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

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 2; p. 970
• Main Authors: Alam, Mahbub Ul, Rahmani, Rahim
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2023; MDPI
• Subjects: Artificial intelligence; Central processing units; Clinical decision making; clinical decision support system; Computer and Systems Sciences; CPUs; data- och systemvetenskap; Decision support systems; Deep Learning; early sepsis detection; Electronic Health Records; federated learning; Health facilities; Hospital costs; Humans; Internet; Internet of Medical Things; Machine Learning; Medical devices; Mortality; multi-modality; Natural language processing; Patients; Privacy; Sepsis; Sepsis - diagnosis; smart healthcare; Usability; clinical decision support system; deep learning; natural language processing; early sepsis detection; Internet of Medical Things; multi-modality; smart healthcare; electronic health records; federated learning
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23020970

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 , 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-recall curve, and the 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-recall curve is 96.55%, the area under the receiver operating characteristic curve is 99.35%, and is 4.56 h. 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.