Abstract
The care for patients with chronic and progressive diseases often requires that reliable estimates of their remaining lifetime are made. The predominant method for obtaining such individual prognoses is to analyze historical data using Cox regression, and apply the resulting model to data from new patients. However, the black-box nature of the Cox regression model makes it unattractive for clinical practice. Instead most physicians prefer to relate a new patient to the histories of similar, individual patients that were treated before. This paper presents a prognostic inference method that combines the k-nearest neighbor paradigm with Cox regression. It yields survival predictions for individual patients, based on small sets of similar patients from the past, and can be used to implement a prognostic case-retrieval system. To evaluate the method, it was applied to data from patients with idiopathic interstitial pneumonia, a progressive and lethal lung disease. Experiments pointed out that the method competes well with Cox regression. The best predictive performance was obtained with a neighborhood size of 20.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kaplan, E., Meier, P.: Nonparametric estimation from incomplete observations. JASA 53, 457–481 (1958)
Cox, D.R.: Regression models and life tables. J Royal Stat Soc Series B 34, 187–220 (1972)
Akaike, H.: A new look at the statistical model identification. IEEE Trans Auto Control AC-19, 716–723 (1974)
Therneau, T., Grambsch, P.: Modeling Survival Data: Extending the Cox Model. Springer, New York (2000)
Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans Inform Theory 13, 21–27 (1967)
Schmidt, R., Montani, S., Bellazzi, R., Portinale, L., Gierl, L.: Cased-Based Reasoning for medical knowledge-based systems. Int J Med. Inform. 64, 355–367 (2001)
Bichindaritz, I., Marling, C.: Case-based reasoning in the health sciences: Whats next? Artif. Intell. Med. 36, 127–135 (2006)
Duda, R., Hart, P.: Pattern Classification and Scene Analysis. Wiley, New York (1973)
Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning. In: Data Mining, Inference, and Prediction, Springer, New York (2001)
American Thoracic Society/European Respiratory Society,: International Multidisciplinary Consensus Classification of the Idiopathic Interstitial Pneumonias. Am J Respir. Crit. Care Med. 165(2), 277–304 (2002)
Perez, A., Rogers, R.M., Dauber, J.H.: The prognosis of idiopathic pulmonary fibrosis. Am J Respir. Cell. Mol. Biol. 129(Suppl. 3), 19–26 (2003)
Brier, G.W.: Verification of forecasts expressed in terms of probability. Monthly Weather Rev. 78, 1–3 (1950)
Graf, E., Schmoor, C., Sauerbrei, W., Schumacher, M.: Assessment and comparison of prognostic classification schemes for survival data. Stat. Med. 18, 2529–2545 (1999)
Hanley, J.A., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1), 29–36 (1982)
Latsi, P.I., du Bois, R.M., Nicholson, A.G., et al.: Fibrotic idiopathic interstitial pneumonia: the prognostic value of longitudinal functional trends. Am J Respir Crit. Care Med. 168(5), 531–537 (2003)
Hamilton, P.W., Bartels, P.H., Anderson, N., Thompson, D., Montironi, R., Sloan, J.M.: Case-based prediction of survival in colorectal cancer patients. Anal Quant Cytol Histol 21(4), 283–291 (1999)
Anand, S.S., Hamilton, P.W., Hughes, J.G., Bell, D.A.: On prognostic models, artificial intelligence and censored observations. Meth Inf. Med 40(1), 18–24 (2001)
Aha, D., Daniels, J.J. (eds.): Case Based Reasoning Integrations: Papers from the 1998 Workshop (Technical Report, WS-98-15). AAAI Press, Menlo Park, CA (1998)
Collard, H.R., King Jr, T.E., Bartelson, B.B., et al.: Changes in clinical and physiologic variables predict survival in idiopathic pulmonary fibrosis. Am J Respir. Crit. Care Med. 168(5), 538–542 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Prijs, M., Peelen, L., Bresser, P., Peek, N. (2007). A Nearest Neighbor Approach to Predicting Survival Time with an Application in Chronic Respiratory Disease. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science(), vol 4594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73599-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-73599-1_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73598-4
Online ISBN: 978-3-540-73599-1
eBook Packages: Computer ScienceComputer Science (R0)