Disease Prediction by Machine Learning Over Big Data From Healthcare Communities
Open accessAbstract
With big data growth in biomedical and healthcare communities, accurate analysis of medical data benefits early disease detection, patient care, and community services. However, the analysis accuracy is reduced when the quality of medical data is incomplete. Moreover, different regions exhibit unique characteristics of certain regional diseases, which may weaken the prediction of disease outbreaks. In this paper, we streamline machine learning algorithms for effective prediction of chronic disease outbreak in disease-frequent communities. We experiment the modified prediction models over real-life hospital data collected from central China in 2013-2015. To overcome the difficulty of incomplete data, we use a latent factor model to reconstruct the missing data. We experiment on a regional chronic disease of cerebral infarction. We propose a new convolutional neural network (CNN)-based multimodal disease risk prediction algorithm using structured and unstructured data from hospital. To the best of our knowledge, none of the existing work focused on both data types in the area of medical big data analytics. Compared with several typical prediction algorithms, the prediction accuracy of our proposed algorithm reaches 94.8% with a convergence speed, which is faster than that of the CNN-based unimodal disease risk prediction algorithm.
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AI/ML techniques have been widely applied in healthcare to generate insights from massive amounts of data.
“AI/ML techniques have been widely applied in healthcare to generate insights from massive amounts of data [20–22]”
From Artificial intelligence and machine learning in mobile apps for mental health: A scoping review (2022) · cites this paper as [22]
Cites
Business intelligence and analytics: from big data to big impact
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012) · MIS Quarterly · doi:10.2307/41703503 · Cited by 6,250 · see citing works
Chen, M., Mao, S., & Liu, Y. (2014) · Mobile Networks and Applications · doi:10.1007/s11036-013-0489-0 · Cited by 2,913 · see citing works
Self-citationMining electronic health records: towards better research applications and clinical care
Jensen, P. B., Jensen, L. J., & Brunak, S. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3208 · Cited by 1,680 · see citing works
Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients
Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014) · Health Affairs · doi:10.1377/hlthaff.2014.0041 · Cited by 1,235 · see citing works
Health-CPS: Healthcare Cyber-Physical System Assisted by Cloud and Big Data
Zhang⋆, Y., Meikang Qiu, A., Tsai, C., Hassan, M. M., & Alamri, A. M. (2015) · IEEE Systems Journal · doi:10.1109/jsyst.2015.2460747 · Cited by 927 · see citing works
Risk factors and risk assessment tools for falls in hospital in-patients: a systematic review
Oliver, D. (2004) · Age and Ageing · doi:10.1093/ageing/afh017 · Cited by 714 · see citing works
Smart Clothing: Connecting Human with Clouds and Big Data for Sustainable Health Monitoring
Chen, M., Ma, Y., Song, J., Lai, C., & Hu, B. (2016) · Mobile Networks and Applications · doi:10.1007/s11036-016-0745-1 · Cited by 446 · see citing works
Self-citationA Manufacturing Big Data Solution for Active Preventive Maintenance
Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., & Vasilakos, A. V. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2670505 · Cited by 443 · see citing works
Wearable 2.0: Enabling Human-Cloud Integration in Next Generation Healthcare Systems
Chen, M., Ma, Y., Li, Y., et al. (2017) · IEEE Communications Magazine · doi:10.1109/mcom.2017.1600410cm · Cited by 379 · see citing works
Self-citationThe 'big data' revolution in healthcare: Accelerating value and innovation
Groves, P., Kayyali, B., Knott, D., & Kuiken, S. V. (2016) · Cited by 356 · see citing works
Zhang, Y., Yang, Z., Lu, H., Zhou, X., Phillips, P., Liu, Q., & Wang, S. (2016) · IEEE Access · doi:10.1109/access.2016.2628407 · Cited by 279 · see citing works
Enhanced fingerprinting and trajectory prediction for IoT localization in smart buildings
