zSQ3bXQOE7q_wBWSothSmSBV4SphaytgXSjgX-z270uaf3hq6wVw9ybcaJlr_LaB2i3YCJPbcLWpZXl2B7IRbfNP65PWuKIUFchifdxWpTiOkRzHnw-Gi_5g-CWezck_RJi4C4eXor570JtMJ-EBxIHjBrnyxPm8LEIwRpVl6ZlSrvZYRsA7RdmyzVhOhTN-2NpzLrRtlGtRubZWrmURvFy9M2vydVzYe5v0v-pJubbrB9P5sW1N13JfBG3SknsRZFYxU0lXlSJVvkJptYxgqxs9204AC3sO1wierZrRdMkf4yZhekJ9kDxStk4Twf1msFeSCK2YyDW26DUYrIm63jL5_q1OD84ZVfbNsghedYg5l-v_unh48W88hWuDg_2RHQ3He4_gekqApmuNZgt6y_lJeIzcbFk8aQ0ihq-XbYO_AZmGR0o |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Lb9QwEB6VIhAXxJtAgSDBCaK1YzuxDwgBZbVLS4VEi3ozTmwXJLS77G5V9a_x65jJY6vl0VuvsRNNxvPZnz2eGYBnQQnmg0EgRYSbdAXPjONFVgohXVXFUpUUO_xxrxgdyA-H6nADfvWxMHStsp8Tm4naT2s6Ix_kptCFNpLrQeyuRXzaHr6e_cyoghR5WvtyGq2J7ITTE9y-LV6Nt3Gsn-f58P3-u1HWVRjIaiX5EiVipVcVc9x4idwB4e_yGA0TRmpK22K8QwIlCrRqGURwvJJM1r7MXfC1rwR-9xJcLoXihLHysFyd75AHTXLTxekwoQcLXCspni2XGZIWrjK2thY2JQP-xXP_vq75h8-2WQqHN-B6x2HTN63R3YSNMLkFV9qqlqe3odilDf9Jc-aaRkpWgXzWp119iqMUaXL6eX-8GIzGX9LZnDxFZB134OBClHcXNifTSbgPaeC4VY6e2J-XVQjGqLquPVPKNb7LBHivKFt3ucuphMYP2_jQhbatci0q1zbKtSyBF6t3Zm3mjnN7vyX9r3pS1u3mwXR-ZDsQW-6roE1ecy-CLCIzUbpYi1z5iNJqmcBWP3q2mwoW9sxwE3i6akYQk2fGTcL0mPogjaS8nSaBe-1gryQRWjFRamzRa2awJup6y-T7tyZROGdU47coEnjZW8yZXP_XxYPzf-MJXEXk2d3x3s5DuJaTPdP9RrMFm8v5cXiEJG1ZPW7QkMLXi4bfb2z_Sho |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Lightweight+federated+learning+for+STIs%2FHIV+prediction&rft.jtitle=Scientific+reports&rft.au=Nguyen%2C+Thi+Phuoc+Van&rft.au=Yang%2C+Wencheng&rft.au=Tang%2C+Zhaohui&rft.au=Xia%2C+Xiaoyu&rft.date=2024-03-19&rft.issn=2045-2322&rft.eissn=2045-2322&rft.volume=14&rft.issue=1&rft_id=info:doi/10.1038%2Fs41598-024-56115-0&rft.externalDBID=n%2Fa&rft.externalDocID=10_1038_s41598_024_56115_0 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2045-2322&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2045-2322&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2045-2322&client=summon |