Machine Learning Techniques in Adaptive and Personalized Systems for Health and Wellness

References

• Abd, D., Alwan, J. K., Ibrahim, M., & Naeem, M. B. (2017). The utilisation of machine learning approaches for medical data classification and personal care system mangementfor sickle cell disease [Paper presentation]. 2017 Annual Conference on New Trends in Information and Communications Technology Applications, NTICT 2017 (pp. 213–218). https://doi.org/10.1109/NTICT.2017.7976147
   Google Scholar
• Abdo, M. A., Abdel-Hamid, A. A., & Elzouka, H. A. (2020). A cloud-based mobile healthcare monitoring framework with location privacy preservation [Paper presentation]. 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT) (pp. 1–8). https://doi.org/10.1109/3ICT51146.2020.9311999
   Google Scholar
• Afreen, N., Singh, S., & Kumar, S. (2019). Individualized patient- centered type 2 diabetes recommender system. In International Conference on Advances in Computing and Data Sciences (ICACDS) (pp. 45–54). Springer. https://doi.org/10.1007/978-981-13-9939-8_5
   Google Scholar
• Akbulut, A., Ertugrul, E., & Topcu, V. (2018). Fetal health status prediction based on maternal clinical history using machine learning techniques. Computer Methods and Programs in Biomedicine, 163, 87–100. https://doi.org/10.1016/j.cmpb.2018.06.010
   PubMed Web of Science ®Google Scholar
• Alfian, G., Syafrudin, M., Ijaz, M. F., Syaekhoni, M. A., Fitriyani, N. L., & Rhee, J. (2018). A personalized healthcare monitoring system for diabetic patients by utilizing BLE-based sensors and real-time data processing. Sensors, 18(7), 2183. https://doi.org/10.3390/s18072183
   PubMed Web of Science ®Google Scholar
• Alharthi, R., Alharthi, R., Guthier, B., & El Saddik, A. (2019). CASP: Context-aware stress prediction system. Multimedia Tools and Applications, 78(7), 9011–9031. https://doi.org/10.1007/s11042-017-5246-0
   Web of Science ®Google Scholar
• Alloghani, M., Al-Jumeily, D., Mustafina, J., Hussain, A., & Aljaaf, A. J. (2020). A systematic review on supervised and unsupervised machine learning algorithms for data science. In M. W. Berry (Ed.), Supervised and unsupervised learning for data science (pp. 3–21). Springer. https://doi.org/10.1007/978-3-030-22475-2_1
   Google Scholar
• Apple Inc. (n.d.). Core ML overview – Machine learning – Apple developer. Retrieved October 31, 2021, from https://developer.apple.com/machine-learning/core-ml/
   Google Scholar
• Arpaia, P., Crauso, F., de Benedetto, E., Duraccio, L., Improta, G., & Serino, F. (2022). Soft transducer for patient’s vitals telemonitoring with deep learning-based personalized anomaly detection. Sensors, 22(2), 536. https://doi.org/10.3390/s22020536
   PubMed Web of Science ®Google Scholar
• Asthana, S., Megahed, A., & Strong, R. (2017). A recommendation system for proactive health monitoring using IoT and wearable technologies. In M. Z.-H. Tata S. (Ed.), Proceedings - 2017 IEEE 6th International Conference on AI and Mobile Services, AIMS 2017 (pp. 14–21). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/AIMS.2017.11
   Google Scholar
• Auffenberg, G. B., Ghani, K. R., Ramani, S., Usoro, E., Denton, B., Rogers, C., Stockton, B., Miller, D. C., & Singh, K. (2019). askMUSIC: Leveraging a clinical registry to develop a new machine learning model to inform patients of prostate cancer treatments chosen by similar men. European Urology, 75(6), 901–907. https://doi.org/10.1016/j.eururo.2018.09.050
   PubMed Web of Science ®Google Scholar
• Aujla, G. S., Jindal, A., Chaudhary, R., Kumar, N., Vashist, S., Sharma, N., & Obaidat, M. S. (2019). DLRS: Deep learning-based recommender system for smart healthcare ecosystem [Paper presentation]. ICC 2019 - 2019 IEEE International Conference on Communications (ICC) (pp. 1–6). https://doi.org/10.1109/ICC.2019.8761416
   Google Scholar
• Bae, S., Chung, T., Ferreira, D., Dey, A. K., & Suffoletto, B. (2018). Mobile phone sensors and supervised machine learning to identify alcohol use events in young adults: Implications for just-in-time adaptive interventions. Addictive Behaviors, 83(SI), 42–47. https://doi.org/10.1016/j.addbeh.2017.11.039
   PubMedGoogle Scholar
• Barata, F., Kowatsch, T., Tinschert, P., & Filler, A. (2016). Personal MobileCoach: Tailoring behavioral interventions to the needs of individual participants [Paper presentation]. UbiComp 2016 Adjunct - Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 1089–1094). https://doi.org/10.1145/2968219.2972713
   Google Scholar
• Barbosa, V. A. D. F., Gomes, J. C., de Santana, M. A., de Lima, C. L., Calado, R. B., Bertoldo Júnior, C. R., Albuquerque, J. E., de, A., de Souza, R. G., de Araújo, R. J. E., Mattos Júnior, L. A. R., de Souza, R. E., & dos Santos, W. P. (2021). Covid-19 rapid test by combining a Random Forest-based web system and blood tests. Journal of Biomolecular Structure and Dynamics, 1–20. https://doi.org/10.1080/07391102.2021.1966509
   Google Scholar
• Basu, D., Kashid, S., Pawar, S., & Datta, D. (2020). An integrated detection and treatment recommendation framework for breast cancer using convolutional neural networks and TOPSIS [Paper presentation]. 2020 IEEE 17th India Council International Conference, INDICON 2020, December 10. https://doi.org/10.1109/INDICON49873.2020.9342266
   Google Scholar
• Baumeister, H., Kraft, R., Baumel, A., Pryss, R., & Messner, E.-M. (2019). Persuasive E-health design for behavior change. In Digital phenotyping and mobile sensing (pp. 261–276). Springer. https://doi.org/10.1007/978-3-030-31620-4_17
   Google Scholar
• Ben-Israel, D., Jacobs, W. B., Casha, S., Lang, S., Ryu, W. H. A., de Lotbiniere-Bassett, M., & Cadotte, D. W. (2020). The impact of machine learning on patient care: A systematic review. Artificial Intelligence in Medicine, 103, 101785. https://doi.org/10.1016/j.artmed.2019.101785
   PubMed Web of Science ®Google Scholar
• Bisong, E. (2019). Building machine learning and deep learning models on Google cloud platform: A comprehensive guide for beginners. Apress.
