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Review of Machine Learning and Deep Learning Based Recommender Systems for Health Informatics

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Book cover Deep Learning Techniques for Biomedical and Health Informatics

Part of the book series: Studies in Big Data ((SBD,volume 68))

Abstract

Recommender Systems have become essential in personalized healthcare as they provide meaningful information to the patients depending on the specific requirements and availability of health records. With the improvement of machine learning techniques, the recommender system brings about several opportunities to the medical science. Systems can perform more efficiently and solve complex problems using deep learning, even when data set is diverse and unstructured. Here we present a comprehensive overview of the challenges associated with the existing recommender systems. Machine learning and deep learning techniques that are generally applied for health recommender system are discussed in detail along with their application to health informatics.

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References

  1. Swan, M.: Sensor mania! The internet of things, wearable computing, objective metrics, and the quantified self 2.0. J. Sens. Actuator Netw. 1(3), 217–253 (2012)

    Article  Google Scholar 

  2. Calero Valdez, A., Ziefle, M., Verbert, K., Felfernig, A., Holzinger, A.: Recommender systems for health informatics: state-of-the-art and future perspectives. In: Holzinger, A. (ed.) Machine Learning for Health Informatics. Lecture Notes in Computer Science, vol. 9605. Springer, Cham (2016)

    Google Scholar 

  3. Erdeniz, S.P., Maglogiannis, I., Menychtas, A., Felfernig, A., Tran, T.N.T.: Recommender systems for IoT enabled m-health applications. In: Iliadis, L., Maglogiannis, I., Plagianakos, V. (eds.) Artificial Intelligence Applications and Innovations. AIAI 2018. IFIP Advances in Information and Communication Technology, vol. 520. Springer, Cham (2018)

    Google Scholar 

  4. Ramsundar, B., Kearnes, S., Riley, P., Webster, D., Konerding, D., Pande, V.: Massively Multitask Networks for Drug Discovery (2015). arXiv:1502.02072

  5. Zhang, S., Zhou, J., Hu, H., Gong, H., Chen, L., Cheng, C., Zeng, J.: A deep learning framework for modeling structural features of RNA-binding protein targets. Nucleic Acids Res. 44 (2015). https://doi.org/10.1093/nar/gkv1025

    Article  Google Scholar 

  6. Tian, K., Shao, M., Wang, Y., Guan, J., Zhou, S.: Boosting compound-protein interaction prediction by deep learning. Methods 110 (2016). https://doi.org/10.1016/j.ymeth.2016.06.024

    Article  Google Scholar 

  7. Xu, T., Zhang, H., Huang, X., Zhang, S., Metaxas, D.N.: Multimodal deep learning for cervical dysplasia diagnosis. In: Ourselin, S., Joskowicz, L., Sabuncu, M., Unal, G., Wells, W. (eds.) Medical Image Computing and Computer-Assisted Intervention—MICCAI 2016. MICCAI 2016. Lecture Notes in Computer Science, vol. 9901. Springer, Cham (2016)

    Chapter  Google Scholar 

  8. Brosch, T., Tam, R., The Alzheimer’s Disease Neuroimaging Initiative: Manifold learning of brain MRIs by deep learning. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) Medical Image Computing and Computer-Assisted Intervention—MICCAI 2013. MICCAI 2013. Lecture Notes in Computer Science, vol. 8150. Springer, Berlin (2013)

    Google Scholar 

  9. Rose, D.C., Arel, I., Karnowski, T.P., Paquit, V.C.: Applying deep-layered clustering to mammography image analytics. In: Biomedical Sciences and Engineering Conference, Oak Ridge, TN, pp. 1–4 (2010)

    Google Scholar 

  10. Acharya, U.R., Fujita, H., Oh, S., Hagiwara, Y., Tan, J.H., Adam, M.: Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals. Inf. Sci. 415–416, 190–198 (2017)

    Article  Google Scholar 

  11. Hassan, M.M., Huda, S., Uddin, M.Z., Almogren, A., Alrubaian, M.: Human activity recognition from body sensor data using deep learning. J. Med. Syst. 42, 99 (2018)

    Article  Google Scholar 

  12. Poggi, M., Mattoccia, S.: A wearable mobility aid for the visually impaired based on embedded 3d vision and deep learning. In Proceeding of IEEE Symposium of Computer and Communication, pp. 208–213 (2016)

    Google Scholar 

  13. Huang, J., Zhou, W., Li, H., Li, W.: Sign language recognition using real-sense. In: Proceeding of IEEE China, SIP, pp. 166–170 (2015)

    Google Scholar 

  14. Garimella, V.R.K., Alfayad, A., Weber, I.: Social media image analysis for public health. In: Proceeding of CHI Conference Human Factors Computer System, pp. 5543–5547 (2016)

    Google Scholar 

  15. Zou, B., Lampos, V., Gorton, R., Cox, I.J. On infectious intestinal disease surveillance using social media content. In: Proceeding of 6th International Conference on Digital Health Conference, pp. 157–161 (2016)

