Survivability Prediction of Colorectal Cancer Patients: A System with Evolving Features for Continuous Improvement
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 t...
| প্রধান লেখক: | , , , , , |
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| বিন্যাস: | প্রবন্ধ |
| ভাষা: | English |
| প্রকাশিত: |
MDPI AG
2018-09-01
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| মালা: | Sensors |
| বিষয়গুলি: | |
| অনলাইন ব্যবহার করুন: | http://www.mdpi.com/1424-8220/18/9/2983 |
| _version_ | 1828150495816974336 |
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| author | Tiago Oliveira Ana Silva Ken Satoh Vicente Julian Pedro Leão Paulo Novais |
| author_facet | Tiago Oliveira Ana Silva Ken Satoh Vicente Julian Pedro Leão Paulo Novais |
| author_sort | Tiago Oliveira |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T21:45:22Z |
| format | Article |
| id | doaj.art-e3da010326cb4c51bc5189e574da59e1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T21:45:22Z |
| publishDate | 2018-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e3da010326cb4c51bc5189e574da59e12022-12-22T04:01:27ZengMDPI AGSensors1424-82202018-09-01189298310.3390/s18092983s18092983Survivability Prediction of Colorectal Cancer Patients: A System with Evolving Features for Continuous ImprovementTiago Oliveira0Ana Silva1Ken Satoh2Vicente Julian3Pedro Leão4Paulo Novais5National Institute of Informatics, Tokyo 100-0003, JapanAlgoritmi Centre/Department of Informatic, University of Minho, 4710-057 Braga, PortugalNational Institute of Informatics, Tokyo 100-0003, JapanDepartment of Systems and Computation, Universitat Politécnica de València, Valencia 46022, SpainICVS/3B’s, University of Minho, 4710-057 Braga, PortugalAlgoritmi Centre/Department of Informatic, University of Minho, 4710-057 Braga, PortugalPrediction 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.http://www.mdpi.com/1424-8220/18/9/2983survivability predictionclinical decision supportmachine learning |
| spellingShingle | Tiago Oliveira Ana Silva Ken Satoh Vicente Julian Pedro Leão Paulo Novais Survivability Prediction of Colorectal Cancer Patients: A System with Evolving Features for Continuous Improvement Sensors survivability prediction clinical decision support machine learning |
| title | Survivability Prediction of Colorectal Cancer Patients: A System with Evolving Features for Continuous Improvement |
| title_full | Survivability Prediction of Colorectal Cancer Patients: A System with Evolving Features for Continuous Improvement |
| title_fullStr | Survivability Prediction of Colorectal Cancer Patients: A System with Evolving Features for Continuous Improvement |
| title_full_unstemmed | Survivability Prediction of Colorectal Cancer Patients: A System with Evolving Features for Continuous Improvement |
| title_short | Survivability Prediction of Colorectal Cancer Patients: A System with Evolving Features for Continuous Improvement |
| title_sort | survivability prediction of colorectal cancer patients a system with evolving features for continuous improvement |
| topic | survivability prediction clinical decision support machine learning |
| url | http://www.mdpi.com/1424-8220/18/9/2983 |
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