   Google Scholar
• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
   Web of Science ®Google Scholar
• Buettner, R., Klenk, F., & Ebert, M. (2020). A systematic literature review of machine learning-based disease profiling and personalized treatment [Paper presentation]. Proceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020 (pp. 1673–1678). https://doi.org/10.1109/COMPSAC48688.2020.00-15
   Google Scholar
• Burns, M. N., Begale, M., Duffecy, J., Gergle, D., Karr, C. J., Giangrande, E., & Mohr, D. C. (2011). Harnessing context sensing to develop a mobile intervention for depression. Journal of Medical Internet Research, 13(3), e55. https://doi.org/10.2196/jmir.1838
   PubMed Web of Science ®Google Scholar
• Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16–28. https://doi.org/10.1016/j.compeleceng.2013.11.024
   Web of Science ®Google Scholar
• Cheerla, N., & Gevaert, O. (2017). MicroRNA based pan-cancer diagnosis and treatment recommendation. BMC Bioinformatics, 18(1), 32. https://doi.org/10.1186/s12859-016-1421-y
   PubMedGoogle Scholar
• Chen, M., Yang, J., Zhou, J., Hao, Y., Zhang, J., & Youn, C.-H. (2018). 5G-smart diabetes: Toward personalized diabetes diagnosis with healthcare big data clouds. IEEE Communications Magazine, 56(4), 16–23. https://doi.org/10.1109/MCOM.2018.1700788
   Web of Science ®Google Scholar
• Chen, T., Zhang, X., Jiang, H., Asaeikheybari, G., Goel, N., Hooper, M. W., & Huang, M.-C. (2018). Are you smoking? Automatic alert system helping people keep away from cigarettes. Smart Health, 9–10, 158–169. https://doi.org/10.1016/j.smhl.2018.07.008
   Google Scholar
• Chiang, P., & Dey, S. (2019). Offline and online learning techniques for personalized blood pressure prediction and health behavior recommendations. IEEE Access, 7, 130854–130864. https://doi.org/10.1109/ACCESS.2019.2939218
   Web of Science ®Google Scholar
• Chiang, P.-H., Wong, M., & Dey, S. (2021). Using wearables and machine learning to enable personalized lifestyle recommendations to improve blood pressure. IEEE Journal of Translational Engineering in Health and Medicine, 9, 2700513. https://doi.org/10.1109/JTEHM.2021.3098173
   PubMedGoogle Scholar
• Chin, W. H., Nuo Wi Tay, N., Kubota, N., & Loo, C. K. (2020). A lightweight neural-net with assistive mobile robot for human fall detection system [Paper presentation]. Proceedings of the International Joint Conference on Neural Networks (pp. 1–6). https://doi.org/10.1109/IJCNN48605.2020.9206637
   Google Scholar
• Cho, G., Yim, J., Choi, Y., Ko, J., & Lee, S. H. (2019). Review of machine learning algorithms for diagnosing mental illness. Psychiatry Investigation, 16(4), 262–269. https://doi.org/10.30773/pi.2018.12.21.2
   PubMed Web of Science ®Google Scholar
• Copeland, M., Soh, J., Puca, A., Manning, M., & Gollob, D. (2015). Microsoft azure. Apress.
   Google Scholar
• Coutinho, E., Alshukri, A., de Berardinis, J., & Dowrick, C. (2021). POLYHYMNIA mood-empowering people to cope with depression through music listening [Paper presentation]. UbiComp/ISWC 2021 - Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers (pp. 188–193). https://doi.org/10.1145/3460418.3479334
   Google Scholar
• da Silva, V. J., da Silva Souza, V., Guimarães da Cruz, R., Mesquita Vidal Martinez de Lucena, J., Jazdi, N., & Ferreira de Lucena Junior, V. (2019). Commercial devices-based system designed to improve the treatment adherence of hypertensive patients. Sensors, 19(20), 4539. https://doi.org/10.3390/s19204539
   PubMed Web of Science ®Google Scholar
• Delmastro, F., Martino, F. D., & Dolciotti, C. (2020). Cognitive training and stress detection in MCI frail older people through wearable sensors and machine learning. IEEE Access, 8, 65573–65590. https://doi.org/10.1109/ACCESS.2020.2985301
   Web of Science ®Google Scholar
• Dijkhuis, T. B., Blaauw, F. J., van Ittersum, M. W., Velthuijsen, H., & Aiello, M. (2018). Personalized physical activity coaching: A machine learning approach. Sensors, 18(2), 623. https://doi.org/10.3390/s18020623
   PubMed Web of Science ®Google Scholar
• D'mello, S. K., & Kory, J. (2015). A review and meta-analysis of multimodal affect detection systems. ACM Computing Surveys, 47(3), 1–36. https://doi.org/10.1145/2682899
   Web of Science ®Google Scholar
• Edara, D. C., Sistla, V., & Kolli, V. K. K. (2020). Health app recommendation system using ensemble multimodel deep learning. Journal of Engineering Science and Technology Review, 13(5), 7–19. https://doi.org/10.25103/jestr.135.03
   Google Scholar
• El Barachi, M., Oroumchian, F., Rauf, R., Khan, U., Al Hassooni, B., Al Basosi, A., & Kazia, S. (2019). EpiSense: Towards a smart solution for epileptic patients’ care [Paper presentation]. 2019 4th International Conference on Smart and Sustainable Technologies (SpliTech) (pp. 1–6). https://doi.org/10.23919/SpliTech.2019.8783034
   Google Scholar
• Elvitigala, D. S., Scholl, P. M., Suriyaarachchi, H., Dissanayake, V., & Nanayakkara, S. (2021). StressShoe: A DIY toolkit for just-in-time personalised stress interventions for office workers performing sedentary tasks [Paper presentation]. Proceedings of the 23rd International Conference on Mobile Human-Computer Interaction. https://doi.org/10.1145/3447526.3472023
   Google Scholar
