A multi-source heterogeneous medical data enhancement framework based on lakehouse

Zhang G. Research on the deployment strategy of big data visualization platform by the internet of things technology. EAI Endorsed Trans Scalable Inf Syst. 2023;10(4):11. https://doi.org/10.4108/eetsis.v10i3.3051.

Article  Google Scholar 

Ge YF, Wang H, Bertino E, Zhan ZH, Cao J, Zhang Y, Zhang J. Evolutionary dynamic database partitioning optimization for privacy and utility. IEEE Trans Dependable Secure Comput. 2023. https://doi.org/10.1109/TDSC.2023.3302284.

Article  Google Scholar 

Ge Y-F, Yu W-J, Cao J, Wang H, Zhan Z-H, Zhang Y, Zhang J. Distributed memetic algorithm for outsourced database fragmentation. IEEE Trans Cybern. 2021;51(10):4808–21. https://doi.org/10.1109/TCYB.2020.3027962.

Article  Google Scholar 

Li J-Y, Zhan Z-H, Wang H, Zhang J. Data-driven evolutionary algorithm with perturbation-based ensemble surrogates. IEEE Trans Cybern. 2021;51(8):3925–37. https://doi.org/10.1109/TCYB.2020.3008280.

Article  Google Scholar 

Wang C, Sun B, Du KJ, Li JY, Zhan ZH, Jeon SW, Wang H, Zhang J. A novel evolutionary algorithm with column and sub-block local search for sudoku puzzles. IEEE Trans Games. 2024;16(1):162–72. https://doi.org/10.1109/TG.2023.3236490.

Article  Google Scholar 

Yang JQ, Yang QT, Du KJ, Chen CH, Wang H, Jeon SW, Zhang J, Zhan ZH. Bi-directional feature fixation-based particle swarm optimization for large-scale feature selection. IEEE Trans Big Data. 2023;9(3):1004–17. https://doi.org/10.1109/TBDATA.2022.3232761.

Article  Google Scholar 

Li JY, Du KJ, Zhan ZH, Wang H, Zhang J. Distributed differential evolution with adaptive resource allocation. IEEE Trans Cybern. 2023;53(5):2791–804. https://doi.org/10.1109/TCYB.2022.3153964.

Article  Google Scholar 

Shi W, Chen WN, Kwong S, Zhang J, Wang H, Gu T, Yuan H, Zhang J. A coevolutionary estimation of distribution algorithm for group insurance portfolio. IEEE Trans Syst Man Cybern Syst. 2022;52(11):6714–28. https://doi.org/10.1109/TSMC.2021.3096013.

Article  Google Scholar 

Huang T, Gong Y-J, Chen W-N, Wang H, Zhang J. A probabilistic niching evolutionary computation framework based on binary space partitioning. IEEE Trans Cybern. 2022;52(1):51–64. https://doi.org/10.1109/TCYB.2020.2972907.

Article  Google Scholar 

Hao R, Sheng M, Zhang Y, Zhao H, Hao C, Li W, Wang L, Li C. Enhancing clustering performance in sepsis time series data using gravity field. In: Health information science. Singapore: Springer; 2023. p. 199–212.

Chapter  Google Scholar 

Jiang H, Zhou R, Zhang L, Wang H, Zhang Y. Sentence level topic models for associated topics extraction. World Wide Web. 2019;22(6):2545–60. https://doi.org/10.1007/s11280-018-0639-1.

Article  Google Scholar 

Sarki R, Ahmed K, Wang H, Zhang Y. Automated detection of mild and multi-class diabetic eye diseases using deep learning. Health Inf Sci Syst. 2020;8(1):32. https://doi.org/10.1007/s13755-020-00125-5.

Article  Google Scholar 

Vimalachandran P, Liu H, Lin Y, Ji K, Wang H, Zhang Y. Improving accessibility of the Australian my health records while preserving privacy and security of the system. Health Inf Sci Syst. 2020;8(1):31. https://doi.org/10.1007/s13755-020-00126-4.

Article  Google Scholar 

Supriya S, Siuly S, Wang H, Zhang Y. Automated epilepsy detection techniques from electroencephalogram signals: a review study. Health Inf Sci Syst. 2020;8(1):33. https://doi.org/10.1007/s13755-020-00129-1.

Article  Google Scholar 

Pandey D, Wang H, Yin X, Wang K, Zhang Y, Shen J. Automatic breast lesion segmentation in phase preserved dce-mris. Health Inf Sci Syst. 2022;10(1):9. https://doi.org/10.1007/s13755-022-00176-w.

