TY - JOUR
T1 - IPOscore
T2 - An interactive web-based platform for postoperative surgical complications analysis and prediction in the oncology domain
AU - Mochão, Hugo
AU - Gonçalves, Daniel
AU - Alexandre, Leonardo
AU - Castro, Carolina
AU - Valério, Duarte
AU - Barahona, Pedro
AU - Moreira-Gonçalves, Daniel
AU - Costa, Paulo Matos da
AU - Henriques, Rui
AU - Santos, Lúcio L.
AU - Costa, Rafael S.
N1 - info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50022%2F2020/PT#
info:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0042%2F2018/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50006%2F2020/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F50006%2F2020/PT#
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50021%2F2020/PT#
info:eu-repo/grantAgreement/FCT/OE/2021.07759.BD/PT#
CEECIND/01399/2017
PY - 2022/6
Y1 - 2022/6
N2 - Background: The performance of traditional risk score systems to predict (post)-operative outcomes is limited. This weakness reduces confidence in its use to support clinical risk mitigation decisions. However, the rapid growth of health data in the last years offers principles to deal with some of these limitations. In this regard, the data allows the extraction of relevant information for both patients stratification and the rigorous identification of associated risk factors. The patients can then be targeted to specific preoperative optimization programs, thus contributing to the reduction of associated morbidity and mortality. Objectives: The main goal of this work is, therefore, to provide a clinical decision support system (CDSS) based on data-driven modeling methods for surgical risk prediction specific for cancer patients in Portugal. Results: The result is IPOscore, a single web-based platform aimed at being an innovative approach to assist clinical decision-making in the surgical oncology domain. This system includes a database to store/manage the clinical data collected in a structured format, data visualization and analysis tools, and predictive machine learning models to predict postoperative outcomes in cancer patients. IPOscore also includes a pattern mining module based on biclustering to assess the discriminative power of a pattern towards postsurgical outcomes. Additionally, a mobile application is provided to this end. Conclusions: The IPOscore platform is a valuable tool for surgical oncologists not only for clinical data management but also as a preventative and predictive healthcare system. Currently, this clinical support tool is being tested at the Portuguese Institute of Oncology (IPO-Porto), and can be accessed online at https://iposcore.org.
AB - Background: The performance of traditional risk score systems to predict (post)-operative outcomes is limited. This weakness reduces confidence in its use to support clinical risk mitigation decisions. However, the rapid growth of health data in the last years offers principles to deal with some of these limitations. In this regard, the data allows the extraction of relevant information for both patients stratification and the rigorous identification of associated risk factors. The patients can then be targeted to specific preoperative optimization programs, thus contributing to the reduction of associated morbidity and mortality. Objectives: The main goal of this work is, therefore, to provide a clinical decision support system (CDSS) based on data-driven modeling methods for surgical risk prediction specific for cancer patients in Portugal. Results: The result is IPOscore, a single web-based platform aimed at being an innovative approach to assist clinical decision-making in the surgical oncology domain. This system includes a database to store/manage the clinical data collected in a structured format, data visualization and analysis tools, and predictive machine learning models to predict postoperative outcomes in cancer patients. IPOscore also includes a pattern mining module based on biclustering to assess the discriminative power of a pattern towards postsurgical outcomes. Additionally, a mobile application is provided to this end. Conclusions: The IPOscore platform is a valuable tool for surgical oncologists not only for clinical data management but also as a preventative and predictive healthcare system. Currently, this clinical support tool is being tested at the Portuguese Institute of Oncology (IPO-Porto), and can be accessed online at https://iposcore.org.
KW - Cancer
KW - Data management
KW - Data mining
KW - Decision support tool
KW - Intelligent systems engineering
KW - Machine learning
KW - Postsurgical risk stratification
KW - Web-based platform
UR - http://www.scopus.com/inward/record.url?scp=85127195307&partnerID=8YFLogxK
U2 - 10.1016/j.cmpb.2022.106754
DO - 10.1016/j.cmpb.2022.106754
M3 - Article
C2 - 35364482
AN - SCOPUS:85127195307
SN - 0169-2607
VL - 219
SP - 1
EP - 14
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
M1 - 106754
ER -