• Forman, E. M., Goldstein, S. P., Crochiere, R. J., Butryn, M. L., Juarascio, A. S., Zhang, F., & Foster, G. D. (2019). Randomized controlled trial of OnTrack, a just-in-time adaptive intervention designed to enhance weight loss. Translational Behavioral Medicine, 9(6), 989–1001. https://doi.org/10.1093/tbm/ibz137
   PubMed Web of Science ®Google Scholar
• Forman, E. M., Goldstein, S. P., Zhang, F., Evans, B. C., Manasse, S. M., Butryn, M. L., Juarascio, A. S., Abichandani, P., Martin, G. J., & Foster, G. D. (2019). OnTrack: Development and feasibility of a smartphone app designed to predict and prevent dietary lapses. Translational Behavioral Medicine, 9(2), 236–245. https://doi.org/10.1093/tbm/iby016
   PubMed Web of Science ®Google Scholar
• Ganju, A., Satyan, S., Tanna, V., & Menezes, S. R. (2021). AI for improving children’s health: A community case study. Frontiers in Artificial Intelligence, 3, 544972. https://doi.org/10.3389/frai.2020.544972
   PubMedGoogle Scholar
• Ghandeharioun, A., McDuff, D., Czerwinski, M., & Rowan, K. (2019). EMMA: An emotion-aware wellbeing chatbot [Paper presentation]. 2019 8th International Conference on Affective Computing and Intelligent Interaction, ACII 2019 (pp. 15–21). https://doi.org/10.1109/ACII.2019.8925455
   Google Scholar
• Google Inc. (n.d.-a). ML Kit | Google developers. Retrieved October 31, 2021, from https://developers.google.com/ml-kit
   Google Scholar
• Google Inc. (n.d.-b). TensorFlow Lite | ML for mobile and edge devices. Retrieved October 31, 2021, from https://www.tensorflow.org/lite/
   Google Scholar
• Gorbonos, E., Liu, Y., & Hoang, C. T. (2018). NutRec: Nutrition oriented online recipe recommender [Paper presentation]. 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2018) (pp. 25–32). https://doi.org/10.1109/WI.2018.0-111
   Google Scholar
• Gu, W. (2017). PhD forum abstract: Non-intrusive blood glucose monitor by multi-task deep learning [Paper presentation]. Proceedings - 2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2017 (pp. 249–250). https://doi.org/10.1145/3055031.3055049
   Google Scholar
• Gu, Y., Yang, M., Shi, Z., & Wu, Z. (2015). The applications of decision-level data fusion techniques in the field of multiuser detection for DS-UWB systems. Sensors, 15(10), 24771–24790. https://doi.org/10.3390/s151024771
   PubMed Web of Science ®Google Scholar
• Gupta, A., & Katarya, R. (2020). Social media based surveillance systems for healthcare using machine learning: A systematic review. Journal of Biomedical Informatics, 108, 103500. https://doi.org/10.1016/j.jbi.2020.103500
   PubMed Web of Science ®Google Scholar
• Hassan, M. M., Gumaei, A., Aloi, G., Fortino, G., & Zhou, M. (2019). A smartphone-enabled fall detection framework for elderly people in connected home healthcare. IEEE Network., 33(6), 58–63. https://doi.org/10.1109/MNET.001.1900100
   Web of Science ®Google Scholar
• Hermens, H., Op den Akker, H., Tabak, M., Wijsman, J., & Vollenbroek, M. (2014). Personalized coaching systems to support healthy behavior in people with chronic conditions. Journal of Electromyography and Kinesiology, 24(6), 815–826. https://doi.org/10.1016/j.jelekin.2014.10.003
   PubMed Web of Science ®Google Scholar
• Holt, J. M., Cusatis, R., Asan, O., Williams, J., Nukuna, S., Flynn, K. E., Moore, J., & Crotty, B. H. (2020). Incorporating patient-generated contextual data into care: Clinician perspectives using the consolidated framework for implementation science. Healthcare, 8(1), 100369. https://doi.org/10.1016/j.hjdsi.2019.100369
   Web of Science ®Google Scholar
• Hu, Y., Wen, G., Liao, H., Wang, C., Dai, D., & Yu, Z. (2021). Automatic construction of chinese herbal prescriptions from tongue images using CNNs and auxiliary latent therapy topics. IEEE Transactions on Cybernetics, 51(2), 708–721. https://doi.org/10.1109/TCYB.2019.2909925
   PubMed Web of Science ®Google Scholar
• Hung, C. Y., Lin, C. H., Chang, C. S., Li, J. L., & Lee, C. C. (2019). Predicting gastrointestinal bleeding events from multimodal in-hospital electronic health records using deep fusion networks [Paper presentation]. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (pp. 2447–2450). https://doi.org/10.1109/EMBC.2019.8857244
   Google Scholar
• IBM. (n.d.). IBM Cloud. Retrieved October 31, 2021, from https://www.ibm.com/cloud
   Google Scholar
• Jamil, F., Kahng, H. K., Kim, S., & Kim, D.-H. (2021). Towards secure fitness framework based on IoT-enabled blockchain network integrated with machine learning algorithms. Sensors, 21(5), 1640. https://doi.org/10.3390/s21051640
   PubMed Web of Science ®Google Scholar
• Javed, A. R., Sarwar, M. U., Ur Rehman, S., Khan, H. U., Al-Otaibi, Y. D., & Alnumay, W. S. (2021). PP-SPA: Privacy preserved smartphone-based personal assistant to improve routine life functioning of cognitive impaired individuals. Neural Processing Letters, 1–18. https://doi.org/10.1007/s11063-020-10414-5
   Google Scholar
• Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101
   PubMedGoogle Scholar
• Jović, A., Brkić, K., & Bogunović, N. (2015). A review of feature selection methods with applications [Paper presentation]. 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2015 - Proceedings (pp. 1200–1205). https://doi.org/10.1109/MIPRO.2015.7160458
   Google Scholar
• Kadri, N., Ellouze, A., & Ksantini, M. (2020). Recommendation system for human physical activities using smartphones [Paper presentation]. 2020 2nd International Conference on Computer and Information Sciences, ICCIS 2020. https://doi.org/10.1109/ICCIS49240.2020.9257671