Article  Google Scholar 

Alvi AM, Siuly S, Wang H. A long short-term memory based framework for early detection of mild cognitive impairment from eeg signals. IEEE Trans Emerg Topics Comput Intell. 2023;7(2):375–88. https://doi.org/10.1109/TETCI.2022.3186180.

Article  Google Scholar 

Miao Z, Sealey MD, Sathyanarayanan S, Delen D, Zhu L, Shepherd S. A data preparation framework for cleaning electronic health records and assessing cleaning outcomes for secondary analysis. Inf Syst. 2023;111: 102130.

Article  Google Scholar 

Nguyen BNT, Phạm PN, Nguyen VT, Viet PQ, Tuan LD, Snasel V. Py_ape: Text data acquiring, extracting, cleaning and schema matching in python. In: Future data and security engineering. Big Data, security and privacy, smart city and industry 4.0 applications: 7th international conference, FDSE 2020, Quy Nhon, Vietnam, November 25–27, 2020, Proceedings 7. Springer; 2020. pp. 78–89.

Mutinda FW, Liew K, Yada S, Wakamiya S, Aramaki E. Automatic data extraction to support meta-analysis statistical analysis: a case study on breast cancer. BMC Med Inf Decis Mak. 2022;22(1):1–13.

Google Scholar 

Li H, Zhou G, Zhou S, Chen S, Mao S, Jin T Multi-source heterogeneous log fusion technology of power information system based on big data and imprecise reasoning theory. In: 2020 IEEE 20th international conference on communication technology (ICCT). 2020. pp. 1609–14. https://doi.org/10.1109/ICCT50939.2020.9295848

Lv Z, Deng W, Zhang Z, Guo N, Yan G. A data fusion and data cleaning system for smart grids big data. In: 2019 IEEE Intl conf on parallel & distributed processing with applications, big data & cloud computing, sustainable computing & communications, social computing & networking (ISPA/BDCloud/SocialCom/SustainCom). 2019. pp. 802–7. 10.1109/ISPA-BDCloud-SustainCom-SocialCom48970.2019.00119

Miao X, Wu Y, Wang J, Gao Y, Mao X, Yin J. Generative semi-supervised learning for multivariate time series imputation. In: Proceedings of the AAAI conference on artificial intelligence. 2021; pp. 8983–91.

Du W, Côté D, Liu Y. Saits: self-attention-based imputation for time series. Expert Syst Appl. 2023;219: 119619.

Article  Google Scholar 

Khayati M, Lerner A, Tymchenko Z, Cudré-Mauroux P. Mind the gap: an experimental evaluation of imputation of missing values techniques in time series. Proc VLDB Endowment. 2020;13:768–82.

Article  Google Scholar 

Ren P, Li S, Hou W, Zheng W, Li Z, Cui Q, Chang W, Li X, Zeng C, Sheng M. Mhdp: an efficient data lake platform for medical multi-source heterogeneous data. In: Web information systems and applications: 18th international conference, WISA 2021, Kaifeng, China, September 24–26, 2021, Proceedings 18. Springer; 2021. pp. 727–38.

Rekatsinas T, Chu X, Ilyas IF, Ré C. Holoclean: Holistic data repairs with probabilistic inference. 2017. Available from http://arxiv.org/abs/1702.00820

Rubin DB, Schenker N. Multiple imputation in health-are databases: an overview and some applications. Stat Med. 1991;10(4):585–98.

Article  Google Scholar 

Das PP, Mast M, Wiese L, Jack T, Wulf A. Data extraction for associative classification using mined rules in pediatric intensive care data. BTW; 2023.

Google Scholar 

Li H, Zhou G, Zhou S, Chen S, Mao S, Jin T Multi-source heterogeneous log fusion technology of power information system based on big data and imprecise reasoning theory. In: 2020 IEEE 20th international conference on communication technology (ICCT). IEEE; 2020. pp. 1609–14.

Wang C, Feng S. Research on collection and preprocessing of multisource heterogeneous elevator data. In: 2020 IEEE international conference on power, intelligent computing and systems (ICPICS). IEEE; 2020. p. 490–3.

Chapter  Google Scholar 

Lv Z, Deng W, Zhang Z, Guo N, Yan G. A data fusion and data cleaning system for smart grids big data. In: 2019 IEEE Intl Conf on parallel & distributed processing with applications, big data & cloud computing, sustainable computing & communications, social computing & networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE; 2019. pp. 802–7.

Ying Z, Huang Y, Chen K. Yu T Big data cleaning model of multi-source heterogeneous power grid based on machine learning classification algorithm. J Phys Conf Ser. 2021;2087: 012095.

