{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T19:57:56Z","timestamp":1780430276084,"version":"3.54.1"},"reference-count":54,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,14]],"date-time":"2023-01-14T00:00:00Z","timestamp":1673654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The concept of the Internet of Medical Things brings a promising option to utilize various electronic health records stored in different medical devices and servers to create practical but secure clinical decision support systems. To achieve such a system, we need to focus on several aspects, most notably the usability aspect of deploying it using low-end devices. This study introduces one such application, namely FedSepsis, for the early detection of sepsis using electronic health records. We incorporate several cutting-edge deep learning techniques for the prediction and natural-language processing tasks. We also explore the multimodality aspect for the better use of electronic health records. A secure distributed machine learning mechanism is essential to building such a practical internet of medical things application. To address this, we analyze two federated learning techniques. Moreover, we use two different kinds of low-computational edge devices, namely Raspberry Pi and Jetson Nano, to address the challenges of using such a system in a practical setting and report the comparisons. We report several critical system-level information about the devices, namely CPU utilization, disk utilization, process CPU threads in use, process memory in use (non-swap), process memory available (non-swap), system memory utilization, temperature, and network traffic. We publish the prediction results with the evaluation metrics area under the receiver operating characteristic curve, the area under the precision\u2013recall curve, and the earliness to predict sepsis in hours. Our results show that the performance is satisfactory, and with a moderate amount of devices, the federated learning setting results are similar to the single server-centric setting. Multimodality provides the best results compared to any single modality in the input features obtained from the electronic health records. Generative adversarial neural networks provide a clear superiority in handling the sparsity of electronic health records. Multimodality with the generative adversarial neural networks provides the best result: the area under the precision\u2013recall curve is 96.55%, the area under the receiver operating characteristic curve is 99.35%, and earliness is 4.56 h. FedSepsis suggests that incorporating such a concept together with low-end computational devices could be beneficial for all the medical sector stakeholders and should be explored further.<\/jats:p>","DOI":"10.3390\/s23020970","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T05:30:07Z","timestamp":1673847007000},"page":"970","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1101-3793","authenticated-orcid":false,"given":"Mahbub Ul","family":"Alam","sequence":"first","affiliation":[{"name":"Department of Computer and Systems Sciences, Stockholm University, 16407 Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5924-5457","authenticated-orcid":false,"given":"Rahim","family":"Rahmani","sequence":"additional","affiliation":[{"name":"Department of Computer and Systems Sciences, Stockholm University, 16407 Stockholm, Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1136\/bmj.323.7313.625","article-title":"The challenge of complexity in health care","volume":"323","author":"Plsek","year":"2001","journal-title":"Bmj"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Irfan, M., and Ahmad, N. (2018, January 25\u201326). Internet of Medical Things: Architectural model, motivational factors and impediments. Proceedings of the 2018 15th Learning and Technology Conference (L&T), Jeddah, Saudi Arabia.","DOI":"10.1109\/LT.2018.8368495"},{"key":"ref_3","first-page":"240","article-title":"Internet of Medical Things (IoMT): Applications, benefits and future challenges in healthcare domain","volume":"12","author":"Joyia","year":"2017","journal-title":"J. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1007\/s00779-018-1178-6","article-title":"Emerging trends, issues, and challenges in Internet of Medical Things and wireless networks","volume":"22","author":"Manogaran","year":"2018","journal-title":"Pers. Ubiquitous Comput."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Goyal, S., Sharma, N., Bhushan, B., Shankar, A., and Sagayam, M. (2021). Iot enabled technology in secured healthcare: Applications, challenges and future directions. Cognitive Internet of Medical Things for Smart Healthcare, Springer International Publishing.","DOI":"10.1007\/978-3-030-55833-8_2"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kukhtevich, I., Goryunova, V., Goryunova, T., and Zhilyaev, P. (2020, January 27\u201329). Medical Decision Support Systems and Semantic Technologies in Healthcare. Proceedings of the Russian Conference on Digital Economy and Knowledge Management (RuDEcK 2020), Voronezh, Russia.","DOI":"10.2991\/aebmr.k.200730.068"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"106169","DOI":"10.1016\/j.cmpb.2021.106169","article-title":"Quality-in-use characteristics for clinical decision support