   Google Scholar
• Kajiwara, Y., & Kimura, H. (2019). Object identification and safe route recommendation based on human flow for the visually impaired. Sensors, 19(24), 5343. https://doi.org/10.3390/s19245343
   PubMed Web of Science ®Google Scholar
• Kang, J. J. (2021). A military human performance management system design using machine learning algorithms [Paper presentation]. 2021 31st International Telecommunication Networks and Applications Conference (ITNAC) (pp. 13–18). https://doi.org/10.1109/ITNAC53136.2021.9652140
   Google Scholar
• Kariyawasam, R., Nadeeshani, M., Hamid, T., Subasinghe, I., Samarasinghe, P., & Ratnayake, P. (2019). Pubudu: Deep learning based screening and intervention of dyslexia, dysgraphia and dyscalculia [Paper presentation]. 2019 IEEE 14th International Conference on Industrial and Information Systems: Engineering for Innovations for Industry 4.0, ICIIS 2019 - Proceedings (pp. 476–481). https://doi.org/10.1109/ICIIS47346.2019.9063301
   Google Scholar
• Kesavan, R., & Arumugam, S. (2020). Adaptive deep convolutional neural network-based secure integration of fog to cloud supported Internet of Things for health monitoring system. Transactions on Emerging Telecommunications Technologies, 31(10), e4104. https://doi.org/10.1002/ett.4104
   Web of Science ®Google Scholar
• Khalaf, M., Hussain, A. J., Al-Jumeily, D., Keenan, R., Keight, R., Fergus, P., & Idowu, I. O. (2016). Applied difference techniques of machine learning algorithm and web-based management system for sickle cell disease [Paper presentation]. Proceedings - 2015 International Conference on Developments in eSystems Engineering, DeSE 2015 (pp. 231–235). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/DeSE.2015.18
   Google Scholar
• Khumrin, P., Ryan, A., Juddy, T., Verspoor, K. (2018). DrKnow: A diagnostic learning tool with feedback from automated clinical decision support. AMIA Annual Symposium Proceedings. AMIA Symposium, 2018, 1348–1357.
   PubMedGoogle Scholar
• Koren, G., Souroujon, D., Shaul, R., Bloch, A., Leventhal, A., Lockett, J., & Shalev, V. (2019). “A patient like me” – An algorithm-based program to inform patients on the likely conditions people with symptoms like theirs have. Medicine, 98(42), e17596. https://doi.org/10.1097/MD.0000000000017596
   PubMed Web of Science ®Google Scholar
• Krishnan, S. P. T., & Gonzalez, J. L. U. (2015). Building your next big thing with Google Cloud Platform. Apress. https://doi.org/10.1007/978-1-4842-1004-8
   Google Scholar
• Lahat, D., Adali, T., & Jutten, C. (2015). Multimodal data fusion: An overview of methods, challenges, and prospects. Proceedings of the IEEE, 103(9), 1449–1477. https://doi.org/10.1109/JPROC.2015.2460697
   Web of Science ®Google Scholar
• Le Glaz, A., Haralambous, Y., Kim-Dufor, D. H., Lenca, P., Billot, R., Ryan, T. C., Marsh, J., DeVylder, J., Walter, M., Berrouiguet, S., & Lemey, C. (2021). Machine learning and natural language processing in mental health: Systematic review. Journal of Medical Internet Research, 23(5), e15708. https://doi.org/10.2196/15708
   PubMed Web of Science ®Google Scholar
• Lee, H., Kang, J., & Yeo, J. (2021). Medical specialty recommendations by an artificial intelligence chatbot on a smartphone: Development and deployment. Journal of Medical Internet Research, 23(5), e27460. https://doi.org/10.2196/27460
   PubMed Web of Science ®Google Scholar
• Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60. https://doi.org/10.1109/MSP.2020.2975749
   Web of Science ®Google Scholar
• Li, Z., Das, S., Codella, J., Hao, T., Lin, K., Maduri, C., & Chen, C.-H. (2019). An adaptive, data-driven personalized advisor for increasing physical activity. IEEE Journal of Biomedical and Health Informatics, 23(3), 999–1010. https://doi.org/10.1109/JBHI.2018.2879805
   PubMed Web of Science ®Google Scholar
• Liang, Z., Zhang, G., Huang, J. X., & Hu, Q. V. (2014). Deep learning for healthcare decision making with EMRs [Paper presentation]. Proceedings - 2014 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2014 (pp. 556–559). https://doi.org/10.1109/BIBM.2014.6999219
   Google Scholar
• Liao, W. X., He, P., Hao, J., Wang, X. Y., Yang, R. L., An, D., & Cui, L. G. (2020). Automatic identification of breast ultrasound image based on supervised block-based region segmentation algorithm and features combination migration deep learning model. IEEE Journal of Biomedical and Health Informatics, 24(4), 984–993. https://doi.org/10.1109/JBHI.2019.2960821
   PubMed Web of Science ®Google Scholar
• Liu, B. (2011). Supervised learning. In Web data mining (pp. 63–132). Springer. https://doi.org/10.1007/978-3-642-19460-3_3
   Google Scholar
• Loftus, T. J., Filiberto, A. C., Li, Y., Balch, J., Cook, A. C., Tighe, P. J., Efron, P. A., Upchurch, G. R., Rashidi, P., Li, X., & Bihorac, A. (2020). Decision analysis and reinforcement learning in surgical decision-making. Surgery, 168(2), 253–266. https://doi.org/10.1016/j.surg.2020.04.049
   PubMed Web of Science ®Google Scholar
• Lopez-Guede, J. M., Moreno-Fernandez-De-Leceta, A., Martinez-Garcia, A., & Graña, M. (2015). Lynx: Automatic elderly behavior prediction in home telecare. BioMed Research International, 2015, 201939. https://doi.org/10.1155/2015/201939
