A Content-Boosted Collaborative Filtering Algorithm for Personalized Training in Interpretation of Radiological Imaging

Abstract

Devising a method that can select cases based on the performance levels of trainees and the characteristics of cases is essential for developing a personalized training program in radiology education. In this paper, we propose a novel hybrid prediction algorithm called content-boosted collaborative filtering (CBCF) to predict the difficulty level of each case for each trainee. The CBCF utilizes a content-based filtering (CBF) method to enhance existing trainee-case ratings data and then provides final predictions through a collaborative filtering (CF) algorithm. The CBCF algorithm incorporates the advantages of both CBF and CF, while not inheriting the disadvantages of either. The CBCF method is compared with the pure CBF and pure CF approaches using three datasets. The experimental data are then evaluated in terms of the MAE metric. Our experimental results show that the CBCF outperforms the pure CBF and CF methods by 13.33 and 12.17 %, respectively, in terms of prediction precision. This also suggests that the CBCF can be used in the development of personalized training systems in radiology education.

A Biased Proportional-Integral-Derivative-Incorporated Latent Factor Analysis Model

“…Compared with the traditional SVD algorithm, the performance of the biased SVD algorithm is better, because it takes individuation into account. As mentioned above, the prediction score formula of the biased SVD model is shown in (7) where b u and b i indicate the user bias and item bias, representing the eigenvalues of users and items, respectively, and µ expresses the average score of all users.…”

“…The calculation formula of the predicted value is the same as that of the biased SVD model (7). Then the instantaneous error value is brought into the PID controller to gain the adjusted error for iteration to obtain the final predicted value.…”

“…In other words, the user provides little information for reference by the recommendation system, but the number of items that are needed to predict the scores is quite large. In previous studies, scholars have proposed many solutions to these problems, such as singular value decomposition (SVD) [5], principal component analysis (PCA) [6], contentboosted CF algorithm (CBCF) [7], tree augmented naïve bayes optimized by extended logistic regression (TAN-ELR) [8]. All of them have their pros and cons.…”

Development of a Personalized Training System Using the Lung Image Database Consortium and Image Database Resource Initiative Database

“…To deal with the problem of a cold start, we use a hybrid prediction algorithm, named CBCF, to assess the difficulty level of each case for each trainee (26). The CBCF algorithm incorporates the advantages of both CBF and CF, while eliminating the disadvantages of both.…”

“…Currently, personalized radiology education in the interpretation of radiologic imaging has been gaining increased attention from researchers around the world. Researchers are exploring approaches for the development of personalized radiology education systems for the interpretation of radiographic imaging and, fortunately, have made some progress in this area (24)(25)(26). The user model approach, collaborative filtering (CF) method, and content-boosted CF algorithm (CBCF) were proposed to predict the difficulty levels of unseen cases for a given trainee to be used in personalized radiology education.…”