    Google Scholar 

  16. Saha, J., Chowdhury, C., Biswas, S.: Two phase ensemble classifier for smartphone based human activity recognition independent of hardware configuration and usage behavior. Microsyst. Technol. 24, 2737 (2018)

    Article  Google Scholar 

  17. Huang, T., Lan, L., Fang, X., An, P., Min, J., Wang, F.: Promises and challenges of big data computing in health sciences. Big Data Res. 2(1), 2–11 (2015)

    Article  Google Scholar 

  18. Yang, S., Zhou, P., Duan, K., Hossain, M.S., Alhamid, M.F.: emHealth: towards emotion health through depression prediction and intelligent health recommender system. Mob. Netw. Appl. 23, 216–226 (2018)

    Article  Google Scholar 

  19. Hussein, A.S., Omar, W.M., Li, X., Ati, M.: Efficient chronic disease diagnosis prediction and recommendation system. In: Proceeding of IEEE-EMBS Conference on Biomedical Engineering and Sciences, Langkawi, pp. 209–214 (2012)

    Google Scholar 

  20. Felipe, LO., Barrué, C., Cortés, A., Wolverson, E., Antomarini, M., Landrin, I., Votis, K., Paliokas, I., Cortés, U.: Health recommender system design in the context of CAREGIVERSPROMMD project. In: Proceeding of PETRA ’18: The 11th PErvasive Technologies Related to Assistive Environments Conference, June, Corfu, Greece (2018)

    Google Scholar 

  21. Morrell, T.G., Kerschberg, I.: Personal health explorer: a semantic health recommendation system. In: Proceeding of IEEE 28th International Conference on Data Engineering Workshops, Arlington, VA, pp. 55–59 (2012)

    Google Scholar 

  22. Bocanegra, C.L.S., Ramos, J.L.S., Rizo, C., Civit, A., Fernandez-Luque, L.: HealthRecSys: a semantic content-based recommender system to complement health videos. BMC Med. Inform. Decis. Mak. 17, 63 (2017)

    Article  Google Scholar 

  23. Sanchez-Bocanegra, C.L., Sanchez-Laguna, F., Sevillano, J.L.: Introduction on health recommender systems. Methods Mol. Biol. 1246, 131–146 (2015)

    Google Scholar 

  24. Keogh, E.: Instance-based learning. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning. Springer, Boston (2011)

    Google Scholar 

  25. Ustev, Y.E., Incel, O.D., Ersoy, C.: User, device and orientation independent human activity recognition on mobile phone challenges and a proposal. In: The ACM Conference on Pervasive and Ubiquitous Computing Adjunct Publication, Zurich, pp. 1427–1435 (2013)

    Google Scholar 

  26. Park, H., Dong, S.Y., Lee, M., Youn, I.: The role of heart-rate variability parameters in activity recognition and energy-expenditure estimation using wearable sensors. Sensors (Basel) 2017(7), 1698 (2017)

    Article  Google Scholar 

  27. Shoaib, M., Bosch, S., Incel, O.D., Scholten, H., Havinga, P.J.M.: Complex human activity recognition using smartphone and wrist-worn motion sensors. In: Sensors, p. 426 (2016)

    Google Scholar 

  28. Zhang, S., Rowlands, A.V., Murray, P., Hurst, T.L.: Physical activity classification using the GENEA wrist-worn accelerometer. Med. Sci. Sports Exerc. 44, 742–748 (2012)

    Article  Google Scholar 

  29. Garcia-Ceja, E., Brena, R.F., Carrasco-Jimenez, J.C., Garrido, L.: Long-term activity recognition from wristwatch accelerometer data. Sensors 14, 22500–22524 (2014)

    Article  Google Scholar 

  30. Saha, J., Chowdhury, C., Biswas, S.: Device independent activity monitoring using smart handhelds. In: Proceeding of 7th International Conference on Cloud Computing, Data Science and Engineering—Confluence, Noida, pp. 406–411 (2017)

    Google Scholar 

  31. Bayat, A., Pomplun, M., Tran, D.A.: A study on human activity recognition using accelerometer data from smartphones. Procedia Comput. Sci. 34, 450–457 (2014)

    Article  Google Scholar 

  32. Saha, J., Roy Chowdhury, I,, Chowdhury, C., Biswas, S., Aslam, N.: An ensemble of condition based classifiers for device independent detailed human activity recognition using smartphones. Information 9(4), 94 (2018)

    Article  Google Scholar 

  33. Jamshidi, S., Torkamani, M.A., Mellen, J., Jhaveri, M., Pan, P., Chung, J., Kardes, H.: A hybrid health journey recommender system using electronic medical records. In: The Proceedings of the 3rd International Workshop on Health Recommender Systems, HealthRecSys 2018, co-located with the 12th ACM Conference on Recommender Systems (ACM RecSys 2018), Vancouver, BC, Canada (2018)

