A new recurrent neural network based predictive model for Faecal Calprotectin analysis: A retrospective study
This work addresses improving diagnostic accuracy for Inflammatory Bowel Disease patients, but it is incremental as it applies an existing neural network type to a specific medical dataset.
The authors tackled predicting Faecal Calprotectin levels for intestinal inflammation using a novel Echo State Network model, achieving statistically significant improvements over logistic regression in a retrospective study of 804 patients.
Faecal Calprotectin (FC) is a surrogate marker for intestinal inflammation, termed Inflammatory Bowel Disease (IBD), but not for cancer. In this retrospective study of 804 patients, an enhanced benchmark predictive model for analyzing FC is developed, based on a novel state-of-the-art Echo State Network (ESN), an advanced dynamic recurrent neural network which implements a biologically plausible architecture, and a supervised learning mechanism. The proposed machine learning driven predictive model is benchmarked against a conventional logistic regression model, demonstrating statistically significant performance improvements.