A Risk Prediction Framework to Optimize Remote Patient Monitoring Following Cardiothoracic Surgery

Ricardo Santos; Bruno Ribeiro; Pedro Dias; Isabel Curioso; Pedro Madeira; Federico Guede-Fernández; Jorge Santos; Pedro Coelho; Inês Sousa; Ana Londral

doi:10.1007/978-3-031-43430-3_32

A Risk Prediction Framework to Optimize Remote Patient Monitoring Following Cardiothoracic Surgery

Ricardo Santos, Bruno Ribeiro, Pedro Dias, Isabel Curioso, Pedro Madeira, Federico Guede-Fernández, Jorge Santos, Pedro Coelho, Inês Sousa, Ana Londral

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Remote Patient Monitoring (RPM) in cardiac surgery can become valuable for clinicians to follow patients post-discharge closely. However, these services require additional and frequently limited human and technical resources. We present the CardioFollow.AI Framework, a decision support system to assist doctors in selecting patients to be monitored remotely. Currently supporting a clinical trial, it leverages a Machine Learning model to predict the risk of post-discharge complications. Interpretable assessments are included so that clinicians can evaluate individual predictions. Additionally, the user-friendly interface of the CardioFollow.AI Framework enhances the follow-up of discharged patients by granting access to centralised information. This paper outlines the design and implementation of the CardioFollow.AI Framework and its potential impact on improving personalised patient careq.

Original language	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track
Subtitle of host publication	European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part VII
Editors	Gianmarco De Francisci Morales, Claudia Perlich, Natali Ruchansky, Nicolas Kourtellis, Elena Baralis, Francesco Bonchi
Place of Publication	Cham
Publisher	Springer
Pages	366-371
Number of pages	6
ISBN (Electronic)	978-3-031-43430-3
ISBN (Print)	978-3-031-43429-7
DOIs	https://doi.org/10.1007/978-3-031-43430-3_32
Publication status	Published - Sept 2023
Event	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023 - Turin, Italy Duration: 18 Sept 2023 → 22 Sept 2023

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	14175 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023
Country/Territory	Italy
City	Turin
Period	18/09/23 → 22/09/23

Keywords

Cardiothoracic Surgery
Decision Support Systems
Machine Learning
Remote Patient Monitoring

Access to Document

10.1007/978-3-031-43430-3_32

Cite this

Santos, R., Ribeiro, B., Dias, P., Curioso, I., Madeira, P., Guede-Fernández, F., Santos, J., Coelho, P., Sousa, I., & Londral, A. (2023). A Risk Prediction Framework to Optimize Remote Patient Monitoring Following Cardiothoracic Surgery. In G. De Francisci Morales, C. Perlich, N. Ruchansky, N. Kourtellis, E. Baralis, & F. Bonchi (Eds.), Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track: European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part VII (pp. 366-371). (Lecture Notes in Computer Science; Vol. 14175 LNAI). Springer. https://doi.org/10.1007/978-3-031-43430-3_32

Santos, Ricardo ; Ribeiro, Bruno ; Dias, Pedro et al. / A Risk Prediction Framework to Optimize Remote Patient Monitoring Following Cardiothoracic Surgery. Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track: European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part VII. editor / Gianmarco De Francisci Morales ; Claudia Perlich ; Natali Ruchansky ; Nicolas Kourtellis ; Elena Baralis ; Francesco Bonchi. Cham : Springer, 2023. pp. 366-371 (Lecture Notes in Computer Science).

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abstract = "Remote Patient Monitoring (RPM) in cardiac surgery can become valuable for clinicians to follow patients post-discharge closely. However, these services require additional and frequently limited human and technical resources. We present the CardioFollow.AI Framework, a decision support system to assist doctors in selecting patients to be monitored remotely. Currently supporting a clinical trial, it leverages a Machine Learning model to predict the risk of post-discharge complications. Interpretable assessments are included so that clinicians can evaluate individual predictions. Additionally, the user-friendly interface of the CardioFollow.AI Framework enhances the follow-up of discharged patients by granting access to centralised information. This paper outlines the design and implementation of the CardioFollow.AI Framework and its potential impact on improving personalised patient careq.",

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note = "Funding Information: Acknowledgements. This work refers to the project “CardioFollow.AI: An intelligent system to improve patients{\textquoteright} safety and remote surveillance in follow-up for cardiothoracic surgery”, and is supported by {\textquoteleft}FCT - Portuguese Foundation for Science and Technology, I.P.{\textquoteright}, with the reference DSAIPA/AI/0094/2020. Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023 ; Conference date: 18-09-2023 Through 22-09-2023",

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Santos, R , Ribeiro, B , Dias, P , Curioso, I, Madeira, P, Guede-Fernández, F , Santos, J , Coelho, P, Sousa, I & Londral, A 2023, A Risk Prediction Framework to Optimize Remote Patient Monitoring Following Cardiothoracic Surgery. in G De Francisci Morales, C Perlich, N Ruchansky, N Kourtellis, E Baralis & F Bonchi (eds), Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track: European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part VII. Lecture Notes in Computer Science, vol. 14175 LNAI, Springer, Cham, pp. 366-371, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2023, Turin, Italy, 18/09/23. https://doi.org/10.1007/978-3-031-43430-3_32

A Risk Prediction Framework to Optimize Remote Patient Monitoring Following Cardiothoracic Surgery. / Santos, Ricardo ; Ribeiro, Bruno ; Dias, Pedro et al.
Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track: European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part VII. ed. / Gianmarco De Francisci Morales; Claudia Perlich; Natali Ruchansky; Nicolas Kourtellis; Elena Baralis; Francesco Bonchi. Cham: Springer, 2023. p. 366-371 (Lecture Notes in Computer Science; Vol. 14175 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Santos R , Ribeiro B , Dias P , Curioso I, Madeira P, Guede-Fernández F et al. A Risk Prediction Framework to Optimize Remote Patient Monitoring Following Cardiothoracic Surgery. In De Francisci Morales G, Perlich C, Ruchansky N, Kourtellis N, Baralis E, Bonchi F, editors, Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track: European Conference, ECML PKDD 2023, Turin, Italy, September 18–22, 2023, Proceedings, Part VII. Cham: Springer. 2023. p. 366-371. (Lecture Notes in Computer Science). doi: 10.1007/978-3-031-43430-3_32