Gründner J, Prokosch HU, Stürzl M, Croner RS, Christoph J, Toddenroth D (2018)
Publication Type: Journal article
Publication year: 2018
Book Volume: 247
Pages Range: 101-105
DOI: 10.3233/978-1-61499-852-5-101
Using gene markers and other patient features to predict clinical outcomes plays a vital role in enhancing clinical decision making and improving prognostic accuracy. This work uses a large set of colorectal cancer patient data to train predictive models using machine learning methods such as random forest, general linear model, and neural network for clinically relevant outcomes including disease free survival, survival, radio-chemotherapy response (RCT-R) and relapse. The most successful predictive models were created for dichotomous outcomes like relapse and RCT-R with accuracies of 0.71 and 0.70 on blinded test data respectively. The best prediction models regarding overall survival and disease-free survival had C-Index scores of 0.86 and 0.76 respectively. These models could be used in the future to aid a decision for or against chemotherapy and improve survival prognosis. We propose that future work should focus on creating reusable frameworks and infrastructure for training and delivering predictive models to physicians, so that they could be readily applied to other diseases in practice and be continuously developed integrating new data.
APA:
Gründner, J., Prokosch, H.-U., Stürzl, M., Croner, R.S., Christoph, J., & Toddenroth, D. (2018). Predicting Clinical Outcomes in Colorectal Cancer Using Machine Learning. Studies in health technology and informatics, 247, 101-105. https://doi.org/10.3233/978-1-61499-852-5-101
MLA:
Gründner, Julian, et al. "Predicting Clinical Outcomes in Colorectal Cancer Using Machine Learning." Studies in health technology and informatics 247 (2018): 101-105.
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