2024
Chaves, A.; Sousa, R.; Machado, J.; Abelha, A.; Peixoto, H.
Collaborative Platform for Intelligent Monitoring of Diabetic Foot Patients - Colab4IMDF Proceedings Article
Em: A., Ferras C. Diez J. H. Rocha (Ed.): pp. 195-204, Springer Science and Business Media Deutschland GmbH, 2024, ISSN: 23673370, (cited By 0; Conference of International Conference on Information Technology and Systems, ICITS 2024 ; Conference Date: 24 January 2024 Through 26 January 2024; Conference Code:308799).
Resumo | Links | BibTeX | Etiquetas: Clinical research; Decision support systems; Deep learning; mHealth; Patient treatment, Collaborative platform; Diabetic foot; Diabetic foot ulcer; Diabetic foot wound follow-up; Diabetics patients; Follow up; Health information systems; Healthcare standards; Microservice; Multi agent, Multi agent systems
@inproceedings{Chaves2024195,
title = {Collaborative Platform for Intelligent Monitoring of Diabetic Foot Patients - Colab4IMDF},
author = {A. Chaves and R. Sousa and J. Machado and A. Abelha and H. Peixoto},
editor = {Ferras C. Diez J.H. Rocha A.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187779099&doi=10.1007%2f978-3-031-54256-5_18&partnerID=40&md5=7cc7dae3a967fb508b0024598e23f8e6},
doi = {10.1007/978-3-031-54256-5_18},
issn = {23673370},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {Lecture Notes in Networks and Systems},
volume = {933 LNNS},
pages = {195-204},
publisher = {Springer Science and Business Media Deutschland GmbH},
abstract = {This research work aims to set the basis for the development of a multidisciplinary platform designed to assist healthcare professionals, their institutions and more important, diabetic patients to manage diabetic foot syndrome complications. The system’s primary function is the automated classification of diabetic foot ulcers (DFU), with the ultimate goal of expediting treatment and improving patient well-being. Given the sporadic nature of diabetic patients’ medical consultations, the time gaps between appointments can exacerbate symptoms. To address this issue, the project advocates for sustained communication between physicians and patients, facilitated by the exchange of patient images, thereby reducing the need for frequent in-person visits. This manuscript shows project’s initial phase that involves the research and modelling of an architecture that ranges from mHealth to Deep Learning algorithm capable of rapidly and accurately classifying user-submitted images, offering clinical decision support and autonomous identification of potential DFU complications from the patient’s perspective. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.},
note = {cited By 0; Conference of International Conference on Information Technology and Systems, ICITS 2024 ; Conference Date: 24 January 2024 Through 26 January 2024; Conference Code:308799},
keywords = {Clinical research; Decision support systems; Deep learning; mHealth; Patient treatment, Collaborative platform; Diabetic foot; Diabetic foot ulcer; Diabetic foot wound follow-up; Diabetics patients; Follow up; Health information systems; Healthcare standards; Microservice; Multi agent, Multi agent systems},
pubstate = {published},
tppubtype = {inproceedings}
}
This research work aims to set the basis for the development of a multidisciplinary platform designed to assist healthcare professionals, their institutions and more important, diabetic patients to manage diabetic foot syndrome complications. The system’s primary function is the automated classification of diabetic foot ulcers (DFU), with the ultimate goal of expediting treatment and improving patient well-being. Given the sporadic nature of diabetic patients’ medical consultations, the time gaps between appointments can exacerbate symptoms. To address this issue, the project advocates for sustained communication between physicians and patients, facilitated by the exchange of patient images, thereby reducing the need for frequent in-person visits. This manuscript shows project’s initial phase that involves the research and modelling of an architecture that ranges from mHealth to Deep Learning algorithm capable of rapidly and accurately classifying user-submitted images, offering clinical decision support and autonomous identification of potential DFU complications from the patient’s perspective. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.