- -

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

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

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

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Oliveira, Tiago es_ES
dc.contributor.author Silva, Ana es_ES
dc.contributor.author Satoh, Ken es_ES
dc.contributor.author Julian Inglada, Vicente Javier es_ES
dc.contributor.author Leao, Pedro es_ES
dc.contributor.author Novais, Paulo es_ES
dc.date.accessioned 2020-06-12T03:32:50Z
dc.date.available 2020-06-12T03:32:50Z
dc.date.issued 2018-09 es_ES
dc.identifier.uri http://hdl.handle.net/10251/146155
dc.description.abstract [EN] 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. es_ES
dc.description.sponsorship The work of Tiago Oliveira was supported by JSPS KAKENHI Grant Number JP18K18115. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Sensors es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Survivability prediction es_ES
dc.subject Clinical decision support es_ES
dc.subject Machine learning es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.title Survivability Prediction of Colorectal Cancer Patients: A System with Evolving Features for Continuous Improvement es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/s18092983 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/JSPS//JP18K18115/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Oliveira, T.; Silva, A.; Satoh, K.; Julian Inglada, VJ.; Leao, P.; Novais, P. (2018). Survivability Prediction of Colorectal Cancer Patients: A System with Evolving Features for Continuous Improvement. Sensors. 18(9). https://doi.org/10.3390/s18092983 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/s18092983 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 18 es_ES
dc.description.issue 9 es_ES
dc.identifier.eissn 1424-8220 es_ES
dc.identifier.pmid 30200676 es_ES
dc.identifier.pmcid PMC6163414 es_ES
dc.relation.pasarela S\373997 es_ES
dc.contributor.funder Japan Society for the Promotion of Science es_ES
dc.description.references Engstrom, P. F., Arnoletti, J. P., Benson, A. B., Chen, Y.-J., Choti, M. A., Cooper, H. S., … Willett, C. (2009). Colon Cancer. Journal of the National Comprehensive Cancer Network, 7(8), 778-831. doi:10.6004/jnccn.2009.0056 es_ES
dc.description.references Taniyama, T. K., Hashimoto, K., Kastumata, N., Hirakawa, A., Yonemori, K., Yunokawa, M., … Fujiwara, Y. (2014). Can oncologists predict survival for patients with progressive disease after standard chemotherapies? Current Oncology, 21(2), 84. doi:10.3747/co.21.1743 es_ES
dc.description.references Kishore, J., Goel, M., & Khanna, P. (2010). Understanding survival analysis: Kaplan-Meier estimate. International Journal of Ayurveda Research, 1(4), 274. doi:10.4103/0974-7788.76794 es_ES
dc.description.references Silva, A., Oliveira, T., Novais, P., Neves, J., & Leão, P. (2016). Developing an Individualized Survival Prediction Model for Colon Cancer. Advances in Intelligent Systems and Computing, 87-95. doi:10.1007/978-3-319-40114-0_10 es_ES
dc.description.references Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29-36. doi:10.1148/radiology.143.1.7063747 es_ES
dc.description.references Valentini, V., van Stiphout, R. G. P. M., Lammering, G., Gambacorta, M. A., Barba, M. C., Bebenek, M., … Lambin, P. (2011). Nomograms for Predicting Local Recurrence, Distant Metastases, and Overall Survival for Patients With Locally Advanced Rectal Cancer on the Basis of European Randomized Clinical Trials. Journal of Clinical Oncology, 29(23), 3163-3172. doi:10.1200/jco.2010.33.1595 es_ES
