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Sensors 2018, 18(9), 2983;

Survivability Prediction of Colorectal Cancer Patients: A System with Evolving Features for Continuous Improvement

National Institute of Informatics, Tokyo 100-0003, Japan
Algoritmi Centre/Department of Informatic, University of Minho, 4710-057 Braga, Portugal
Department of Systems and Computation, Universitat Politécnica de València, Valencia 46022, Spain
ICVS/3B’s, University of Minho, 4710-057 Braga, Portugal
Author to whom correspondence should be addressed.
Received: 5 July 2018 / Revised: 15 August 2018 / Accepted: 4 September 2018 / Published: 6 September 2018
(This article belongs to the Special Issue Smart Decision-Making)
Full-Text   |   PDF [1155 KB, uploaded 17 September 2018]   |  


Prediction in health care is closely related with the decision-making process. On the one hand, accurate survivability prediction can help physicians decide between palliative care or other practice for a patient. On the other hand, the notion of remaining lifetime can be an incentive for patients to live a fuller and more fulfilling life. This work presents a pipeline for the development of survivability prediction models and a system that provides survivability predictions for years one to five after the treatment of patients with colon or rectal cancer. The functionalities of the system are made available through a tool that balances the number of necessary inputs and prediction performance. It is mobile-friendly and facilitates the access of health care professionals to an instrument capable of enriching their practice and improving outcomes. The performance of survivability models was compared with other existing works in the literature and found to be an improvement over the current state of the art. The underlying system is capable of recalculating its prediction models upon the addition of new data, continuously evolving as time passes. View Full-Text
Keywords: survivability prediction; clinical decision support; machine learning survivability prediction; clinical decision support; machine learning

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Oliveira, T.; Silva, A.; Satoh, K.; Julian, V.; Leão, P.; Novais, P. Survivability Prediction of Colorectal Cancer Patients: A System with Evolving Features for Continuous Improvement. Sensors 2018, 18, 2983.

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