system assessment","volume":"207","author":"Ouhbi","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1093\/jamia\/ocy068","article-title":"Opportunities and challenges in developing deep learning models using electronic health records data: A systematic review","volume":"25","author":"Xiao","year":"2018","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2093","DOI":"10.1109\/TMM.2017.2729400","article-title":"Retrieval From and Understanding of Large-Scale Multi-modal Medical Datasets: A Review","volume":"19","author":"Unay","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.eswa.2018.09.056","article-title":"Specifics of medical data mining for diagnosis aid: A survey","volume":"118","author":"Itani","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_11","unstructured":"Molnar, C. (2023, January 06). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. Available online: https:\/\/christophm.github.io\/interpretable-ml-book\/."},{"key":"ref_12","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"133583","DOI":"10.1109\/ACCESS.2019.2941419","article-title":"A survey on multimodal data-driven smart healthcare systems: Approaches and applications","volume":"7","author":"Cai","year":"2019","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41746-020-00341-z","article-title":"Fusion of medical imaging and electronic health records using deep learning: A systematic review and implementation guidelines","volume":"3","author":"Huang","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_15","first-page":"423","article-title":"Multimodal machine learning: A survey and taxonomy","volume":"41","author":"Ahuja","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1109\/MSP.2017.2738401","article-title":"Deep multimodal learning: A survey on recent advances and trends","volume":"34","author":"Ramachandram","year":"2017","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_17","unstructured":"Kairouz, P., McMahan, H.B., Avent, B., Bellet, A., Bennis, M., Bhagoji, A.N., Bonawitz, K., Charles, Z., Cormode, G., and Cummings, R. (2019). Advances and open problems in federated learning. arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Briggs, C., Fan, Z., and Andras, P. (2020). A Review of Privacy Preserving Federated Learning for Private IoT Analytics. arXiv.","DOI":"10.1007\/978-3-030-70604-3_2"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s41666-020-00082-4","article-title":"Federated learning for healthcare informatics","volume":"5","author":"Xu","year":"2021","journal-title":"J. Healthc. Inform. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1001\/jama.2016.0287","article-title":"The third international consensus definitions for sepsis and septic shock (Sepsis-3)","volume":"315","author":"Singer","year":"2016","journal-title":"JAMA"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1097\/CCM.0000000000000330","article-title":"Empiric antibiotic treatment reduces mortality in severe sepsis and septic shock from the first hour: Results from a guideline-based performance improvement program","volume":"42","author":"Ferrer","year":"2014","journal-title":"Crit. Care Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.1097\/01.CCM.0000217961.75225.E9","article-title":"Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock","volume":"34","author":"Kumar","year":"2006","journal-title":"Crit. Care Med."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Johnston, S.J., and Cox, S.J. (2017). The Raspberry Pi: A Technology Disrupter, and the Enabler of Dreams. Electronics, 6.","DOI":"10.3390\/electronics6030051"},{"key":"ref_24","first-page":"14","article-title":"Nvidia makes it easy to embed AI: The Jetson nano packs a lot of machine-learning power into DIY projects-[Hands on]","volume":"57","author":"Cass","year":"2020","journal-title":"IEEE Spectr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1109\/TNNLS.2020.2979670","article-title":"A survey of the usages of deep learning for natural language processing","volume":"32","author":"Otter","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ye, X., Soares, F., De Maria, E., G\u00f3mez Vilda, P., Cabitza, F., Fred, A., and Gamboa, H. (2020, January 24\u201326). Cognitive Internet of Medical Things Architecture for Decision Support Tool to Detect Early Sepsis Using Deep Learning. Proceedings of the Biomedical Engineering Systems and Technologies, BIOSTEC 2020, Valletta, Malta. Revised Selected Papers.","DOI":"10.1007\/978-3-030-72379-8"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Alam, M.U., and Rahmani, R. (2021). Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application. Sensors, 21.","DOI":"10.3390\/s21155025"},{"key":"ref_28","first-page":"50","article-title":"Federated Learning: Challenges, Methods, and Future Directions","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_29","first-page":"374","article-title":"Towards Federated Learning at Scale: System Design","volume":"1","author":"Bonawitz","year":"2019","journal-title":"Proc. Mach. Learn. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gao, Y., Kim, M., Abuadbba, S., Kim, Y., Thapa, C., Kim, K., Camtepe, S.A., Kim, H., and Nepal, S. (2020). End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things. arXiv.","DOI":"10.1109\/SRDS51746.2020.00017"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Orescanin, M., Ergezer, M., Singh, G., and Baxter, M. (2021, January 13\u201315). Federated Fine-Tuning Performance on Edge Devices. Proceedings of the 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), Online.","DOI":"10.1109\/ICMLA52953.2021.00191"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1093\/jamia\/ocx084","article-title":"The MIMIC Code Repository: Enabling reproducibility in critical care research","volume":"25","author":"Johnson","year":"2018","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1001\/jama.2016.0288","article-title":"Assessment of clinical criteria for sepsis: For the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)","volume":"315","author":"Seymour","year":"2016","journal-title":"JAMA"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1007\/BF01709751","article-title":"The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction\/failure","volume":"22","author":"Vincent","year":"1996","journal-title":"Intensive Care Med."},{"key":"ref_35","unstructured":"McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B.A. (2017, January 20\u201322). Communication-efficient learning of deep networks from decentralized data. Proceedings of the Artificial Intelligence and Statistics, PMLR, Fort Lauderdale, FL, USA."},{"key":"ref_36","unstructured":"Reddi, S., Charles, Z., Zaheer, M., Garrett, Z., Rush, K., Kone\u010dn\u1ef3, J., Kumar, S., and McMahan, H.B. (2020). Adaptive federated optimization. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_39","unstructured":"Yoon, J., Jordon, J., and Schaar, M. (2018, January 17\u201323). Gain: Missing data imputation using generative adversarial nets. Proceedings of the International Conference on Machine Learning, PMLR, Baltimore, MD, USA."},{"key":"ref_40","unstructured":"Huang, K., Altosaar, J., and Ranganath, R. (2019). ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Alsentzer, E., Murphy, J., Boag, W., Weng, W.H., Jin, D., Naumann, T., and McDermott, M. (2019, January 7\u201310). Publicly Available Clinical BERT Embeddings. Proceedings of the 2nd Clinical Natural Language Processing Workshop, Minneapolis, MN, USA.","DOI":"10.18653\/v1\/W19-1909"},{"key":"ref_42","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv."},{"key":"ref_43","first-page":"139","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_44","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Neumann, M., King, D., Beltagy, I., and Ammar, W. (2019). ScispaCy: Fast and robust models for biomedical natural language processing. arXiv.","DOI":"10.18653\/v1\/W19-5034"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Goerzen, J. (2004). Foundations of Python Network Programming, Apress.","DOI":"10.1007\/978-1-4302-0752-8"},{"key":"ref_47","unstructured":"Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A. (2017, January 4\u20139). Automatic differentiation in PyTorch. Proceedings of the NIPS-W 2017, Long Beach, CA, USA."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Hatzivasilis, G., Soultatos, O., Ioannidis, S., Verikoukis, C., Demetriou, G., and Tsatsoulis, C. (2019, January 29\u201331). Review of security and privacy for the Internet of Medical Things (IoMT). Proceedings of the 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), Santorini, Greece.","DOI":"10.1109\/DCOSS.2019.00091"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"9411","DOI":"10.1007\/s11042-020-10073-7","article-title":"Automatic speech recognition: A survey","volume":"80","author":"Malik","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Goel, A., Tung, C., Lu, Y.H., and Thiruvathukal, G.K. (2020, January 2\u201316). A survey of methods for low-power deep learning and computer vision. Proceedings of the 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), New Orleans, LA, USA.","DOI":"10.1109\/WF-IoT48130.2020.9221198"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.procs.2020.07.058","article-title":"Intelligent context-based healthcare metadata aggregator in internet of medical things platform","volume":"175","author":"Alam","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.comcom.2020.05.029","article-title":"Sensors for Internet of Medical Things: State-of-the-art, security and privacy issues, challenges and future directions","volume":"160","author":"Ray","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Alam, M.U., Baldvinsson, J.R., and Wang, Y. (2022, January 21\u201322). Exploring LRP and Grad-CAM visualization to interpret multi-label-multi-class pathology prediction using chest radiography. Proceedings of the 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS), Shenzhen, China.","DOI":"10.1109\/CBMS55023.2022.00052"},{"key":"ref_54","unstructured":"Goodman, L.R. (2014). Felson\u2019s Principles of Chest Roentgenology, a Programmed Text, Elsevier Health Sciences."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/970\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:06:03Z","timestamp":1760119563000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/970"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,14]]},"references-count":54,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23020970"],"URL":"https:\/\/doi.org\/10.3390\/s23020970","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,14]]}}}