   PubMed Web of Science ®Google Scholar
• Loram, I., Siddique, A., Sanchez, M. B., Harding, P., Silverdale, M., Kobylecki, C., & Cunningham, R. (2020). Objective analysis of neck muscle boundaries for cervical dystonia using ultrasound imaging and deep learning. IEEE Journal of Biomedical and Health Informatics, 24(4), 1016–1027. https://doi.org/10.1109/JBHI.2020.2964098
   PubMed Web of Science ®Google Scholar
• Luštrek, M., Bohanec, M., Cavero Barca, C., Ciancarelli, M. C., Clays, E., Dawodu, A. A., Derboven, J., De Smedt, D., Dovgan, E., Lampe, J., Marino, F., Mlakar, M., Pioggia, G., Puddu, P. E., Rodríguez, J. M., Schiariti, M., Slapničar, G., Slegers, K., Tartarisco, G., Valič, J., & Vodopija, A. (2021). A personal health system for self-management of congestive heart failure (HeartMan): Development, technical evaluation, and proof-of-concept randomized controlled trial. JMIR Medical Informatics, 9(3), e24501. https://doi.org/10.2196/24501
   PubMed Web of Science ®Google Scholar
• Mahyari, A., & Pirolli, P. (2021). Physical exercise recommendation and success prediction using interconnected recurrent neural networks [Paper presentation]. 2021 IEEE International Conference on Digital Health (ICDH) (pp. 148–153). https://doi.org/10.1109/ICDH52753.2021.00027
   Google Scholar
• Martín, P., Sánchez, M., Álvarez, L., Alonso, V., & Bajo, J. (2011). Multi-agent system for detecting elderly people falls through mobile devices. Advances in Intelligent and Soft Computing, 92, 93–99. https://doi.org/10.1007/978-3-642-19937-0_12
   Google Scholar
• Martin-Isla, C., Campello, V. M., Izquierdo, C., Raisi-Estabragh, Z., Baeßler, B., Petersen, S. E., & Lekadir, K. (2020). Image-based cardiac diagnosis with machine learning: A review. Frontiers in Cardiovascular Medicine, 7(1), 1. https://doi.org/10.3389/fcvm.2020.00001
   PubMedGoogle Scholar
• Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: Review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236–1246. https://doi.org/10.1093/bib/bbx044
   PubMed Web of Science ®Google Scholar
• Mishra, V., Künzler, F., Kramer, J.-N., Fleisch, E., Kowatsch, T., & Kotz, D. (2021). Detecting receptivity for mHealth interventions in the natural environment. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5(2), 1–24. https://doi.org/10.1145/3463492
   Google Scholar
• Mlodzinski, E., Stone, D. J., & Celi, L. A. (2020). Machine learning for pulmonary and critical care medicine: A narrative review. Pulmonary Therapy, 6(1), 67–77. https://doi.org/10.1007/s41030-020-00110-z
   PubMedGoogle Scholar
• Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Medicine, 6(7), e1000097. https://doi.org/10.1371/journal.pmed.1000097
   PubMed Web of Science ®Google Scholar
• Mrozek, D., Koczur, A., & Małysiak-Mrozek, B. (2020). Fall detection in older adults with mobile IoT devices and machine learning in the cloud and on the edge. Information Sciences, 537, 132–147. https://doi.org/10.1016/j.ins.2020.05.070
   Web of Science ®Google Scholar
• Murnane, E. L., Huffaker, D., & Kossinets, G. (2015). Mobile health apps: Adoption, adherence, and abandonment [Paper presentation]. UbiComp and ISWC 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the Proceedings of the 2015 ACM International Symposium on Wearable Computers (pp. 261–264). https://doi.org/10.1145/2800835.2800943
   Google Scholar
• National Library of Medicine. (n.d.). PubMed. Retrieved October 31, 2021, from https://pubmed.ncbi.nlm.nih.gov/
   Google Scholar
• Neloy, A. A., Alam, S., Bindu, R. A., & Moni, N. J. (2019). Machine learning based health prediction system using IBM cloud as PaaS [Paper presentation]. Proceedings of the International Conference on Trends in Electronics and Informatics, ICOEI 2019 (pp. 444–450). https://doi.org/10.1109/ICOEI.2019.8862754
   Google Scholar
• Nosakhare, E., & Picard, R. (2020). Toward assessing and recommending combinations of behaviors for improving health and well-being. ACM Transactions on Computing for Healthcare, 1(1), 1–29. https://doi.org/10.1145/3368958
   Google Scholar
• Paredes, P., Gilad-Bachrach, R., Czerwinski, M., Roseway, A., Rowan, K., & Hernandez, J. (2014). PopTherapy: Coping with stress through pop-culture [Paper presentation]. Proceedings - PERVASIVEHEALTH 2014: 8th International Conference on Pervasive Computing Technologies for Healthcare (pp. 109–117). https://doi.org/10.4108/icst.pervasivehealth.2014.255070
   Google Scholar
• Pathinarupothi, R. K., Durga, P., & Rangan, E. S. (2018). Data to diagnosis in global health: A 3P approach. BMC Medical Informatics and Decision Making, 18(1), 78. https://doi.org/10.1186/s12911-018-0658-y
   PubMedGoogle Scholar
• Pelle, T., Bevers, K., van der Palen, J., van den Hoogen, F. H. J., & van den Ende, C. H. M. (2019). Development and evaluation of a tailored e-self-management intervention (dr. Bart app) for knee and/or hip osteoarthritis: Study protocol. BMC Musculoskeletal Disorders, 20(1), 398. https://doi.org/10.1186/s12891-019-2768-9
   PubMed Web of Science ®Google Scholar
• Popp, C. J., St-Jules, D. E., Hu, L., Ganguzza, L., Illiano, P., Curran, M., Li, H., Schoenthaler, A., Bergman, M., Schmidt, A. M., Segal, E., Godneva, A., & Sevick, M. A. (2019). The rationale and design of the personal diet study, a randomized clinical trial evaluating a personalized approach to weight loss in individuals with pre-diabetes and early-stage type 2 diabetes. Contemporary Clinical Trials, 79, 80–88. https://doi.org/10.1016/j.cct.2019.03.001
