4.7 Article

IPOscore: An interactive web-based platform for postoperative surgical complications analysis and prediction in the oncology domain

Journal

Publisher

ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2022.106754

Keywords

Web-based platform; Decision support tool; Intelligent systems engineering; Postsurgical risk stratification; Cancer; Data management; Data mining; Machine learning

Funding

  1. Fundacao para a Ciencia e a Tecnologia (FCT) , through IDMEC, under LAETA project [UIDB/50022/2020, DSAIPA/DS/0042/2018]
  2. Associate Laboratory for Green Chemistry (LAQV) [UIDB/50006/2020, UIDP/50006/2020]
  3. national funds from FCT/MCTES [UIDB/50021/2020, 2021.07759, CEECIND/01399/2017]
  4. INESC-ID plurianual
  5. Fundação para a Ciência e a Tecnologia [DSAIPA/DS/0042/2018] Funding Source: FCT

Ask authors/readers for more resources

The study aims to provide a clinical decision support system for cancer patients in Portugal based on data-driven modeling methods. The result is IPOscore, an innovative platform for surgical oncology that includes a database, data visualization and analysis tools, and predictive machine learning models.
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 . (c) 2022 Elsevier B.V. All rights reserved.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available