dc.description.references Renfro, L. A., Grothey, A., Xue, Y., Saltz, L. B., André, T., Twelves, C., … Sargent, D. J. (2014). ACCENT-Based Web Calculators to Predict Recurrence and Overall Survival in Stage III Colon Cancer. JNCI: Journal of the National Cancer Institute, 106(12). doi:10.1093/jnci/dju333 es_ES
dc.description.references Weiser, M. R., Gönen, M., Chou, J. F., Kattan, M. W., & Schrag, D. (2011). Predicting Survival After Curative Colectomy for Cancer: Individualizing Colon Cancer Staging. Journal of Clinical Oncology, 29(36), 4796-4802. doi:10.1200/jco.2011.36.5080 es_ES
dc.description.references Chang, G. J., Hu, C.-Y., Eng, C., Skibber, J. M., & Rodriguez-Bigas, M. A. (2009). Practical Application of a Calculator for Conditional Survival in Colon Cancer. Journal of Clinical Oncology, 27(35), 5938-5943. doi:10.1200/jco.2009.23.1860 es_ES
dc.description.references Al-Bahrani, R., Agrawal, A., & Choudhary, A. (2013). Colon cancer survival prediction using ensemble data mining on SEER data. 2013 IEEE International Conference on Big Data. doi:10.1109/bigdata.2013.6691752 es_ES
dc.description.references Al-Bahrani, R., Agrawal, A., & Choudhary, A. (2017). Survivability prediction of colon cancer patients using neural networks. Health Informatics Journal, 25(3), 878-891. doi:10.1177/1460458217720395 es_ES
dc.description.references Wang, S. J., Wissel, A. R., Luh, J. Y., Fuller, C. D., Kalpathy-Cramer, J., & Thomas, C. R. (2011). An Interactive Tool for Individualized Estimation of Conditional Survival in Rectal Cancer. Annals of Surgical Oncology, 18(6), 1547-1552. doi:10.1245/s10434-010-1512-3 es_ES
dc.description.references Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015). Machine learning applications in cancer prognosis and prediction. Computational and Structural Biotechnology Journal, 13, 8-17. doi:10.1016/j.csbj.2014.11.005 es_ES
dc.description.references Jing Han, Haihong E, Guan Le, & Jian Du. (2011). Survey on NoSQL database. 2011 6th International Conference on Pervasive Computing and Applications. doi:10.1109/icpca.2011.6106531 es_ES
dc.description.references Zafar, R., Yafi, E., Zuhairi, M. F., & Dao, H. (2016). Big Data: The NoSQL and RDBMS review. 2016 International Conference on Information and Communication Technology (ICICTM). doi:10.1109/icictm.2016.7890788 es_ES
dc.description.references Chawla, N. V. (s. f.). Data Mining for Imbalanced Datasets: An Overview. Data Mining and Knowledge Discovery Handbook, 853-867. doi:10.1007/0-387-25465-x_40 es_ES
dc.description.references RapidMiner Documentation: Operator Reference Guidehttps://docs.rapidminer.com/latest/studio/operators/ es_ES
dc.description.references Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140. doi:10.1007/bf00058655 es_ES
dc.description.references Freund, Y., & Schapire, R. E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119-139. doi:10.1006/jcss.1997.1504 es_ES
dc.description.references Džeroski, S., & Ženko, B. (2004). Is Combining Classifiers with Stacking Better than Selecting the Best One? Machine Learning, 54(3), 255-273. doi:10.1023/b:mach.0000015881.36452.6e es_ES
dc.description.references Kittler, J. (1998). Combining classifiers: A theoretical framework. Pattern Analysis and Applications, 1(1), 18-27. doi:10.1007/bf01238023 es_ES
dc.description.references Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7), 1145-1159. doi:10.1016/s0031-3203(96)00142-2 es_ES
dc.description.references Papazoglou, M. P., & van den Heuvel, W.-J. (2007). Service oriented architectures: approaches, technologies and research issues. The VLDB Journal, 16(3), 389-415. doi:10.1007/s00778-007-0044-3 es_ES
dc.description.references Top 5 Considerations When Evaluating NoSQL Databaseshttps://www.ascent.tech/wp-content/uploads/documents/mongodb/10gen-top-5-nosql-considerations-february-2015.pdf es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem