TY - JOUR
T1 - Predicting Cardiovascular Rehabilitation of Patients with Coronary Artery Disease Using Transfer Feature Learning
AU - Torres, Romina
AU - Zurita, Christopher
AU - Mellado, Diego
AU - Nicolis, Orietta
AU - Saavedra, Carolina
AU - Tuesta, Marcelo
AU - Salinas, Matías
AU - Bertini, Ayleen
AU - Pedemonte, Oneglio
AU - Querales, Marvin
AU - Salas, Rodrigo
N1 - Publisher Copyright:
© 2023 by the authors.
PY - 2023/2
Y1 - 2023/2
N2 - Cardiovascular diseases represent the leading cause of death worldwide. Thus, cardiovascular rehabilitation programs are crucial to mitigate the deaths caused by this condition each year, mainly in patients with coronary artery disease. COVID-19 was not only a challenge in this area but also an opportunity to open remote or hybrid versions of these programs, potentially reducing the number of patients who leave rehabilitation programs due to geographical/time barriers. This paper presents a method for building a cardiovascular rehabilitation prediction model using retrospective and prospective data with different features using stacked machine learning, transfer feature learning, and the joint distribution adaptation tool to address this problem. We illustrate the method over a Chilean rehabilitation center, where the prediction performance results obtained for 10-fold cross-validation achieved error levels with an NMSE of (Formula presented.) and an (Formula presented.) of (Formula presented.), where the best-achieved performance was an error level with a normalized mean squared error of 0.008 and an (Formula presented.) up to (Formula presented.). The results are encouraging for remote cardiovascular rehabilitation programs because these models could support the prioritization of remote patients needing more help to succeed in the current rehabilitation phase.
AB - Cardiovascular diseases represent the leading cause of death worldwide. Thus, cardiovascular rehabilitation programs are crucial to mitigate the deaths caused by this condition each year, mainly in patients with coronary artery disease. COVID-19 was not only a challenge in this area but also an opportunity to open remote or hybrid versions of these programs, potentially reducing the number of patients who leave rehabilitation programs due to geographical/time barriers. This paper presents a method for building a cardiovascular rehabilitation prediction model using retrospective and prospective data with different features using stacked machine learning, transfer feature learning, and the joint distribution adaptation tool to address this problem. We illustrate the method over a Chilean rehabilitation center, where the prediction performance results obtained for 10-fold cross-validation achieved error levels with an NMSE of (Formula presented.) and an (Formula presented.) of (Formula presented.), where the best-achieved performance was an error level with a normalized mean squared error of 0.008 and an (Formula presented.) up to (Formula presented.). The results are encouraging for remote cardiovascular rehabilitation programs because these models could support the prioritization of remote patients needing more help to succeed in the current rehabilitation phase.
KW - cardiovascular rehabilitation
KW - joint distribution adaptation
KW - machine learning
KW - transfer feature learning
UR - http://www.scopus.com/inward/record.url?scp=85147890050&partnerID=8YFLogxK
U2 - 10.3390/diagnostics13030508
DO - 10.3390/diagnostics13030508
M3 - Article
AN - SCOPUS:85147890050
SN - 2075-4418
VL - 13
JO - Diagnostics
JF - Diagnostics
IS - 3
M1 - 508
ER -