Lin, K., Chen, M., Deng, J., Hassan, M. M., & Fortino, G. (2016) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2016.2543242 · Cited by 246 · see citing works
Self-citationConvolutional Neural Network for Paraphrase Identification
Yin, W., & Schütze, H. (2015) · North American Chapter of the Association for Computational Linguistics · doi:10.3115/v1/n15-1091 · Cited by 236 · see citing works
Cost minimization while satisfying hard/soft timing constraints for heterogeneous embedded systems
Qiu, M., & Sha, E. H. ‐. (2009) · ACM Transactions on Design Automation of Electronic Systems · doi:10.1145/1497561.1497568 · Cited by 230 · see citing works
Chen, M., Zhou, P., & Fortino, G. (2016) · IEEE Access · doi:10.1109/access.2016.2641480 · Cited by 150 · see citing works
Self-citationSingh, A., Nadkarni, G., Gottesman, O., Ellis, S. B., Bottinger, E. P., & Guttag, J. V. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.11.005 · Cited by 136 · see citing works
Wang, S., Zhan, T., Chen, Y., et al. (2016) · IEEE Access · doi:10.1109/access.2016.2620996 · Cited by 131 · see citing works
Zhai, S., Chang, K.-H., Zhang, R., & Zhang, Z. M. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939759 · Cited by 104 · see citing works
Optimal Big Data Sharing Approach for Tele-Health in Cloud Computing
Qiu, L., Gai, K., & Meikang Qiu, A. (2016) · IEEE International Conference on Smart Cloud (SmartCloud) · doi:10.1109/smartcloud.2016.21 · Cited by 104 · see citing works
Enabling real-time information service on telehealth system over cloud-based big data platform
Wang, J., Qiu, M., & Guo, B. (2016) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2016.05.003 · Cited by 91 · see citing works
A relative similarity based method for interactive patient risk prediction
Qian, B., Wang, X., Cao, N., et al. (2014) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0379-5 · Cited by 90 · see citing works
Bandyopadhyay, S., Wolfson, J. A., Vock, D. M., Vazquez‐Benitez, G., Adomavičius, G., Elidrisi, M., Johnson, P. E., & O’Connor, P. J. (2014) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0386-6 · Cited by 83 · see citing works
Tian, D., Zhou, J., Wang, Y., et al. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2422144 · Cited by 80 · see citing works
Localization Based on Social Big Data Analysis in the Vehicular Networks
Lin, K., Luo, J., Hu, L., Hossain, M. S., & Ghoneim, A. (2016) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2016.2641467 · Cited by 80 · see citing works
Zhang, Y., Chen, X., Zhan, T., Jiao, Z., Sun, Y., Chen, Z., Yao, Y., Fang, L., Lv, Y., & Wang, S. (2016) · IEEE Access · doi:10.1109/access.2016.2611530 · Cited by 78 · see citing works
Chu, S.-M., Shih, W., Yang, Y., Chen, P., & Chu, Y.-H. (2015) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2015.03.047 · Cited by 56 · see citing works
Dynamic Hierarchical Classification for Patient Risk-of-Readmission
Roy, S. B., Teredesai, A. M., Zolfaghar, K., et al. (2015) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2788585 · Cited by 53 · see citing works
HEART Score to Further Risk Stratify Patients With Low TIMI Scores
Marcoon, S., Chang, A. M., Lee, B., Salhi, R. A., & Hollander, J. E. (2013) · Critical Pathways in Cardiology A Journal of Evidence-Based Medicine · doi:10.1097/hpc.0b013e31827377e1 · Cited by 45 · see citing works
Septic Shock Prediction for Patients with Missing Data
Ho, J. C., Lee, C. H., & Ghosh, J. (2014) · ACM Transactions on Management Information Systems · doi:10.1145/2591676 · Cited by 44 · see citing works
Simultaneous Modeling of Multiple Diseases for Mortality Prediction in Acute Hospital Care
Nori, N., Kashima, H., Yamashita, K., et al. (2015) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783308 · Cited by 41 · see citing works
A Study on Lung Cancer Detection by Image Processing
Wang, W., & Wu, S. (2006) · International Conference on Communications, Circuits and Systems · doi:10.1109/icccas.2006.284656 · Cited by 23 · see citing works
Cognitive Computing and Big Data Analytics
(2012) · Wiley eBooks · doi:10.1002/9781119183648 · Cited by 12 · see citing works
Flagged first, then by citations
Cited by
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Artificial intelligence for mental health and mental illnesses: an overview
Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H.-C., & Jeste, D. V. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1094-0 · Cited by 970 · see citing works
Rasmy, L., Xiang, Y., Xie, Z., Tao, C., & Zhi, D. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00455-y · Cited by 886 · see citing works
Multimodal machine learning in precision health: A scoping review
Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., & Luo, Y. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00712-8 · Cited by 528 · see citing works
Medical 4.0 technologies for healthcare: Features, capabilities, and applications
Haleem, A., Javaid, M., Pratap Singh, R., & Suman, R. (2022) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2022.04.001 · Cited by 468 · see citing works
Artificial intelligence and the implementation challenge
Shaw, J., Rudzicz, F., Jamieson, T., & Goldfarb, A. (2019) · Journal of Medical Internet Research · doi:10.2196/13659 · Cited by 389 · see citing works
Ye, C., Fu, T., Hao, S., Zhang, Y., Wang, O., Jin, B., Xia, M., Liu, M., Zhou, X., Wu, Q., et al. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9268 · Cited by 250 · see citing works
A Survey on Security and Privacy Issues in Modern Healthcare Systems: Attacks and Defenses
Newaz, A. I., Sikder, A. K., Rahman, M. A., & Uluagac, A. S. (2021) · ACM Transactions on Computing for Healthcare · doi:10.1145/3453176 · Cited by 225 · see citing works
Homecare robotic systems for healthcare 4.0: visions and enabling technologies
Yang, G., Pang, Z., Jamal Deen, M., Dong, M., Zhang, Y.-T., Lovell, N., & Rahmani, A. M. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2990529 · Cited by 213 · see citing works
Data-Driven Computing and Caching in 5G Networks: Architecture and Delay Analysis
Chen, M., Qian, Y., Hao, Y., Li, Y., & Song, J. (2018) · IEEE Wireless Communications · doi:10.1109/mwc.2018.1700216 · Cited by 195 · see citing works
Self-citationThe emerging role of cognitive computing in healthcare: a systematic literature review
Behera, R. K., Bala, P. K., & Dhir, A. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.04.024 · Cited by 193 · see citing works
Goldberg, S. B., Flemotomos, N., Martinez, V. R., Tanana, M. J., Kuo, P. B., Pace, B. T., Villatte, J. L., Georgiou, P. G., Van Epps, J., Imel, Z. E., et al. (2020) · Journal of Counseling Psychology · doi:10.1037/cou0000382 · Cited by 141 · see citing works
FairLens: Auditing black-box clinical decision support systems
Panigutti, C., Perotti, A., Panisson, A., Bajardi, P., & Pedreschi, D. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102657 · Cited by 86 · see citing works
Artificial intelligence and machine learning in mobile apps for mental health: A scoping review
Milne-Ives, M., Selby, E., Inkster, B., Lam, C., & Meinert, E. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000079 · Cited by 68 · see citing works
Choudhury, A. (2022) · JMIR Human Factors · doi:10.2196/35421 · Cited by 56 · see citing works
IoT-based Technologies for Addressing the Unique Healthcare Needs of the Elderly Population
Ali, S. A., & Khan, R. (2023) · doi:10.20944/preprints202303.0088.v1 · Cited by 7 · see citing works
- Authors
- Chen, M., Hao, Y., Hwang, K., Wang, L., & Wang, L.
- Affiliations
- Huazhong University of Science and Technology (CN)
- University of Southern California (US)
- Venue
- IEEE Access
- Published
- 2017
- Volume
- 5
- Pages
- 8869-8879
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2610135452
- Cited by
- 1,311
- References
- 32
- Citations by year
- 2026: 97 · 2025: 128 · 2024: 154 · 2023: 153 · 2022: 184 · 2021: 185 · 2020: 183 · 2019: 126 · 2018: 81 · 2017: 19 · 2012: 1
- FWCI
- 153.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (61572220, 81671904)
- International Science and Technology Corporation Program of Chinese Ministry of Science and Technology (S2014ZR0340)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Keywords
- disease risk prediction, cerebral infarction, multimodal fusion, convolutional neural networks, missing data imputation, latent factor models, big data in healthcare, hospital data
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