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International Journal of Colorectal Disease

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01-09-2015 | Original Article

Seasonal variation in onset and relapse of IBD and a model to predict the frequency of onset, relapse, and severity of IBD based on artificial neural network

Authors: Jiang Chen Peng, Zhi Hua Ran, Jun Shen

Published in: International Journal of Colorectal Disease | Issue 9/2015

Abstract

Background

Previous research has yielded conflicting data as to whether the natural history of inflammatory bowel disease follows a seasonal pattern. The purpose of this study was (1) to determine whether the frequency of onset and relapse of inflammatory bowel disease follows a seasonal pattern and (2) to establish a model to predict the frequency of onset, relapse, and severity of inflammatory bowel disease (IBD) with meteorological data based on artificial neural network (ANN).

Method

Patients with diagnosis of ulcerative colitis (UC) or Crohn’s disease (CD) between 2003 and 2011 were investigated according to the occurrence of onset and flares of symptoms. The expected onset or relapse was calculated on a monthly basis over the study period. For artificial neural network (ANN), patients from 2003 to 2010 were assigned as training cohort and patients in 2011 were assigned as validation cohort. Mean square error (MSE) and mean absolute percentage error (MAPE) were used to evaluate the predictive accuracy.

Results

We found no seasonal pattern of onset (P = 0.248) and relapse (P = 0.394) among UC patients. But, the onset (P = 0.015) and relapse (P = 0.004) of CD were associated with seasonal pattern, with a peak in July and August. ANN had average accuracy to predict the frequency of onset (MSE = 0.076, MAPE = 37.58 %) and severity of IBD (MSE = 0.065, MAPE = 42.15 %) but high accuracy in predicting the frequency of relapse of IBD (MSE = 0.009, MAPE = 17.1 %).

Conclusion

The frequency of onset and relapse in IBD showed seasonality only in CD, with a peak in July and August, but not in UC. ANN may have its value in predicting the frequency of relapse among patients with IBD.

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Title: Seasonal variation in onset and relapse of IBD and a model to predict the frequency of onset, relapse, and severity of IBD based on artificial neural network
Authors: Jiang Chen Peng
Zhi Hua Ran
Jun Shen
Publication date: 01-09-2015
Publisher: Springer Berlin Heidelberg
Published in: International Journal of Colorectal Disease / Issue 9/2015
Print ISSN: 0179-1958
Electronic ISSN: 1432-1262
DOI: https://doi.org/10.1007/s00384-015-2250-6

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Seasonal variation in onset and relapse of IBD and a model to predict the frequency of onset, relapse, and severity of IBD based on artificial neural network

Abstract

Background

Method

Results

Conclusion

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Background

Method

Results

Conclusion

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