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• W4308444826 abstract "Background: Hospitals face a significant problem meeting patients' medical needs during epidemics, especially when the number of patients increases rapidly, as seen during the recent COVID-19 pandemic. This study designs a treatment recommender system (RS) for the efficient management of human capital and resources such as doctors, medicines, and resources in hospitals. We hypothesize that a deep learning framework, when combined with search paradigms in an image framework, can make the RS very efficient. Methodology: This study uses a Convolutional neural network (CNN) model for the feature extraction of the images and discovers the most similar patients. The input queries patients from the hospital database with similar chest X-ray images. It uses a similarity metric for the similarity computation of the images. Results: This methodology recommends the doctors, medicines, and resources associated with similar patients to a COVID-19 patients being admitted to the hospital. The performance of the proposed RS is verified with five different feature extraction CNN models and four similarity measures. The proposed RS with a ResNet-50 CNN feature extraction model and Maxwell-Boltzmann similarity is found to be a proper framework for treatment recommendation with a mean average precision of more than 0.90 for threshold similarities in the range of 0.7 to 0.9 and an average highest cosine similarity of more than 0.95. Conclusions: Overall, an RS with a CNN model and image similarity is proven as an efficient tool for the proper management of resources during the peak period of pandemics and can be adopted in clinical settings." @default.
• W4308444826 created "2022-11-11" @default.
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• W4308444826 date "2022-11-05" @default.
• W4308444826 modified "2023-10-05" @default.
• W4308444826 title "Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity" @default.
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• W4308444826 doi "https://doi.org/10.3390/diagnostics12112700" @default.
• W4308444826 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36359545" @default.
• W4308444826 hasPublicationYear "2022" @default.
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