Article  Google Scholar 

Dalca AV, Guttag J, Sabuncu MR. Unsupervised data imputation via variational inference of deep subspaces. 2019. Available form http://arxiv.org/abs/1903.03503

Srivastava M, Garg R, Mishra P. Analysis of data extraction and data cleaning in web usage mining. In: Proceedings of the 2015 international conference on advanced research in computer science engineering and technology (ICARCSET 2015). 2015. pp. 1–6.

Jonnalagadda SR, Goyal P, Huffman MD. Automating data extraction in systematic reviews: a systematic review. Syst Rev. 2015;4(1):78. https://doi.org/10.1186/s13643-015-0066-7.

Article  Google Scholar 

Pradhan R, Hoaglin DC, Cornell M, Liu W, Wang V. Automatic extraction of quantitative data from clinicaltrials.gov to conduct meta-analyses. J Clin Epidemiol. 2019;105:92–100. https://doi.org/10.1016/j.jclinepi.2018.08.023.

Article  Google Scholar 

Gao P, Han H. Robust web data extraction based on weighted path-layer similarity. J Comput Inf Syst. 2022;62(3):536–46.

Google Scholar 

Musleh M, Ouzzani M, Tang N, Doan A. Coclean: Collaborative data cleaning. In: Proceedings of the 2020 ACM SIGMOD international conference on management of data. 2020. pp. 2757–60.

Liu W, Zhang C, Yu B, Li Y. A general multi-source data fusion framework. In: Proceedings of the 2019 11th international conference on machine learning and computing. IEEE; 2019. p. 285–9.

Chapter  Google Scholar 

Krishnan S, Wu E Alphaclean: Automatic generation of data cleaning pipelines. 2019. Available from http://arxiv.org/abs/1904.11827

Batista GE, Monard MC. A study of k-nearest neighbour as an imputation method. His. 2002;87(251–260):48.

Google Scholar 

Singh R, Subramani S, Du J, Zhang Y, Wang H, Miao Y, Ahmed K. Antisocial behavior identification from twitter feeds using traditional machine learning algorithms and deep learning. EAI Endorsed Trans Scalable Inf Syst. 2023;10:17. https://doi.org/10.4108/eetsis.v10i3.3184.

Article  Google Scholar 

Cao W, Wang D, Li J, Zhou H, Li L, Li Y. Brits: bidirectional recurrent imputation for time series. Adv Neural Inf Process Syst. 2018;31:10.

Google Scholar 

Luo Y, Zhang Y, Cai X, Yuan X. E2gan: End-to-end generative adversarial network for multivariate time series imputation. In: Proceedings of the 28th international joint conference on artificial intelligence. AAAI press; 2019. p. 3094–100.

Google Scholar 

Zhang Y, Sheng M, Liu X, Wang R, Lin W, Ren P, Wang X, Zhao E, Song W. A heterogeneous multi-modal medical data fusion framework supporting hybrid data exploration. Health Inf Sci Syst. 2022;10(1):22.

Article  Google Scholar 

Hyndman RJ. Hospital. 2015. http://www.hospitalcompare.hhs.gov/

Barry Becker RK. Adult. 1996. https://archive.ics.uci.edu/dataset/2/adult

Royston P. Multiple imputation of missing values. Stand Genomic Sci. 2004;4(3):227–41.

Google Scholar 

Breiman L. Random forests. Mach Learn. 2001;45:5–32.

Article  Google Scholar 

Johnson A, Bulgarelli L, Pollard T, Horng S, Celi LA, Mark R. Mimic-iv. PhysioNet. 2020. https://physionet.org/content/mimiciv/1.0/ . Accessed 23 Aug 2021.

Pollard TJ, Johnson AE, Raffa JD, Celi LA, Mark RG, Badawi O. The EICU collaborative research database, a freely available multi-center database for critical care research. Sci Data. 2018;5(1):1–13.

Article  Google Scholar 

Chen T, He T, Benesty M, Khotilovich V, Tang Y, Cho H, Chen K, Mitchell R, Cano I, Zhou T. Xgboost: extreme gradient boosting. R package version 0.4-2. 2015;1:1–4.

Balakrishnama S, Ganapathiraju A. Linear discriminant analysis-a brief tutorial. Inst Signal Inf Process. 1998;18(1998):1–8.

Google Scholar 

Gunn SR. Support vector machines for classification and regression. ISIS Techn Rep. 1998;14(1):5–16.

Google Scholar 

Fujimoto S, Meger D, Precup D. Off-policy deep reinforcement learning without exploration. In: International conference on machine learning. PMLR; 2019. p. 2052–62.

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