    Google Scholar 

  34. Stikic, M., Schiele, B.: Activity recognition from sparsely labeled data using multi-instance learning. In: Proceeding of Location and Context Awareness. LoCA 2009. Lecture Notes in Computer Science, vol. 5561. Springer, Berlin (2009)

    Google Scholar 

  35. Toda, T., Inoue, S., Tanaka, S., Ueda, N.: Training human activity recognition for labels with inaccurate time stamps. In: Proceeding of UbiComp ’14 Adjunct, pp. 863–872, 13–17 Sept 2014

    Google Scholar 

  36. Stikic, M., Larlus, D., Schiele, B.: Multi-graph based semisupervised learning for activity recognition. In: Proceeding of International Symposium on Wearable Computers, Linz, pp. 85–92 (2009)

    Google Scholar 

  37. Ong, W.H.: An unsupervised approach for human activity detection and recognition. Int. J. Simul. Syst. Sci. Technol. 14(5) (2013)

    Google Scholar 

  38. https://medium.com/odessa-ml-club/a-journey-to-clustering-introduction-to-dbscan-e724fa899b6f. Last seen 20/5/2019

  39. Kwon, Y., Kang, K., Bae, C.: Unsupervised learning for human activity recognition using smartphone sensors. Expert Syst. Appl. 41(14), 6067–6074 (2014)

    Article  Google Scholar 

  40. Lara, O.D., Labrador, M.A.: A survey of human activity recognition using wearable sensors. In: IEEE Communication Surveys and Tutorials, vol. 15 (2013)

    Article  Google Scholar 

  41. Yuan, W., Li, C., Guan, D., et al.: Socialized healthcare service recommendation using deep learning. Neural Comput. Appl. 30, 2071–2082 (2018)

    Article  Google Scholar 

  42. Eskofier, B.M., et al.: Recent machine learning advancements in sensor-based mobility analysis: deep learning for Parkinson’s disease assessment. In: Proceeding of 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, pp. 655–658 (2016)

    Google Scholar 

  43. Sun, J., Fu, Y., Li, S., He, J., Xu, C., Tan, L.: Sequential human activity recognition based on deep convolutional network and extreme learning machine using wearable sensors. Hindawi J. Sens. (8580959), 10 (2018)

    Google Scholar 

  44. Sahoo, A.K., Pradhan, C., Barik, R.K., Dubey, H.: DeepReco: deep learning based health recommender system using collaborative filtering. Computation 7(25) (2019)

    Article  Google Scholar 

  45. Zhang, C., Wang, G., Zhao, J., Gao, P., Lin, J., Yang, H.: Patient-specific ECG classification based on recurrent neural networks and clustering technique. In: Proceeding of 13th IASTED International Conference on Biomedical Engineering (BioMed), Innsbruck, Austria, pp. 63–67 (2017)

    Google Scholar 

  46. Miotto, R., Li, L., Kidd, A.B., Dudley, J.T.: Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci. Rep. 6, 1–10 (2016)

    Google Scholar 

  47. Fakoor, R., Ladhak, F., Nazi, A., Huber, M.: Using deep learning to enhance cancer diagnosis and classification. In: Proceedings of the 30th International Conference on Machine Learning, JMLR: W&CP vol. 28, Atlanta, Georgia, USA (2013)

    Google Scholar 

  48. Sedhain, S., Menon, A.K., Xie, L., Sanner, S.: AutoRec: auto encoders meet collaborative filtering. In: Proceeding of 24th International Conference World Wide Web, Florence, Italy (2015)

    Google Scholar 

  49. Deng, X., Huangfu, F.: Collaborative variational deep learning for healthcare recommendation. IEEE Access 7, 55679–55688 (2019)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Computer Science and Engineering, Jadavpur University, Kolkata, India

    Jayita Saha & Chandreyee Chowdhury

  2. Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, India

    Suparna Biswas

Authors
  1. Jayita Saha
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  2. Chandreyee Chowdhury
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  3. Suparna Biswas
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Corresponding author

Correspondence to Chandreyee Chowdhury .

Editor information

Editors and Affiliations

  1. Department of Computer Science, North Orissa University, Takatpur, Odisha, India

    Sujata Dash

  2. School of Computer Science and Engineering, KIIT Deemed to University, Bhubaneswar, Odisha, India

    Biswa Ranjan Acharya

  3. Computer Science and Engineering Department, G. B. Pant Government Engineering College, New Delhi, Delhi, India

    Mamta Mittal

  4. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, AL, USA

    Ajith Abraham

  5. Department of Organizational Systems and Adult Health, University of Maryland, Baltimore, MD, USA

    Arpad Kelemen

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Saha, J., Chowdhury, C., Biswas, S. (2020). Review of Machine Learning and Deep Learning Based Recommender Systems for Health Informatics. In: Dash, S., Acharya, B., Mittal, M., Abraham, A., Kelemen, A. (eds) Deep Learning Techniques for Biomedical and Health Informatics. Studies in Big Data, vol 68. Springer, Cham. https://doi.org/10.1007/978-3-030-33966-1_6

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