   PubMed Web of Science ®Google Scholar
• Prabhu, G., Kuklyte, J., Gualano, L., Venkataraman, K., Ahmadi, A., Duff, O., Walsh, D., Woods, C., O’Connor, N. E., & Moran, K. (2018). Design and development of the medFit app: A mobile application for cardiovascular disease rehabilitation. In International Conference on Wireless Mobile Communication and Healthcare (MobiHealth) (pp. 20–28). Springer. https://doi.org/10.1007/978-3-319-98551-0_3
   Google Scholar
• Priyadarshi, A., & Saha, S. K. (2020). Web information extraction for finding remedy based on a patient-authored text: A study on homeopathy. Network Modeling Analysis in Health Informatics and Bioinformatics, 9(1), 9. https://doi.org/10.1007/s13721-019-0216-2
   Web of Science ®Google Scholar
• Qian, K., Janott, C., Schmitt, M., Zhang, Z., Heiser, C., Hemmert, W., Yamamoto, Y., & Schuller, B. W. (2021). Can machine learning assist locating the excitation of snore sound? A review. IEEE Journal of Biomedical and Health Informatics, 25(4), 1233–1246. https://doi.org/10.1109/JBHI.2020.3012666
   PubMed Web of Science ®Google Scholar
• Rabbi, M., Aung, M. H., Zhang, M., & Choudhury, T. (2015). MyBehavior: Automatic personalized health feedback from user behaviors and preferences using smartphones [Paper presentation]. UbiComp 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 707–718). https://doi.org/10.1145/2750858.2805840
   Google Scholar
• Rachakonda, L., Mohanty, S. P., & Kougianos, E. (2020). ILog: An intelligent device for automatic food intake monitoring and stress detection in the IoMT. IEEE Transactions on Consumer Electronics, 66(2), 115–124. https://doi.org/10.1109/TCE.2020.2976006
   Web of Science ®Google Scholar
• Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48(1), 137–141. https://doi.org/10.1007/s11747-019-00710-5
   Web of Science ®Google Scholar
• Rajasekaran, S., & Kousalya, G. (2022). Virtual nursing using deep belief networks for elderly people (DBN-EP). Computer Systems Science and Engineering, 42(3), 985–1000. https://doi.org/10.32604/csse.2022.022234
   Web of Science ®Google Scholar
• Rasley, J., Rajbhandari, S., Ruwase, O., & He, Y. (2020). DeepSpeed: System optimizations enable training deep learning models with over 100 billion parameters [Paper presentation]. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 3505–3506). https://doi.org/10.1145/3394486.3406703
   Google Scholar
• Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., Ourselin, S., Sheller, M., Summers, R. M., Trask, A., Xu, D., Baust, M., & Cardoso, M. J. (2020). The future of digital health with federated learning. NPJ Digital Medicine, 3(1), 1–7. https://doi.org/10.1038/s41746-020-00323-1
   PubMed Web of Science ®Google Scholar
• Salemink, K., Strijker, D., & Bosworth, G. (2017). Rural development in the digital age: A systematic literature review on unequal ICT availability, adoption, and use in rural areas. Journal of Rural Studies, 54, 360–371. https://doi.org/10.1016/j.jrurstud.2015.09.001
   Web of Science ®Google Scholar
• Sansrimahachai, W. (2020a). Personalized walking exercise support system for elderly based on machine learning [Paper presentation]. JCSSE 2020 - 17th International Joint Conference on Computer Science and Software Engineering (pp. 6–11). https://doi.org/10.1109/JCSSE49651.2020.9268327
   Google Scholar
• Sansrimahachai, W. (2020b). Personalized walking exercise support system for elderly based on machine learning [Paper presentation]. JCSSE 2020 - 17th International Joint Conference on Computer Science and Software Engineering (pp. 6–11). https://doi.org/10.1109/JCSSE49651.2020.9268327
   Google Scholar
• Sarwar, M. U., & Javed, A. R. (2019). Collaborative health care plan through crowdsource data using ambient application [Paper presentation]. Proceedings - 22nd International Multitopic Conference, INMIC 2019. https://doi.org/10.1109/INMIC48123.2019.9022684
   Google Scholar
• Scherzer, C. R., Ranney, M. L., Jain, S., Bommaraju, S. P., Patena, J., Langdon, K., Nimaja, E., Jennings, E., & Beaudoin, F. L. (2020). Mobile peer-support for opioid use disorders: Refinement of an innovative machine learning tool. Journal of Psychiatry and Brain Science, 5(1), e200001. https://doi.org/10.20900/jpbs.20200001
   PubMedGoogle Scholar
• Shatte, A. B. R., Hutchinson, D. M., & Teague, S. J. (2019). Machine learning in mental health: A scoping review of methods and applications. Psychological Medicine, 49(9), 1426–1448. https://doi.org/10.1017/S0033291719000151
   PubMed Web of Science ®Google Scholar
• Shi, J., Zheng, X., Li, Y., Zhang, Q., & Ying, S. (2018). Multimodal neuroimaging feature learning with multimodal stacked deep polynomial networks for diagnosis of Alzheimer's disease. IEEE Journal of Biomedical and Health Informatics, 22(1), 173–183. https://doi.org/10.1109/JBHI.2017.2655720
   PubMed Web of Science ®Google Scholar
• Shin, D. (2021). The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI. International Journal of Human-Computer Studies, 146, 102551. https://doi.org/10.1016/j.ijhcs.2020.102551
   Web of Science ®Google Scholar
• Sookrah, R., Dhowtal, J. D., & Nagowah, S. D. (2019). A DASH diet recommendation system for hypertensive patients using machine learning [Paper presentation]. 2019 7th International Conference on Information and Communication Technology, ICoICT 2019 (pp. 1–6). https://doi.org/10.1109/ICoICT.2019.8835323
