Kuanr Madhusree, Mohapatra Puspanjali, Mittal Sanchi, Maindarkar Mahesh, Fauda Mostafa M, Saba Luca, Saxena Sanjay, Suri Jasjit S
Department of Computer Science and Engineering, IIIT, Bhubaneswar 751003, India.
Stroke Monitoring and Diagnostic Division, AtheroPointTM, Roseville, CA 95661, USA.
Diagnostics (Basel). 2022 Nov 5;12(11):2700. doi: 10.3390/diagnostics12112700.
: 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. : 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. : 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. : 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.
在疫情期间，医院在满足患者医疗需求方面面临重大问题，尤其是当患者数量迅速增加时，就像最近新冠疫情期间所看到的那样。本研究设计了一种治疗推荐系统（RS），用于医院中医生、药品和资源等人财物资源的高效管理。我们假设，将深度学习框架与图像框架中的搜索范式相结合，可以使推荐系统非常高效。本研究使用卷积神经网络（CNN）模型进行图像特征提取，并找出最相似的患者。输入查询来自医院数据库中具有相似胸部X光图像的患者。它使用一种相似性度量来进行图像的相似性计算。这种方法将与相似患者相关的医生、药品和资源推荐给即将入院的新冠患者。所提出的推荐系统的性能通过五种不同的特征提取CNN模型和四种相似性度量进行了验证。发现具有ResNet - 50 CNN特征提取模型和麦克斯韦 - 玻尔兹曼相似性的所提出的推荐系统是一个合适的治疗推荐框架，对于阈值相似性在0.7至0.9范围内，平均平均精度超过0.90，平均最高余弦相似性超过0.95。总体而言，具有CNN模型和图像相似性的推荐系统被证明是在疫情高峰期合理管理资源的有效工具，可在临床环境中采用。
