Abstract
Patient follow-up appointments are an imperative part of the healthcare model to ensure safe patient recovery and proper course of treatment. The use of mobile devices can help patient monitoring and predictive approaches can provide computational support to identify deteriorating cases. Aiming to aggregate the data produced by those devices with the power of predictive approaches, this paper proposes the eWound-PRIOR framework to provide a remote assessment of postoperative orthopedic wounds. Our approach uses Artificial Intelligence (AI) techniques to process patients’ data related to postoperative wound healing and makes predictions as to whether the patient requires an in-person assessment or not. The experiment results showed that the predictions are promising and adherent to the application context, even if the on-line questionnaire had impaired the training model and the performance.
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ELAP from University of Western Ontario, Canada, Federal University of Juiz de Fora (UFJF), CAPES, CNPq and FAPEMIG.
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Neves, F., Jennings, M., Capretz, M., Bryant, D., Campos, F., Ströele, V. (2021). eWound-PRIOR: An Ensemble Framework for Cases Prioritization After Orthopedic Surgeries. In: Barolli, L., Takizawa, M., Yoshihisa, T., Amato, F., Ikeda, M. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2020. Lecture Notes in Networks and Systems, vol 158. Springer, Cham. https://doi.org/10.1007/978-3-030-61105-7_12
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