   Google Scholar
• Sowah, R. A., Bampoe-Addo, A. A., Armoo, S. K., Saalia, F. K., Gatsi, F., & Sarkodie-Mensah, B. (2020). Design and development of diabetes management system using machine learning. International Journal of Telemedicine and Applications, 2020, 8870141. https://doi.org/10.1155/2020/8870141
   PubMed Web of Science ®Google Scholar
• Spanakis, G., Weiss, G., Boh, B., Lemmens, L., & Roefs, A. (2017). Machine learning techniques in eating behavior e-coaching: Balancing between generalization and personalization. Personal and Ubiquitous Computing, 21(4), 645–659. https://doi.org/10.1007/s00779-017-1022-4
   Google Scholar
• Stafford, I. S., Kellermann, M., Mossotto, E., Beattie, R. M., MacArthur, B. D., & Ennis, S. (2020). A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases. NPJ Digital Medicine, 3(1), 1–11. https://doi.org/10.1038/s41746-020-0229-3
   PubMed Web of Science ®Google Scholar
• Stamate, C., Magoulas, G. D., Kueppers, S., Nomikou, E., Daskalopoulos, I., Jha, A., Pons, J. S., Rothwell, J., Luchini, M. U., Moussouri, T., Iannone, M., & Roussos, G. (2018). The cloudUPDRS app: A medical device for the clinical assessment of Parkinson’s disease. Pervasive and Mobile Computing, 43, 146–166. https://doi.org/10.1016/j.pmcj.2017.12.005
   Web of Science ®Google Scholar
• Stamate, C., Magoulas, G. D., Kueppers, S., Nomikou, E., Daskalopoulos, I., Luchini, M. U., Moussouri, T., & Roussos, G. (2017). Deep learning Parkinson’s from smartphone data [Paper presentation]. 2017 IEEE International Conference on Pervasive Computing and Communications, PerCom 2017 (pp. 31–40). https://doi.org/10.1109/PERCOM.2017.7917848
   Google Scholar
• Stark, B., & Samarah, M. (2019). Mac7: Adaptive Smart Toothbrush [Paper presentation]. Proceedings - 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control, SDPC 2018 (pp. 153–158). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/SDPC.2018.8664828
   Google Scholar
• Suh, M., Nahapetian, A., Woodbridge, J., Rofouei, M., & Sarrafzadeh, M. (2012). Machine learning-based adaptive wireless interval training guidance system. Mobile Networks and Applications, 17(2), 163–177. https://doi.org/10.1007/s11036-011-0331-5
   Web of Science ®Google Scholar
• Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.
   Google Scholar
• Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A. (2014). Going deeper with convolutions [Paper presentation]. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June-2015 (pp. 1–9).
   Google Scholar
• Thieme, A., Belgrave, D., & Doherty, G. (2020). Machine learning in mental health: A systematic review of the HCI literature to support the development of effective and implementable ML systems. ACM Transactions on Computer-Human Interaction, 27(5), 1–53. https://doi.org/10.1145/3398069
   Web of Science ®Google Scholar
• Thomsen, K., Iversen, L., Titlestad, T. L., & Winther, O. (2020). Systematic review of machine learning for diagnosis and prognosis in dermatology. Journal of Dermatological Treatment, 31(5), 496–510. https://doi.org/10.1080/09546634.2019.1682500
   PubMed Web of Science ®Google Scholar
• Thyde, D. N., Mohebbi, A., Bengtsson, H., Jensen, M. L., & Mørup, M. (2021). Machine learning-based adherence detection of type 2 diabetes patients on once-daily basal insulin injections. Journal of Diabetes Science and Technology, 15(1), 98–108. https://doi.org/10.1177/1932296820912411
   PubMedGoogle Scholar
• Tolkachev, A., Sirazitdinov, I., Kholiavchenko, M., Mustafaev, T., & Ibragimov, B. (2021). Deep learning for diagnosis and segmentation of pneumothorax: The results on the Kaggle competition and validation against radiologists. IEEE Journal of Biomedical and Health Informatics, 25(5), 1660–1672. https://doi.org/10.1109/JBHI.2020.3023476
   PubMed Web of Science ®Google Scholar
• Triantafyllidis, A. K., & Tsanas, A. (2019). Applications of machine learning in real-life digital health interventions: Review of the literature. Journal of Medical Internet Research, 21(4), e12286. https://doi.org/10.2196/12286
   PubMed Web of Science ®Google Scholar
• Tuti, T., Paton, C., & Winters, N. (2020). Learning to represent healthcare providers knowledge of neonatal emergency care: Findings from a smartphone-based learning intervention targeting clinicians from LMICs [Paper presentation]. ACM International Conference Proceeding Series (pp. 320–329). https://doi.org/10.1145/3375462.3375479
   Google Scholar
• Van Woensel, W., Roy, P. C., Abidi, S. S. R., & Abidi, S. R. (2020). Indoor location identification of patients for directing virtual care: An AI approach using machine learning and knowledge-based methods. Artificial Intelligence in Medicine, 108, 101931. https://doi.org/10.1016/j.artmed.2020.101931
   PubMed Web of Science ®Google Scholar
• Venne, J., Busshoff, U., Poschadel, S., Menschel, R., Evangelatos, N., Vysyaraju, K., & Brand, A. (2020). International consortium for personalized medicine: An international survey about the future of personalized medicine. Personalized Medicine, 17(2), 89–100. https://doi.org/10.2217/pme-2019-0093
   PubMed Web of Science ®Google Scholar
