The need for multimodal health data modeling: A practical approach for a federated-learning healthcare platform

Cremonesi, Francesco; Planat, Vincent; Kalokyri, Varvara; Kondylakis, Haridimos; Sanavia, Tiziana; Resinas, Victor Miguel Mateos; Singh, Babita; Uribe, Silvia

doi:10.1016/j.jbi.2023.104338

Cited by 18 publications

(4 citation statements)

References 28 publications

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“… 10 The OMOP format was considered as an alternative to the FHIR format, yet we found that OMOP mappings did not exhibit the same level of granularity as the FHIR specification. 12 , 31 , 32 For the same reason, we ruled against using the HDF5 and NIFTI formats to represent image data, noting that these formats can be trivially obtained from DICOMs, while the reverse is not true.…”

Section: Methodsmentioning

confidence: 99%

CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data

Mullie,

Afilalo,

Archambault

et al. 2023

Journal of the American Medical Informatics Association

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Objectives Distributed computations facilitate multi-institutional data analysis while avoiding the costs and complexity of data pooling. Existing approaches lack crucial features, such as built-in medical standards and terminologies, no-code data visualizations, explicit disclosure control mechanisms, and support for basic statistical computations, in addition to gradient-based optimization capabilities. Materials and methods We describe the development of the Collaborative Data Analysis (CODA) platform, and the design choices undertaken to address the key needs identified during our survey of stakeholders. We use a public dataset (MIMIC-IV) to demonstrate end-to-end multi-modal FL using CODA. We assessed the technical feasibility of deploying the CODA platform at 9 hospitals in Canada, describe implementation challenges, and evaluate its scalability on large patient populations. Results The CODA platform was designed, developed, and deployed between January 2020 and January 2023. Software code, documentation, and technical documents were released under an open-source license. Multi-modal federated averaging is illustrated using the MIMIC-IV and MIMIC-CXR datasets. To date, 8 out of the 9 participating sites have successfully deployed the platform, with a total enrolment of >1M patients. Mapping data from legacy systems to FHIR was the biggest barrier to implementation. Discussion and conclusion The CODA platform was developed and successfully deployed in a public healthcare setting in Canada, with heterogeneous information technology systems and capabilities. Ongoing efforts will use the platform to develop and prospectively validate models for risk assessment, proactive monitoring, and resource usage. Further work will also make tools available to facilitate migration from legacy formats to FHIR and DICOM.

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Section: Methodsmentioning

confidence: 99%

CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data

Mullie,

Afilalo,

Archambault

et al. 2023

Journal of the American Medical Informatics Association

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“…IoT, WSNs, and FL are interconnected technologies that have gained significant attention in recent years with applications in healthcare, smart cities, agriculture, industrial automation, and environmental monitoring. This section provides a comprehensive overview of their applications in these domains, discussing benefits and challenges [6,40,47,94].…”

Section: Applications Of Integrated Iot Wsns and Flmentioning

confidence: 99%

A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning

Mengistu,

Kim,

Lin

2024

Sensors

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Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play a crucial role in IoT systems by collecting data from the physical environment. This paper presents a comprehensive survey of the integration of FL, IoT, and WSNs. It covers FL basics, strategies, and types and discusses the integration of FL, IoT, and WSNs in various domains. The paper addresses challenges related to heterogeneity in FL and summarizes state-of-the-art research in this area. It also explores security and privacy considerations and performance evaluation methodologies. The paper outlines the latest achievements and potential research directions in FL, IoT, and WSNs and emphasizes the significance of the surveyed topics within the context of current technological advancements.

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“…To assist physicians, complex systems are often created that cover diagnosis, risk assessment, treatment, and other tasks (Cardio-S, ASMO-CARDIO, Infective Endocarditis, Cardiologs, Cardiac Care Assistant, escardio.org, HEART Pathway, Cardiovascular Disease Management Tool, FFRCT HeartFlow, Cardio-ECO, Cardio-ANTIB). These approaches provide opportunities for developing more complex and scalable CDSS, adaptable to diverse clinical needs [39,13]. Another type of CDSS are systems that do not directly use knowledge from guidelines and clinical recommendations but apply machine learning models to support decision-making.…”

Section: Current State Of Affairsmentioning

confidence: 99%

Hybrid clinical decision support for cardiology: architectural foundations for integrations

Shakhgeldyan,

Gribova,

Geltser

et al. 2024

Fifth International Conference on Image, Video Processing, and Artificial Intelligence (IVPAI 2023)

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Cardiovascular diseases (CVD) are the leading cause of death in most countries around the world, making the accurate assessment of risks and the selection of individual preventive strategies a current focus in healthcare. In this article, the authors presented a prototype of a Clinical Decision Support System (CDSS) for predicting and preventing cardiovascular risks based on a hybrid architecture that integrates machine learning models and ontological knowledge bases. A microservice architecture based on the Cloud-Edge approach is proposed for optimizing computational resources when processing tabular data, signals, video, and images, as well as for enhancing the effectiveness of integration with various Healthcare Information Systems (HIS). The CDSS supports the formalization not only of medical history data and results of studies but also the rules for interpreting the results of predictions based on machine learning models and methods of explainable artificial intelligence (XAI). The developed CDSS includes widely used tools in clinical cardiology and cardiothoracic surgery for risk assessment, as well as proprietary machine learning models for predicting in-hospital mortality, and others. These models contribute to making informed medical decisions for the diagnosis, prevention, and treatment of CVD. The prototype was implemented at the Medical Center of the Far Eastern Federal University and integrated with the "1C" HIS. The experience of implementing the prototype demonstrated the high potential of hybrid CDSS based on microservice architecture for use in clinical practice.

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The need for multimodal health data modeling: A practical approach for a federated-learning healthcare platform

Cited by 18 publications

References 28 publications

CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data

CODA: an open-source platform for federated analysis and machine learning on distributed healthcare data

A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning

Hybrid clinical decision support for cardiology: architectural foundations for integrations

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