• Vicente, A. M., Ballensiefen, W., & Jönsson, J. I. (2020). How personalised medicine will transform healthcare by 2030: The ICPerMed vision. Journal of Translational Medicine, 18(1), 180. https://doi.org/10.1186/s12967-020-02316-w
   PubMed Web of Science ®Google Scholar
• Visalakshi, S., & Radha, V. (2015). A literature review of feature selection techniques and applications: Review of feature selection in data mining [Paper presentation]. 2014 IEEE International Conference on Computational Intelligence and Computing Research, IEEE ICCIC 2014. https://doi.org/10.1109/ICCIC.2014.7238499
   Google Scholar
• Wahle, F., Kowatsch, T., Fleisch, E., Rufer, M., & Weidt, S. (2016). Mobile sensing and support for people with depression: A pilot trial in the wild. JMIR mHealth and uHealth, 4(3), e111. https://doi.org/10.2196/mhealth.5960
   PubMed Web of Science ®Google Scholar
• Wang, L., Fan, R., Zhang, C., Hong, L., Zhang, T., Chen, Y., Liu, K., Wang, Z., & Zhong, J. (2020). Applying machine learning models to predict medication nonadherence in Crohn’s disease maintenance therapy. Patient Preference and Adherence, 14, 917–926. https://doi.org/10.2147/PPA.S253732
   PubMed Web of Science ®Google Scholar
• Wang, S., Sporrel, K., van Hoof, H., Simons, M., de Boer, R. D. D., Ettema, D., Nibbeling, N., Deutekom, M., & Kröse, B. (2021). Reinforcement learning to send reminders at right moments in smartphone exercise application: A feasibility study. International Journal of Environmental Research and Public Health, 18(11), 6059. https://doi.org/10.3390/ijerph18116059
   PubMed Web of Science ®Google Scholar
• Waring, J., Lindvall, C., & Umeton, R. (2020). Automated machine learning: Review of the state-of-the-art and opportunities for healthcare. Artificial Intelligence in Medicine, 104, 101822. https://doi.org/10.1016/j.artmed.2020.101822
   PubMed Web of Science ®Google Scholar
• Wittig, M., & Wittig, A. (2018). Amazon web services in action (2nd ed.). Simon and Schuster.
   Google Scholar
• Xu, P., Chen, D., Liu, X., & Loo, J. (2019). Image-based dietary assessment system for chinese children [Paper presentation]. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018 (pp. 5471–5473). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/BigData.2018.8622579
   Google Scholar
• Yang, J., Xiao, W., Lu, H., & Barnawi, A. (2020). Wireless high-frequency NLOS monitoring system for heart disease combined with hospital and home. Future Generation Computer Systems, 110, 772–780. https://doi.org/10.1016/j.future.2019.11.001
   Web of Science ®Google Scholar
• Yang, W., Chuah, M. C., & Du, L. (2020). M&M: Deep learning aided multi-facet mental health support tool for college students [Paper presentation]. Proceedings of Deep Learning for Wellbeing Applications Leveraging Mobile Devices and Edge Computing (pp. 10–15). https://doi.org/10.1145/3396868.3400898
   Google Scholar
• Yates, D., & Islam, M. Z. (2019). Readiness of smartphones for data collection and data mining with an example application in mental health. In Australasian Conference on Data Mining (AusDM) (pp. 235–246). Springer. https://doi.org/10.1007/978-981-15-1699-3_19
   Google Scholar
• Ying, Z., Cao, S., Xu, S., Liu, X., Lyu, L., Chen, C., & Wang, L. (2021). Privacy-preserving optimal insulin dosing decision. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 (pp. 2640–2644). https://doi.org/10.1109/icassp39728.2021.9414807
   Google Scholar
• Zeevi, D., Korem, T., Zmora, N., Israeli, D., Rothschild, D., Weinberger, A., Ben-Yacov, O., Lador, D., Avnit-Sagi, T., Lotan-Pompan, M., Suez, J., Mahdi, J. A., Matot, E., Malka, G., Kosower, N., Rein, M., Zilberman-Schapira, G., Dohnalová, L., Pevsner-Fischer, M., … Segal, E. (2015). Personalized nutrition by prediction of glycemic responses. Cell, 163(5), 1079–1094. https://doi.org/10.1016/j.cell.2015.11.001
   PubMed Web of Science ®Google Scholar
• Zeng, J. (2020). Developing a machine learning tool for dynamic cancer treatment strategies. Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13742–13743. https://doi.org/10.1609/aaai.v34i10.7143
   Google Scholar
• Zhang, J. (2010). Multi-source remote sensing data fusion: Status and trends. International Journal of Image and Data Fusion, 1(1), 5–24. https://doi.org/10.1080/19479830903561035
   Google Scholar
• Zhang, Y., Chen, K., Weng, Y., Chen, Z., Zhang, J., & Hubbard, R. (2022). An intelligent early warning system of analyzing Twitter data using machine learning on COVID-19 surveillance in the US. Expert Systems with Applications, 198, 116882. https://doi.org/10.1016/j.eswa.2022.116882
   PubMed Web of Science ®Google Scholar
• Zhou, X., Li, Y., & Liang, W. (2020). CNN-RNN based intelligent recommendation for online medical pre-diagnosis support. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(3), 912–921. https://doi.org/10.1109/TCBB.2020.2994780
   Web of Science ®Google Scholar
• Zhu, T., Li, K., Herrero, P., & Georgiou, P. (2021). Deep learning for diabetes: A systematic review. IEEE Journal of Biomedical and Health Informatics, 25(7), 2744–2757. https://doi.org/10.1109/JBHI.2020.3040225
   PubMed Web of Science ®Google Scholar
• Zingg, A., Singh, T., & Myneni, S. (2021). Analysis of online peripartum depression communities: Application of multilabel text classification techniques to inform digitally-mediated prevention and management. Front Digit Health, 3, 653769. https://doi.org/10.3389/fdgth.2021.653769
   PubMedGoogle Scholar